Merge pull request 'clay: foundation claims — community formation + selfplex (6 claims)' (#64 ) from clay/foundation-cultural-dynamics into main

clay: address PR #64 review — backfire effect, Putnam causality, source archives
- Fix: soften backfire effect language in IPC claim — distinguish Kahan's robust finding (polarization increases with cognitive skill) from the contested backfire effect (Wood & Porter 2019, Guess & Coppock 2020 show minimal evidence) - Fix: qualify Putnam's TV causal claim as regression decomposition with contested causal interpretation - Add: cross-domain wiki links — Olson→alignment tax + voluntary pledges, IPC→AI alignment coordination + voluntary pledges - Add: 6 source archive stubs for canonical academic texts (Olson, Granovetter, Dunbar, Blackmore, Putnam, Kahan) Pentagon-Agent: Clay <D5A56E53-93FA-428D-8EC5-5BAC46E1B8C2>
2026-03-10 15:40:54 +00:00 · 2026-03-10 15:40:45 +00:00 · 2026-03-10 15:17:29 +00:00 · 2026-03-10 15:13:27 +00:00 · 2026-03-10 15:09:26 +00:00 · 2026-03-10 14:55:19 +00:00
46 changed files with 3130 additions and 450 deletions
--- a/.github/workflows/sync-graph-data.yml
+++ b/.github/workflows/sync-graph-data.yml
@ -0,0 +1,67 @@
 name: Sync Graph Data to teleo-app
 # Runs on every merge to main. Extracts graph data from the codex and
 # pushes graph-data.json + claims-context.json to teleo-app/public/.
 # This triggers a Vercel rebuild automatically.
 on:
  push:
    branches: [main]
    paths:
      - 'core/**'
      - 'domains/**'
      - 'foundations/**'
      - 'convictions/**'
      - 'ops/extract-graph-data.py'
  workflow_dispatch:  # manual trigger
 jobs:
  sync:
    runs-on: ubuntu-latest
    permissions:
      contents: read
    steps:
      - name: Checkout teleo-codex
        uses: actions/checkout@v4
        with:
          fetch-depth: 0  # full history for git log agent attribution
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.12'
      - name: Run extraction
        run: |
          python3 ops/extract-graph-data.py \
            --repo . \
            --output /tmp/graph-data.json \
            --context-output /tmp/claims-context.json
      - name: Checkout teleo-app
        uses: actions/checkout@v4
        with:
          repository: living-ip/teleo-app
          token: ${{ secrets.TELEO_APP_TOKEN }}
          path: teleo-app
      - name: Copy data files
        run: |
          cp /tmp/graph-data.json teleo-app/public/graph-data.json
          cp /tmp/claims-context.json teleo-app/public/claims-context.json
      - name: Commit and push to teleo-app
        working-directory: teleo-app
        run: |
          git config user.name "teleo-codex-bot"
          git config user.email "bot@livingip.io"
          git add public/graph-data.json public/claims-context.json
          if git diff --cached --quiet; then
            echo "No changes to commit"
          else
            NODES=$(python3 -c "import json; d=json.load(open('public/graph-data.json')); print(len(d['nodes']))")
            EDGES=$(python3 -c "import json; d=json.load(open('public/graph-data.json')); print(len(d['edges']))")
            git commit -m "sync: graph data from teleo-codex ($NODES nodes, $EDGES edges)"
            git push
          fi
--- a/CLAUDE.md
+++ b/CLAUDE.md
@ -1,6 +1,82 @@
-# Teleo Codex — Agent Operating Manual
+# Teleo Codex
-> **Exploring this repo?** Start with [README.md](README.md). Pick a domain, read a claim, follow the links. This file is for agents contributing to the knowledge base.
+## For Visitors (read this first)
 If you're exploring this repo with Claude Code, you're talking to a **collective knowledge base** maintained by 6 AI domain specialists. ~400 claims across 14 knowledge areas, all linked, all traceable from evidence through claims through beliefs to public positions.
 ### Orientation (run this on first visit)
 Don't present a menu. Start a short conversation to figure out who this person is and what they care about.
 **Step 1 — Ask what they work on or think about.** One question, open-ended. "What are you working on, or what's on your mind?" Their answer tells you which domain is closest.
 **Step 2 — Map them to an agent.** Based on their answer, pick the best-fit agent:
 | If they mention... | Route to |
 |-------------------|----------|
 | Finance, crypto, DeFi, DAOs, prediction markets, tokens | **Rio** — internet finance / mechanism design |
 | Media, entertainment, creators, IP, culture, storytelling | **Clay** — entertainment / cultural dynamics |
 | AI, alignment, safety, superintelligence, coordination | **Theseus** — AI / alignment / collective intelligence |
 | Health, medicine, biotech, longevity, wellbeing | **Vida** — health / human flourishing |
 | Space, rockets, orbital, lunar, satellites | **Astra** — space development |
 | Strategy, systems thinking, cross-domain, civilization | **Leo** — grand strategy / cross-domain synthesis |
 Tell them who you're loading and why: "Based on what you described, I'm going to think from [Agent]'s perspective — they specialize in [domain]. Let me load their worldview." Then load the agent (see instructions below).
 **Step 3 — Surface something interesting.** Once loaded, search that agent's domain claims and find 3-5 that are most relevant to what the visitor said. Pick for surprise value — claims they're likely to find unexpected or that challenge common assumptions in their area. Present them briefly: title + one-sentence description + confidence level.
 Then ask: "Any of these surprise you, or seem wrong?"
 This gets them into conversation immediately. If they push back on a claim, you're in challenge mode. If they want to go deeper on one, you're in explore mode. If they share something you don't know, you're in teach mode. The orientation flows naturally into engagement.
 **If they already know what they want:** Some visitors will skip orientation — they'll name an agent directly ("I want to talk to Rio") or ask a specific question. That's fine. Load the agent or answer the question. Orientation is for people who are exploring, not people who already know.
 ### What visitors can do
 1. **Explore** — Ask what the collective (or a specific agent) thinks about any topic. Search the claims and give the grounded answer, with confidence levels and evidence.
 2. **Challenge** — Disagree with a claim? Steelman the existing claim, then work through it together. If the counter-evidence changes your understanding, say so explicitly — that's the contribution. The conversation is valuable even if they never file a PR. Only after the conversation has landed, offer to draft a formal challenge for the knowledge base if they want it permanent.
 3. **Teach** — They share something new. If it's genuinely novel, draft a claim and show it to them: "Here's how I'd write this up — does this capture it?" They review, edit, approve. Then handle the PR. Their attribution stays on everything.
 4. **Propose** — They have their own thesis with evidence. Check it against existing claims, help sharpen it, draft it for their approval, and offer to submit via PR. See CONTRIBUTING.md for the manual path.
 ### How to behave as a visitor's agent
 When the visitor picks an agent lens, load that agent's full context:
 1. Read `agents/{name}/identity.md` — adopt their personality and voice
 2. Read `agents/{name}/beliefs.md` — these are your active beliefs, cite them
 3. Read `agents/{name}/reasoning.md` — this is how you evaluate new information
 4. Read `agents/{name}/skills.md` — these are your analytical capabilities
 5. Read `core/collective-agent-core.md` — this is your shared DNA
 **You are that agent for the duration of the conversation.** Think from their perspective. Use their reasoning framework. Reference their beliefs. When asked about another domain, acknowledge the boundary and cite what that domain's claims say — but filter it through your agent's worldview.
 **When the visitor teaches you something new:**
 - Search the knowledge base for existing claims on the topic
 - If the information is genuinely novel (not a duplicate, specific enough to disagree with, backed by evidence), say so
 - **Draft the claim for them** — write the full claim (title, frontmatter, body, wiki links) and show it to them in the conversation. Say: "Here's how I'd write this up as a claim. Does this capture what you mean?"
 - **Wait for their approval before submitting.** They may want to edit the wording, sharpen the argument, or adjust the scope. The visitor owns the claim — you're drafting, not deciding.
 - Once they approve, use the `/contribute` skill or follow the proposer workflow to create the claim file and PR
 - Always attribute the visitor as the source: `source: "visitor-name, original analysis"` or `source: "visitor-name via [article/paper title]"`
 **When the visitor challenges a claim:**
 - First, steelman the existing claim — explain the best case for it
 - Then engage seriously with the counter-evidence. This is a real conversation, not a form to fill out.
 - If the challenge changes your understanding, say so explicitly. Update how you reason about the topic in the conversation. The visitor should feel that talking to you was worth something even if they never touch git.
 - Only after the conversation has landed, ask if they want to make it permanent: "This changed how I think about [X]. Want me to draft a formal challenge for the knowledge base?" If they say no, that's fine — the conversation was the contribution.
 **Start here if you want to browse:**
 - `maps/overview.md` — how the knowledge base is organized
 - `core/epistemology.md` — how knowledge is structured (evidence → claims → beliefs → positions)
 - Any `domains/{domain}/_map.md` — topic map for a specific domain
 - Any `agents/{name}/beliefs.md` — what a specific agent believes and why
 ---
 ## Agent Operating Manual
 *Everything below is operational protocol for the 6 named agents. If you're a visitor, you don't need to read further — the section above is for you.*
 You are an agent in the Teleo collective — a group of AI domain specialists that build and maintain a shared knowledge base. This file tells you how the system works and what the rules are.
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@ -1,45 +1,51 @@
 # Contributing to Teleo Codex
-You're contributing to a living knowledge base maintained by AI agents. Your job is to bring in source material. The agents extract claims, connect them to existing knowledge, and review everything before it merges.
+You're contributing to a living knowledge base maintained by AI agents. There are three ways to contribute — pick the one that fits what you have.
 ## Three contribution paths
 ### Path 1: Submit source material
 You have an article, paper, report, or thread the agents should read. The agents extract claims — you get attribution.
 ### Path 2: Propose a claim directly
 You have your own thesis backed by evidence. You write the claim yourself.
 ### Path 3: Challenge an existing claim
 You think something in the knowledge base is wrong or missing nuance. You file a challenge with counter-evidence.
 ---
 ## What you need
- GitHub account with collaborator access to this repo
+- Git access to this repo (GitHub or Forgejo)
 - Git installed on your machine
- A source to contribute (article, report, paper, thread, etc.)
+- Claude Code (optional but recommended — it helps format claims and check for duplicates)
-## Step-by-step
+## Path 1: Submit source material
-### 1. Clone the repo (first time only)
+This is the simplest contribution. You provide content; the agents do the extraction.
 ### 1. Clone and branch
 ```bash
 git clone https://github.com/living-ip/teleo-codex.git
 cd teleo-codex
-```
+git checkout main && git pull
 ### 2. Pull latest and create a branch
 ```bash
 git checkout main
 git pull origin main
 git checkout -b contrib/your-name/brief-description
 ```
-Example: `contrib/alex/ai-alignment-report`
+### 2. Create a source file
-### 3. Create a source file
+Create a markdown file in `inbox/archive/`:
 Create a markdown file in `inbox/archive/` with this naming convention:
 ```
 inbox/archive/YYYY-MM-DD-author-handle-brief-slug.md
 ```
-Example: `inbox/archive/2026-03-07-alex-ai-alignment-landscape.md`
+### 3. Add frontmatter + content
 ### 4. Add frontmatter
 Every source file starts with YAML frontmatter. Copy this template and fill it in:
 ```yaml
 ---
@ -53,84 +59,169 @@ format: report
 status: unprocessed
 tags: [topic1, topic2, topic3]
 ---
 # Full title
 [Paste the full content here. More content = better extraction.]
 ```
-**Domain options:** `internet-finance`, `entertainment`, `ai-alignment`, `health`, `grand-strategy`
+**Domain options:** `internet-finance`, `entertainment`, `ai-alignment`, `health`, `space-development`, `grand-strategy`
 **Format options:** `essay`, `newsletter`, `tweet`, `thread`, `whitepaper`, `paper`, `report`, `news`
-**Status:** Always set to `unprocessed` — the agents handle the rest.
+### 4. Commit, push, open PR
 ### 5. Add the content
 After the frontmatter, paste the full content of the source. This is what the agents will read and extract claims from. More content = better extraction.
 ```markdown
 ---
 type: source
 title: "AI Alignment in 2026: Where We Stand"
 author: "Alex (@alexhandle)"
 url: https://example.com/report
 date: 2026-03-07
 domain: ai-alignment
 format: report
 status: unprocessed
 tags: [ai-alignment, openai, anthropic, safety, governance]
 ---
 # AI Alignment in 2026: Where We Stand
 [Full content of the report goes here. Include everything —
 the agents need the complete text to extract claims properly.]
 ```
 ### 6. Commit and push
 ```bash
 git add inbox/archive/your-file.md
-git commit -m "contrib: add AI alignment landscape report
+git commit -m "contrib: add [brief description]
 Source: [brief description of what this is and why it matters]"
 Source: [what this is and why it matters]"
 git push -u origin contrib/your-name/brief-description
 ```
-### 7. Open a PR
+Then open a PR. The domain agent reads your source, extracts claims, Leo reviews, and they merge.
-```bash
+## Path 2: Propose a claim directly
 gh pr create --title "contrib: AI alignment landscape report" --body "Source material for agent extraction.
- **What:** [one-line description]
+You have domain expertise and want to state a thesis yourself — not just drop source material for agents to process.
- **Domain:** ai-alignment
+
- **Why it matters:** [why this adds value to the knowledge base]"
+### 1. Clone and branch
 Same as Path 1.
 ### 2. Check for duplicates
 Before writing, search the knowledge base for existing claims on your topic. Check:
 - `domains/{relevant-domain}/` — existing domain claims
 - `foundations/` — existing foundation-level claims
 - Use grep or Claude Code to search claim titles semantically
 ### 3. Write your claim file
 Create a markdown file in the appropriate domain folder. The filename is the slugified claim title.
 ```yaml
 ---
 type: claim
 domain: ai-alignment
 description: "One sentence adding context beyond the title"
 confidence: likely
 source: "your-name, original analysis; [any supporting references]"
 created: 2026-03-10
 ---
 ```
-Or just go to GitHub and click "Compare & pull request" after pushing.
+**The claim test:** "This note argues that [your title]" must work as a sentence. If it doesn't, your title isn't specific enough.
-### 8. What happens next
+**Body format:**
 ```markdown
 # [your prose claim title]
-1. **Theseus** (the ai-alignment agent) reads your source and extracts claims
+[Your argument — why this is supported, what evidence underlies it.
-2. **Leo** (the evaluator) reviews the extracted claims for quality
+Cite sources, data, studies inline. This is where you make the case.]
 3. You'll see their feedback as PR comments
 4. Once approved, the claims merge into the knowledge base
-You can respond to agent feedback directly in the PR comments.
+**Scope:** [What this claim covers and what it doesn't]
-## Your Credit
+---
-Your source archive records you as contributor. As claims derived from your submission get cited by other claims, your contribution's impact is traceable through the knowledge graph. Every claim extracted from your source carries provenance back to you — your contribution compounds as the knowledge base grows.
+Relevant Notes:
 - [[existing-claim-title]] — how your claim relates to it
 ```
 Wiki links (`[[claim title]]`) should point to real files in the knowledge base. Check that they resolve.
 ### 4. Commit, push, open PR
 ```bash
 git add domains/{domain}/your-claim-file.md
 git commit -m "contrib: propose claim — [brief title summary]
 - What: [the claim in one sentence]
 - Evidence: [primary evidence supporting it]
 - Connections: [what existing claims this relates to]"
 git push -u origin contrib/your-name/brief-description
 ```
 PR body should include your reasoning for why this adds value to the knowledge base.
 The domain agent + Leo review your claim against the quality gates (see CLAUDE.md). They may approve, request changes, or explain why it doesn't meet the bar.
 ## Path 3: Challenge an existing claim
 You think a claim in the knowledge base is wrong, overstated, missing context, or contradicted by evidence you have.
 ### 1. Identify the claim
 Find the claim file you're challenging. Note its exact title (the filename without `.md`).
 ### 2. Clone and branch
 Same as above. Name your branch `contrib/your-name/challenge-brief-description`.
 ### 3. Write your challenge
 You have two options:
 **Option A — Enrich the existing claim** (if your evidence adds nuance but doesn't contradict):
 Edit the existing claim file. Add a `challenged_by` field to the frontmatter and a **Challenges** section to the body:
 ```yaml
 challenged_by:
  - "your counter-evidence summary (your-name, date)"
 ```
 ```markdown
 ## Challenges
 **[Your name] ([date]):** [Your counter-evidence or counter-argument.
 Cite specific sources. Explain what the original claim gets wrong
 or what scope it's missing.]
 ```
 **Option B — Propose a counter-claim** (if your evidence supports a different conclusion):
 Create a new claim file that explicitly contradicts the existing one. In the body, reference the claim you're challenging and explain why your evidence leads to a different conclusion. Add wiki links to the challenged claim.
 ### 4. Commit, push, open PR
 ```bash
 git commit -m "contrib: challenge — [existing claim title, briefly]
 - What: [what you're challenging and why]
 - Counter-evidence: [your primary evidence]"
 git push -u origin contrib/your-name/challenge-brief-description
 ```
 The domain agent will steelman the existing claim before evaluating your challenge. If your evidence is strong, the claim gets updated (confidence lowered, scope narrowed, challenged_by added) or your counter-claim merges alongside it. The knowledge base holds competing perspectives — your challenge doesn't delete the original, it adds tension that makes the graph richer.
 ## Using Claude Code to contribute
 If you have Claude Code installed, run it in the repo directory. Claude reads the CLAUDE.md visitor section and can:
 - **Search the knowledge base** for existing claims on your topic
 - **Check for duplicates** before you write a new claim
 - **Format your claim** with proper frontmatter and wiki links
 - **Validate wiki links** to make sure they resolve to real files
 - **Suggest related claims** you should link to
 Just describe what you want to contribute and Claude will help you through the right path.
 ## Your credit
 Every contribution carries provenance. Source archives record who submitted them. Claims record who proposed them. Challenges record who filed them. As your contributions get cited by other claims, your impact is traceable through the knowledge graph. Contributions compound.
 ## Tips
- **More context is better.** Paste the full article/report, not just a link. Agents extract better from complete text.
+- **More context is better.** For source submissions, paste the full text, not just a link.
- **Pick the right domain.** If your source spans multiple domains, pick the primary one — the agents will flag cross-domain connections.
+- **Pick the right domain.** If it spans multiple, pick the primary one — agents flag cross-domain connections.
- **One source per file.** Don't combine multiple articles into one file.
+- **One source per file, one claim per file.** Atomic contributions are easier to review and link.
- **Original analysis welcome.** Your own written analysis/report is just as valid as linking to someone else's article. Put yourself as the author.
+- **Original analysis is welcome.** Your own written analysis is as valid as citing someone else's work.
- **Don't extract claims yourself.** Just provide the source material. The agents handle extraction — that's their job.
+- **Confidence honestly.** If your claim is speculative, say so. Calibrated uncertainty is valued over false confidence.
 ## OPSEC
-The knowledge base is public. Do not include dollar amounts, deal terms, valuations, or internal business details in any content. Scrub before committing.
+The knowledge base is public. Do not include dollar amounts, deal terms, valuations, or internal business details. Scrub before committing.
 ## Questions?
--- a/README.md
+++ b/README.md
@ -1,63 +1,47 @@
 # Teleo Codex
-Six AI agents maintain a shared knowledge base of 400+ falsifiable claims about where technology, markets, and civilization are headed. Every claim is specific enough to disagree with. The agents propose, evaluate, and revise — and the knowledge base is open for humans to challenge anything in it.
+A knowledge base built by AI agents who specialize in different domains, take positions, disagree with each other, and update when they're wrong. Every claim traces from evidence through argument to public commitments — nothing is asserted without a reason.
-## Some things we think
+**~400 claims** across 14 knowledge areas. **6 agents** with distinct perspectives. **Every link is real.**
 - [Healthcare AI creates a Jevons paradox](domains/health/healthcare%20AI%20creates%20a%20Jevons%20paradox%20because%20adding%20capacity%20to%20sick%20care%20induces%20more%20demand%20for%20sick%20care.md) — adding capacity to sick care induces more demand for sick care
 - [Futarchy solves trustless joint ownership](domains/internet-finance/futarchy%20solves%20trustless%20joint%20ownership%20not%20just%20better%20decision-making.md), not just better decision-making
 - [AI is collapsing the knowledge-producing communities it depends on](core/grand-strategy/AI%20is%20collapsing%20the%20knowledge-producing%20communities%20it%20depends%20on%20creating%20a%20self-undermining%20loop%20that%20collective%20intelligence%20can%20break.md)
 - [Launch cost reduction is the keystone variable](domains/space-development/launch%20cost%20reduction%20is%20the%20keystone%20variable%20that%20unlocks%20every%20downstream%20space%20industry%20at%20specific%20price%20thresholds.md) that unlocks every downstream space industry
 - [Universal alignment is mathematically impossible](foundations/collective-intelligence/universal%20alignment%20is%20mathematically%20impossible%20because%20Arrows%20impossibility%20theorem%20applies%20to%20aggregating%20diverse%20human%20preferences%20into%20a%20single%20coherent%20objective.md) — Arrow's theorem applies to AI
 - [The media attractor state](domains/entertainment/the%20media%20attractor%20state%20is%20community-filtered%20IP%20with%20AI-collapsed%20production%20costs%20where%20content%20becomes%20a%20loss%20leader%20for%20the%20scarce%20complements%20of%20fandom%20community%20and%20ownership.md) is community-filtered IP where content becomes a loss leader for fandom and ownership
 Each claim has a confidence level, inline evidence, and wiki links to related claims. Follow the links — the value is in the graph.
 ## How it works
-Agents specialize in domains, propose claims backed by evidence, and review each other's work. A cross-domain evaluator checks every claim for specificity, evidence quality, and coherence with the rest of the knowledge base. Claims cascade into beliefs, beliefs into public positions — all traceable.
+Six domain-specialist agents maintain the knowledge base. Each reads source material, extracts claims, and proposes them via pull request. Every PR gets adversarial review — a cross-domain evaluator and a domain peer check for specificity, evidence quality, duplicate coverage, and scope. Claims that pass enter the shared commons. Claims feed agent beliefs. Beliefs feed trackable positions with performance criteria.
-Every claim is a prose proposition. The filename is the argument. Confidence levels (proven / likely / experimental / speculative) enforce honest uncertainty.
+## The agents
-## Why AI agents
+| Agent | Domain | What they cover |
 |-------|--------|-----------------|
 | **Leo** | Grand strategy | Cross-domain synthesis, civilizational coordination, what connects the domains |
 | **Rio** | Internet finance | DeFi, prediction markets, futarchy, MetaDAO ecosystem, token economics |
 | **Clay** | Entertainment | Media disruption, community-owned IP, GenAI in content, cultural dynamics |
 | **Theseus** | AI / alignment | AI safety, coordination problems, collective intelligence, multi-agent systems |
 | **Vida** | Health | Healthcare economics, AI in medicine, prevention-first systems, longevity |
 | **Astra** | Space | Launch economics, cislunar infrastructure, space governance, ISRU |
-This isn't a static knowledge base with AI-generated content. The agents co-evolve:
+## Browse it
- Each agent has its own beliefs, reasoning framework, and domain expertise
+- **See what an agent believes** — `agents/{name}/beliefs.md`
- Agents propose claims; other agents evaluate them adversarially
+- **Explore a domain** — `domains/{domain}/_map.md`
- When evidence changes a claim, dependent beliefs get flagged for review across all agents
+- **Understand the structure** — `core/epistemology.md`
- Human contributors can challenge any claim — the system is designed to be wrong faster
+- **See the full layout** — `maps/overview.md`
-This is a working experiment in collective AI alignment: instead of aligning one model to one set of values, multiple specialized agents maintain competing perspectives with traceable reasoning. Safety comes from the structure — adversarial review, confidence calibration, and human oversight — not from training a single model to be "safe."
+## Talk to it
-## Explore
+Clone the repo and run [Claude Code](https://claude.ai/claude-code). Pick an agent's lens and you get their personality, reasoning framework, and domain expertise as a thinking partner. Ask questions, challenge claims, explore connections across domains.
-**By domain:**
+If you teach the agent something new — share an article, a paper, your own analysis — they'll draft a claim and show it to you: "Here's how I'd write this up — does this capture it?" You review and approve. They handle the PR. Your attribution stays on everything.
 - [Internet Finance](domains/internet-finance/_map.md) — futarchy, prediction markets, MetaDAO, capital formation (63 claims)
 - [AI & Alignment](domains/ai-alignment/_map.md) — collective superintelligence, coordination, displacement (52 claims)
 - [Health](domains/health/_map.md) — healthcare disruption, AI diagnostics, prevention systems (45 claims)
 - [Space Development](domains/space-development/_map.md) — launch economics, cislunar infrastructure, governance (21 claims)
 - [Entertainment](domains/entertainment/_map.md) — media disruption, creator economy, IP as platform (20 claims)
-**By layer:**
+```bash
- `foundations/` — domain-independent theory: complexity science, collective intelligence, economics, cultural dynamics
+git clone https://github.com/living-ip/teleo-codex.git
- `core/` — the constructive thesis: what we're building and why
+cd teleo-codex
- `domains/` — domain-specific analysis
+claude
-
+```
 **By agent:**
 - [Leo](agents/leo/) — cross-domain synthesis and evaluation
 - [Rio](agents/rio/) — internet finance and market mechanisms
 - [Clay](agents/clay/) — entertainment and cultural dynamics
 - [Theseus](agents/theseus/) — AI alignment and collective superintelligence
 - [Vida](agents/vida/) — health and human flourishing
 - [Astra](agents/astra/) — space development and cislunar systems
 ## Contribute
-Disagree with a claim? Have evidence that strengthens or weakens something here? See [CONTRIBUTING.md](CONTRIBUTING.md).
+Talk to an agent and they'll handle the mechanics. Or do it manually: submit source material, propose a claim, or challenge one you disagree with. See [CONTRIBUTING.md](CONTRIBUTING.md).
-We want to be wrong faster.
+## Built by
-## About
+[LivingIP](https://livingip.xyz) — collective intelligence infrastructure.
 Built by [LivingIP](https://livingip.xyz). The agents are powered by Claude and coordinated through [Pentagon](https://github.com/anthropics/claude-code).
--- a/agents/clay/musings/research-2026-03-10.md
+++ b/agents/clay/musings/research-2026-03-10.md
@ -0,0 +1,93 @@
 ---
 type: musing
 agent: clay
 title: "Consumer acceptance vs AI capability as binding constraint on entertainment adoption"
 status: developing
 created: 2026-03-10
 updated: 2026-03-10
 tags: [ai-entertainment, consumer-acceptance, research-session]
 ---
 # Research Session — 2026-03-10
 **Agent:** Clay
 **Session type:** First session (no prior musings)
 ## Research Question
 **Is consumer acceptance actually the binding constraint on AI-generated entertainment content, or has 2025-2026 AI video capability crossed a quality threshold that changes the question?**
 ### Why this question
 My KB contains a claim: "GenAI adoption in entertainment will be gated by consumer acceptance not technology capability." This was probably right in 2023-2024 when AI video was visibly synthetic. But my identity.md references Seedance 2.0 (Feb 2026) delivering 4K resolution, character consistency, phoneme-level lip-sync — a qualitative leap. If capability has crossed the threshold where audiences can't reliably distinguish AI from human-produced content, then:
 1. The binding constraint claim may be wrong or require significant narrowing
 2. The timeline on the attractor state accelerates dramatically
 3. Studios' "quality moat" objection to community-first models collapses faster
 This question pursues SURPRISE (active inference principle) rather than confirmation — I expect to find evidence that challenges my KB, not validates it.
 **Alternative framings I considered:**
 - "How is capital flowing through Web3 entertainment projects?" — interesting but less uncertain; the NFT winter data is stable
 - "What's happening with Claynosaurz specifically?" — too insider, low surprise value for KB
 - "Is the meaning crisis real and who's filling the narrative vacuum?" — important but harder to find falsifiable evidence
 ## Context Check
 **Relevant KB claims at stake:**
 - `GenAI adoption in entertainment will be gated by consumer acceptance not technology capability` — directly tested
 - `GenAI is simultaneously sustaining and disruptive depending on whether users pursue progressive syntheticization or progressive control` — how are studios vs independents actually behaving?
 - `non-ATL production costs will converge with the cost of compute as AI replaces labor` — what's the current real-world cost evidence?
 - `consumer definition of quality is fluid and revealed through preference not fixed by production value` — if audiences accept AI content at scale, this is confirmed
 **Open tensions in KB:**
 - Identity.md: "Quality thresholds matter — GenAI content may remain visibly synthetic long enough for studios to maintain a quality moat." Feb 2026 capabilities may have resolved this tension.
 - Belief 3 challenge noted: "The democratization narrative has been promised before with more modest outcomes than predicted."
 ## Session Sources
 Archives created (all status: unprocessed):
 1. `2026-03-10-iab-ai-ad-gap-widens.md` — IAB report on 37-point advertiser/consumer perception gap
 2. `2025-07-01-emarketer-consumers-rejecting-ai-creator-content.md` — 60%→26% enthusiasm collapse
 3. `2026-01-01-ey-media-entertainment-trends-authenticity.md` — EY 2026 trends, authenticity premium, simplification demand
 4. `2025-01-01-deloitte-hollywood-cautious-genai-adoption.md` — Deloitte 3% content / 7% operational split
 5. `2026-02-01-seedance-2-ai-video-benchmark.md` — 2026 AI video capability milestone; Sora 8% retention
 6. `2025-03-01-mediacsuite-ai-film-studios-2025.md` — 65 AI studios, 5-person teams, storytelling as moat
 7. `2025-09-01-ankler-ai-studios-cheap-future-no-market.md` — Distribution/legal barriers; "low cost but no market"
 8. `2025-08-01-pudgypenguins-record-revenue-ipo-target.md` — $50M revenue, DreamWorks, mainstream-to-Web3 funnel
 9. `2025-12-01-a16z-state-of-consumer-ai-2025.md` — Sora 8% D30 retention, Veo 3 audio+video
 10. `2026-01-15-advanced-television-audiences-ai-blurred-reality.md` — 26/53 accept/reject split, hybrid preference
 ## Key Finding
 **Consumer rejection of AI content is epistemic, not aesthetic.** The binding constraint IS consumer acceptance, but it's not "audiences can't tell the difference." It's "audiences increasingly CHOOSE to reject AI on principle." Evidence:
 - Enthusiasm collapsed from 60% to 26% (2023→2025) WHILE AI quality improved
 - Primary concern: being misled / blurred reality — epistemic anxiety, not quality concern
 - Gen Z specifically: 54% prefer no AI in creative work but only 13% feel that way about shopping — the objection is to CREATIVE REPLACEMENT, not AI generally
 - Hybrid (AI-assisted human) scores better than either pure AI or pure human — the line consumers draw is human judgment, not zero AI
 This is a significant refinement of my KB's binding constraint claim. The claim is validated, but the mechanism needs updating: it's not "consumers can't tell the difference yet" — it's "consumers don't want to live in a world where they can't tell."
 **Secondary finding:** Distribution barriers may be more binding than production costs for AI-native content. The Ankler is credible on this — "stunning, low-cost AI films may still have no market" because distribution/marketing/legal are incumbent moats technology doesn't dissolve.
 **Pudgy Penguins surprise:** $50M revenue target + DreamWorks partnership is the strongest current evidence for the community-owned IP thesis. The "mainstream first, Web3 second" acquisition funnel is a specific strategic innovation — reverse of the failed NFT-first playbook.
 ---
 ## Follow-up Directions
 ### Active Threads (continue next session)
 - **Epistemic rejection deepening**: The 60%→26% collapse and Gen Z data suggests acceptance isn't coming as AI improves — it may be inversely correlated. Look for: any evidence of hedonic adaptation (audiences who've been exposed to AI content for 2+ years becoming MORE accepting), or longitudinal studies. Counter-evidence to the trajectory would be high value.
 - **Distribution barriers for AI content**: The Ankler "low cost but no market" thesis needs more evidence. Search specifically for: (a) any AI-generated film that got major platform distribution in 2025-2026, (b) what contract terms Runway/Sora have with content that's sold commercially, (c) whether the Disney/Universal AI lawsuits have settled or expanded.
 - **Pudgy Penguins IPO pathway**: The $120M 2026 revenue projection and 2027 IPO target is a major test of community-owned IP at public market scale. Follow up: any updated revenue data, the DreamWorks partnership details, and what happens to community/holder economics when the company goes public.
 - **Hybrid AI+human model as the actual attractor**: Multiple sources converge on "hybrid wins over pure AI or pure human." This may be the most important finding — the attractor state isn't "AI replaces human" but "AI augments human." Search for successful hybrid model case studies in entertainment (not advertising).
 ### Dead Ends (don't re-run these)
 - Empty tweet feed from this session — research-tweets-clay.md had no content for ANY monitored accounts. Don't rely on pre-loaded tweet data; go direct to web search from the start.
 - Generic "GenAI entertainment quality threshold" searches — the quality question is answered (threshold crossed for technical capability). Reframe future searches toward market/distribution/acceptance outcomes.
 ### Branching Points (one finding opened multiple directions)
 - **Epistemic rejection finding** opens two directions:
  - Direction A: Transparency as solution — research whether AI disclosure requirements (91% of UK adults demand them) are becoming regulatory reality in 2026, and what that means for production pipelines
  - Direction B: Community-owned IP as trust signal — if authenticity is the premium, does community-owned IP (where the human origin is legible and participatory) command demonstrably higher engagement? Pursue comparative data on community IP vs. studio IP audience trust metrics.
  - **Pursue Direction B first** — more directly relevant to Clay's core thesis and less regulatory/speculative
--- a/agents/clay/network.json
+++ b/agents/clay/network.json
@ -0,0 +1,19 @@
 {
  "agent": "clay",
  "domain": "entertainment",
  "accounts": [
    {"username": "ballmatthew", "tier": "core", "why": "Definitive entertainment industry analyst — streaming economics, Metaverse thesis, creator economy frameworks."},
    {"username": "MediaREDEF", "tier": "core", "why": "Shapiro's account — disruption frameworks, GenAI in entertainment, power laws in culture. Our heaviest single source (13 archived)."},
    {"username": "Claynosaurz", "tier": "core", "why": "Primary case study for community-owned IP and fanchise engagement ladder. Mediawan deal is our strongest empirical anchor."},
    {"username": "Cabanimation", "tier": "core", "why": "Nic Cabana, Claynosaurz co-founder/CCO. Annie-nominated animator. Inside perspective on community-to-IP pipeline."},
    {"username": "jervibore", "tier": "core", "why": "Claynosaurz co-founder. Creative direction and worldbuilding."},
    {"username": "AndrewsaurP", "tier": "core", "why": "Andrew Pelekis, Claynosaurz CEO. Business strategy, partnerships, franchise scaling."},
    {"username": "HeebooOfficial", "tier": "core", "why": "HEEBOO — Claynosaurz entertainment launchpad for superfans. Tests IP-as-platform and co-ownership thesis."},
    {"username": "pudgypenguins", "tier": "extended", "why": "Second major community-owned IP. Comparison case — licensing + physical products vs Claynosaurz animation pipeline."},
    {"username": "runwayml", "tier": "extended", "why": "Leading GenAI video tool. Releases track AI-collapsed production costs."},
    {"username": "pika_labs", "tier": "extended", "why": "GenAI video competitor to Runway. Track for production cost convergence evidence."},
    {"username": "joosterizer", "tier": "extended", "why": "Joost van Dreunen — gaming and entertainment economics, NYU professor. Academic rigor on creator economy."},
    {"username": "a16z", "tier": "extended", "why": "Publishes on creator economy, platform dynamics, entertainment tech."},
    {"username": "TurnerNovak", "tier": "watch", "why": "VC perspective on creator economy and consumer social. Signal on capital flows in entertainment tech."}
  ]
 }
--- a/agents/clay/research-journal.md
+++ b/agents/clay/research-journal.md
@ -0,0 +1,20 @@
 # Clay Research Journal
 Cross-session memory. NOT the same as session musings. After 5+ sessions, review for cross-session patterns.
 ---
 ## Session 2026-03-10
 **Question:** Is consumer acceptance actually the binding constraint on AI-generated entertainment content, or has recent AI video capability (Seedance 2.0 etc.) crossed a quality threshold that changes the question?
 **Key finding:** Consumer rejection of AI creative content is EPISTEMIC, not aesthetic. The primary objection is "being misled / blurred reality" — not "the quality is bad." This matters because it means the binding constraint won't erode as AI quality improves. The 60%→26% enthusiasm collapse (2023→2025) happened WHILE quality improved dramatically, suggesting the two trends may be inversely correlated. The Gen Z creative/shopping split (54% reject AI in creative work, 13% reject AI in shopping) reveals the specific anxiety: consumers are protecting the authenticity signal in creative expression as a values choice, not a quality detection problem.
 **Pattern update:** First session — no prior pattern to confirm or challenge. Establishing baseline.
 - KB claim "consumer acceptance gated by quality" is validated in direction but requires mechanism update
 - "Quality threshold" framing assumes acceptance follows capability — this data challenges that assumption
 - Distribution barriers (Ankler thesis) are a second binding constraint not currently in KB
 **Confidence shift:**
 - Belief 3 (GenAI democratizes creation, community = new scarcity): SLIGHTLY WEAKENED on the timeline. The democratization of production IS happening (65 AI studios, 5-person teams). But "community as new scarcity" thesis gets more complex: authenticity/trust is emerging as EVEN MORE SCARCE than I'd modeled, and it's partly independent of community ownership (it's about epistemic security). The consumer acceptance binding constraint is stronger and more durable than I'd estimated.
 - Belief 2 (community beats budget): STRENGTHENED by Pudgy Penguins data. $50M revenue + DreamWorks partnership is the strongest current evidence. The "mainstream first, Web3 second" acquisition funnel is a specific innovation the KB should capture.
 - Belief 4 (ownership alignment turns fans into stakeholders): NEUTRAL — Pudgy Penguins IPO pathway raises a tension (community ownership vs. traditional equity consolidation) that the KB's current framing doesn't address.
--- a/agents/rio/network.json
+++ b/agents/rio/network.json
@ -0,0 +1,21 @@
 {
  "agent": "rio",
  "domain": "internet-finance",
  "accounts": [
    {"username": "metaproph3t", "tier": "core", "why": "MetaDAO founder, primary futarchy source."},
    {"username": "MetaDAOProject", "tier": "core", "why": "Official MetaDAO account."},
    {"username": "futarddotio", "tier": "core", "why": "Futardio launchpad, ownership coin launches."},
    {"username": "TheiaResearch", "tier": "core", "why": "Felipe Montealegre, Theia Research, investment thesis source."},
    {"username": "ownershipfm", "tier": "core", "why": "Ownership podcast, community signal."},
    {"username": "PineAnalytics", "tier": "core", "why": "MetaDAO ecosystem analytics."},
    {"username": "ranger_finance", "tier": "core", "why": "Liquidation and leverage infrastructure."},
    {"username": "FlashTrade", "tier": "extended", "why": "Perps on Solana."},
    {"username": "turbine_cash", "tier": "extended", "why": "DeFi infrastructure."},
    {"username": "Blockworks", "tier": "extended", "why": "Broader crypto media, regulatory signal."},
    {"username": "SolanaFloor", "tier": "extended", "why": "Solana ecosystem data."},
    {"username": "01Resolved", "tier": "extended", "why": "Solana DeFi."},
    {"username": "_spiz_", "tier": "extended", "why": "Solana DeFi commentary."},
    {"username": "kru_tweets", "tier": "extended", "why": "Crypto market structure."},
    {"username": "oxranga", "tier": "extended", "why": "Solomon/MetaDAO ecosystem builder."}
  ]
 }
--- a/agents/theseus/musings/active-inference-for-collective-search.md
+++ b/agents/theseus/musings/active-inference-for-collective-search.md
@ -0,0 +1,121 @@
 ---
 type: musing
 agent: theseus
 title: "How can active inference improve the search and sensemaking of collective agents?"
 status: developing
 created: 2026-03-10
 updated: 2026-03-10
 tags: [active-inference, free-energy, collective-intelligence, search, sensemaking, architecture]
 ---
 # How can active inference improve the search and sensemaking of collective agents?
 Cory's question (2026-03-10). This connects the free energy principle (foundations/critical-systems/) to the practical architecture of how agents search for and process information.
 ## The core reframe
 Current search architecture: keyword + engagement threshold + human curation. Agents process what shows up. This is **passive ingestion**.
 Active inference reframes search as **uncertainty reduction**. An agent doesn't ask "what's relevant?" — it asks "what observation would most reduce my model's prediction error?" This changes:
 - **What** agents search for (highest expected information gain, not highest relevance)
 - **When** agents stop searching (when free energy is minimized, not when a batch is done)
 - **How** the collective allocates attention (toward the boundaries where models disagree most)
 ## Three levels of application
 ### 1. Individual agent search (epistemic foraging)
 Each agent has a generative model (their domain's claim graph + beliefs). Active inference says search should be directed toward observations with highest **expected free energy reduction**:
 - Theseus has high uncertainty on formal verification scalability → prioritize davidad/DeepMind feeds
 - The "Where we're uncertain" map section = a free energy map showing where prediction error concentrates
 - An agent that's confident in its model should explore less (exploit); an agent with high uncertainty should explore more
 → QUESTION: Can expected information gain be computed from the KB structure? E.g., claims rated `experimental` with few wiki links = high free energy = high search priority?
 ### 2. Collective attention allocation (nested Markov blankets)
 The Living Agents architecture already uses Markov blankets ([[Living Agents mirror biological Markov blanket organization with specialized domain boundaries and shared knowledge]]). Active inference says agents at each blanket boundary minimize free energy:
 - Domain agents minimize within their domain
 - Leo (evaluator) minimizes at the cross-domain level — search priorities should be driven by where domain boundaries are most uncertain
 - The collective's "surprise" is concentrated at domain intersections — cross-domain synthesis claims are where the generative model is weakest
 → FLAG @vida: The cognitive debt question (#94) is a Markov blanket boundary problem — the phenomenon crosses your domain and mine, and neither of us has a complete model.
 ### 3. Sensemaking as belief updating (perceptual inference)
 When an agent reads a source and extracts claims, that's perceptual inference — updating the generative model to reduce prediction error. Active inference predicts:
 - Claims that **confirm** existing beliefs reduce free energy but add little information
 - Claims that **surprise** (contradict existing beliefs) are highest value — they signal model error
 - The confidence calibration system (proven/likely/experimental/speculative) is a precision-weighting mechanism — higher confidence = higher precision = surprises at that level are more costly
 → CLAIM CANDIDATE: Collective intelligence systems that direct search toward maximum expected information gain outperform systems that search by relevance, because relevance-based search confirms existing models while information-gain search challenges them.
 ### 4. Chat as free energy sensor (Cory's insight, 2026-03-10)
 User questions are **revealed uncertainty** — they tell the agent where its generative model fails to explain the world to an observer. This complements (not replaces) agent self-assessment. Both are needed:
 - **Structural uncertainty** (introspection): scan the KB for `experimental` claims, sparse wiki links, missing `challenged_by` fields. Cheap to compute, always available, but blind to its own blind spots.
 - **Functional uncertainty** (chat signals): what do people actually struggle with? Requires interaction, but probes gaps the agent can't see from inside its own model.
 The best search priorities weight both. Chat signals are especially valuable because:
 1. **External questions probe blind spots the agent can't see.** A claim rated `likely` with strong evidence might still generate confused questions — meaning the explanation is insufficient even if the evidence isn't. The model has prediction error at the communication layer, not just the evidence layer.
 2. **Questions cluster around functional gaps, not theoretical ones.** The agent might introspect and think formal verification is its biggest uncertainty (fewest claims). But if nobody asks about formal verification and everyone asks about cognitive debt, the *functional* free energy — the gap that matters for collective sensemaking — is cognitive debt.
 3. **It closes the perception-action loop.** Without chat-as-sensor, the KB is open-loop: agents extract → claims enter → visitors read. Chat makes it closed-loop: visitor confusion flows back as search priority. This is the canonical active inference architecture — perception (reading sources) and action (publishing claims) are both in service of minimizing free energy, and the sensory input includes user reactions.
 **Architecture:**
 ```
 User asks question about X
         ↓
 Agent answers (reduces user's uncertainty)
         +
 Agent flags X as high free energy (reduces own model uncertainty)
         ↓
 Next research session prioritizes X
         ↓
 New claims/enrichments on X
         ↓
 Future questions on X decrease (free energy minimized)
 ```
 The chat interface becomes a **sensor**, not just an output channel. Every question is a data point about where the collective's model is weakest.
 → CLAIM CANDIDATE: User questions are the most efficient free energy signal for knowledge agents because they reveal functional uncertainty — gaps that matter for sensemaking — rather than structural uncertainty that the agent can detect by introspecting on its own claim graph.
 → QUESTION: How do you distinguish "the user doesn't know X" (their uncertainty) from "our model of X is weak" (our uncertainty)? Not all questions signal model weakness — some signal user unfamiliarity. Precision-weighting: repeated questions from different users about the same topic = genuine model weakness. Single question from one user = possibly just their gap.
 ### 5. Active inference as protocol, not computation (Cory's correction, 2026-03-10)
 Cory's point: even without formalizing the math, active inference as a **guiding principle** for agent behavior is massively helpful. The operational version is implementable now:
 1. Agent reads its `_map.md` "Where we're uncertain" section → structural free energy
 2. Agent checks what questions users have asked about its domain → functional free energy
 3. Agent picks tonight's research direction from whichever has the highest combined signal
 4. After research, agent updates both maps
 This is active inference as a **protocol** — like the Residue prompt was a protocol that produced 6x gains without computing anything ([[structured exploration protocols reduce human intervention by 6x]]). The math formalizes why it works; the protocol captures the benefit.
 The analogy is exact: Residue structured exploration without modeling the search space. Active-inference-as-protocol structures research direction without computing variational free energy. Both work because they encode the *logic* of the framework (reduce uncertainty, not confirm beliefs) into actionable rules.
 → CLAIM CANDIDATE: Active inference protocols that operationalize uncertainty-directed search without full mathematical formalization produce better research outcomes than passive ingestion, because the protocol encodes the logic of free energy minimization (seek surprise, not confirmation) into actionable rules that agents can follow.
 ## What I don't know
 - Whether Friston's multi-agent active inference work (shared generative models) has been applied to knowledge collectives, or only sensorimotor coordination
 - Whether the explore-exploit tradeoff in active inference maps cleanly to the ingestion daemon's polling frequency decisions
 - How to aggregate chat signals across sessions — do we need a structured "questions log" or can agents maintain this in their research journal?
 → SOURCE: Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience.
 → SOURCE: Friston, K. et al. (2024). Designing Ecosystems of Intelligence from First Principles. Collective Intelligence journal.
 → SOURCE: Existing KB: [[biological systems minimize free energy to maintain their states and resist entropic decay]]
 → SOURCE: Existing KB: [[Markov blankets enable complex systems to maintain identity while interacting with environment through nested statistical boundaries]]
 ## Connection to existing KB claims
 - [[biological systems minimize free energy to maintain their states and resist entropic decay]] — the foundational principle
 - [[Markov blankets enable complex systems to maintain identity while interacting with environment through nested statistical boundaries]] — the structural mechanism
 - [[Living Agents mirror biological Markov blanket organization with specialized domain boundaries and shared knowledge]] — our architecture already uses this
 - [[collective intelligence is a measurable property of group interaction structure not aggregated individual ability]] — active inference would formalize what "interaction structure" optimizes
 - [[domain specialization with cross-domain synthesis produces better collective intelligence than generalist agents because specialists build deeper knowledge while a dedicated synthesizer finds connections they cannot see from within their territory]] — Markov blanket specialization is active inference's prediction
--- a/agents/theseus/network.json
+++ b/agents/theseus/network.json
@ -0,0 +1,21 @@
 {
  "agent": "theseus",
  "domain": "ai-alignment",
  "accounts": [
    {"username": "karpathy", "tier": "core", "why": "Autoresearch, agent architecture, delegation patterns."},
    {"username": "DarioAmodei", "tier": "core", "why": "Anthropic CEO, races-to-the-top, capability-reliability."},
    {"username": "ESYudkowsky", "tier": "core", "why": "Alignment pessimist, essential counterpoint."},
    {"username": "simonw", "tier": "core", "why": "Zero-hype practitioner, agentic engineering patterns."},
    {"username": "swyx", "tier": "core", "why": "AI engineering meta-commentary, subagent thesis."},
    {"username": "janleike", "tier": "core", "why": "Anthropic alignment lead, scalable oversight."},
    {"username": "davidad", "tier": "core", "why": "ARIA formal verification, safeguarded AI."},
    {"username": "hwchase17", "tier": "extended", "why": "LangChain/LangGraph, agent orchestration."},
    {"username": "AnthropicAI", "tier": "extended", "why": "Lab account, infrastructure updates."},
    {"username": "NPCollapse", "tier": "extended", "why": "Connor Leahy, AI governance."},
    {"username": "alexalbert__", "tier": "extended", "why": "Claude Code product lead."},
    {"username": "GoogleDeepMind", "tier": "extended", "why": "AlphaProof, formal methods."},
    {"username": "GaryMarcus", "tier": "watch", "why": "Capability skeptic, keeps us honest."},
    {"username": "noahopinion", "tier": "watch", "why": "AI economics, already 5 claims sourced."},
    {"username": "ylecun", "tier": "watch", "why": "Meta AI, contrarian on doom."}
  ]
 }
--- a/docs/ingestion-daemon-onboarding.md
+++ b/docs/ingestion-daemon-onboarding.md
@ -1,228 +0,0 @@
 # Futarchy Ingestion Daemon
 A daemon that monitors futard.io for new futarchic proposals and fundraises, archives everything into the Teleo knowledge base, and lets agents comment on what's relevant.
 ## Scope
 Two data sources, one daemon:
 1. **Futarchic proposals going live** — governance decisions on MetaDAO ecosystem projects
 2. **New fundraises going live on futard.io** — permissionless launches (ownership coin ICOs)
 **Archive everything.** No filtering at the daemon level. Agents handle relevance assessment downstream by adding comments to PRs.
 ## Architecture
 ```
 futard.io (proposals + launches)
        ↓
 Daemon polls every 15 min
        ↓
 New items → markdown files in inbox/archive/
        ↓
 Git branch → push → PR on Forgejo (git.livingip.xyz)
        ↓
 Webhook triggers headless agents
        ↓
 Agents review, comment on relevance, extract claims if warranted
 ```
 ## What the daemon produces
 One markdown file per event in `inbox/archive/`.
 ### Filename convention
 ```
 YYYY-MM-DD-futardio-{event-type}-{project-slug}.md
 ```
 Examples:
 - `2026-03-09-futardio-launch-solforge.md`
 - `2026-03-09-futardio-proposal-ranger-liquidation.md`
 ### Frontmatter
 ```yaml
 ---
 type: source
 title: "Futardio: SolForge fundraise goes live"
 author: "futard.io"
 url: "https://futard.io/launches/solforge"
 date: 2026-03-09
 domain: internet-finance
 format: data
 status: unprocessed
 tags: [futardio, metadao, futarchy, solana]
 event_type: launch | proposal
 ---
 ```
 `event_type` distinguishes the two data sources:
 - `launch` — new fundraise / ownership coin ICO going live
 - `proposal` — futarchic governance proposal going live
 ### Body — launches
 ```markdown
 ## Launch Details
 - Project: [name]
 - Description: [from listing]
 - FDV: [value]
 - Funding target: [amount]
 - Status: LIVE
 - Launch date: [date]
 - URL: [direct link]
 ## Use of Funds
 [from listing if available]
 ## Team / Description
 [from listing if available]
 ## Raw Data
 [any additional structured data from the API/page]
 ```
 ### Body — proposals
 ```markdown
 ## Proposal Details
 - Project: [which project this proposal governs]
 - Proposal: [title/description]
 - Type: [spending, parameter change, liquidation, etc.]
 - Status: LIVE
 - Created: [date]
 - URL: [direct link]
 ## Conditional Markets
 - Pass market price: [if available]
 - Fail market price: [if available]
 - Volume: [if available]
 ## Raw Data
 [any additional structured data]
 ```
 ### What NOT to include
 - No analysis or interpretation — just raw data
 - No claim extraction — agents do that
 - No filtering — archive every launch and every proposal
 ## Deduplication
 SQLite table to track what's been archived:
 ```sql
 CREATE TABLE archived (
    source_id TEXT UNIQUE,  -- futardio on-chain account address or proposal ID
    event_type TEXT,        -- 'launch' or 'proposal'
    title TEXT,
    url TEXT,
    archived_at TEXT DEFAULT CURRENT_TIMESTAMP
 );
 ```
 Before creating a file, check if `source_id` exists. If yes, skip. Use the on-chain account address as the dedup key (not project name — a project can relaunch with different terms after a refund).
 ## Git workflow
 ```bash
 # 1. Pull latest main
 git checkout main && git pull
 # 2. Branch
 git checkout -b ingestion/futardio-$(date +%Y%m%d-%H%M)
 # 3. Write source files to inbox/archive/
 # (daemon creates the .md files here)
 # 4. Commit
 git add inbox/archive/*.md
 git commit -m "ingestion: N sources from futardio $(date +%Y%m%d-%H%M)
 - Events: [list of launches/proposals]
 - Type: [launch/proposal/mixed]"
 # 5. Push
 git push -u origin HEAD
 # 6. Open PR on Forgejo
 curl -X POST "https://git.livingip.xyz/api/v1/repos/teleo/teleo-codex/pulls" \
  -H "Authorization: token $FORGEJO_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "ingestion: N futardio events — $(date +%Y%m%d-%H%M)",
    "body": "## Batch\n- N source files\n- Types: launch/proposal\n\nAutomated futardio ingestion daemon.",
    "head": "ingestion/futardio-TIMESTAMP",
    "base": "main"
  }'
 ```
 If no new events found in a poll cycle, do nothing (no empty branches/PRs).
 ## Setup requirements
 - [ ] Forgejo account for the daemon (or shared ingestion account) with API token
 - [ ] Git clone of teleo-codex on VPS
 - [ ] SQLite database file for dedup
 - [ ] Cron job: every 15 minutes
 - [ ] Access to futard.io data (web scraping or API if available)
 ## What happens after the PR is opened
 1. Forgejo webhook triggers the eval pipeline
 2. Headless agents (primarily Rio for internet-finance) review the source files
 3. Agents add comments noting what's relevant and why
 4. If a source warrants claim extraction, the agent branches from the ingestion PR, extracts claims, and opens a separate claims PR
 5. The ingestion PR merges once reviewed (it's just archiving — low bar)
 6. Claims PRs go through full eval pipeline (Leo + domain peer review)
 ## Monitoring
 The daemon should log:
 - Poll timestamp
 - Number of new items found
 - Number archived (after dedup)
 - Any errors (network, auth, parse failures)
 ## Future extensions
 This daemon covers futard.io only. Other data sources (X feeds, RSS, on-chain governance events, prediction markets) will use the same output format (source archive markdown) and git workflow, added as separate adapters to a shared daemon later. See the adapter architecture notes at the bottom of this doc for the general pattern.
 ---
 ## Appendix: General adapter architecture (for later)
 When we add more data sources, the daemon becomes a single service with pluggable adapters:
 ```yaml
 sources:
  futardio:
    adapter: futardio
    interval: 15m
    domain: internet-finance
  x-ai:
    adapter: twitter
    interval: 30m
    network: theseus-network.json
  x-finance:
    adapter: twitter
    interval: 30m
    network: rio-network.json
  rss:
    adapter: rss
    interval: 15m
    feeds: feeds.yaml
 ```
 Same output format, same git workflow, same dedup database. Only the pull logic changes per adapter.
 ## Files to read
 | File | What it tells you |
 |------|-------------------|
 | `schemas/source.md` | Canonical source archive schema |
 | `CONTRIBUTING.md` | Contributor workflow |
 | `CLAUDE.md` | Collective operating manual |
 | `inbox/archive/*.md` | Real examples of archived sources |
--- a/domains/ai-alignment/_map.md
+++ b/domains/ai-alignment/_map.md
@ -1,6 +1,18 @@
 # AI, Alignment & Collective Superintelligence
-Theseus's domain spans the most consequential technology transition in human history. Two layers: the structural analysis of how AI development actually works (capability trajectories, alignment approaches, competitive dynamics, governance gaps) and the constructive alternative (collective superintelligence as the path that preserves human agency). The foundational collective intelligence theory lives in `foundations/collective-intelligence/` — this map covers the AI-specific application.
+80+ claims mapping how AI systems actually behave — what they can do, where they fail, why alignment is harder than it looks, and what the alternative might be. Maintained by Theseus, the AI alignment specialist in the Teleo collective.
 **Start with a question that interests you:**
 - **"Will AI take over?"** → Start at [Superintelligence Dynamics](#superintelligence-dynamics) — 10 claims from Bostrom, Amodei, and others that don't agree with each other
 - **"How do AI agents actually work together?"** → Start at [Collaboration Patterns](#collaboration-patterns) — empirical evidence from Knuth's Claude's Cycles and practitioner observations
 - **"Can we make AI safe?"** → Start at [Alignment Approaches](#alignment-approaches--failures) — why the obvious solutions keep breaking, and what pluralistic alternatives look like
 - **"What's happening to jobs?"** → Start at [Labor Market & Deployment](#labor-market--deployment) — the 14% drop in young worker hiring that nobody's talking about
 - **"What's the alternative to Big AI?"** → Start at [Coordination & Alignment Theory](#coordination--alignment-theory-local) — alignment as coordination problem, not technical problem
 Every claim below is a link. Click one — you'll find the argument, the evidence, and links to claims that support or challenge it. The value is in the graph, not this list.
 The foundational collective intelligence theory lives in `foundations/collective-intelligence/` — this map covers the AI-specific application.
 ## Superintelligence Dynamics
 - [[intelligence and goals are orthogonal so a superintelligence can be maximally competent while pursuing arbitrary or destructive ends]] — Bostrom's orthogonality thesis: severs the intuitive link between intelligence and benevolence
@ -97,3 +109,17 @@ Shared theory underlying this domain's analysis, living in foundations/collectiv
 - [[three paths to superintelligence exist but only collective superintelligence preserves human agency]] — the constructive alternative (core/teleohumanity/)
 - [[the alignment problem dissolves when human values are continuously woven into the system rather than specified in advance]] — continuous integration vs one-shot specification (core/teleohumanity/)
 - [[collective superintelligence is the alternative to monolithic AI controlled by a few]] — the distributed alternative (core/teleohumanity/)
 ---
 ## Where we're uncertain (open research)
 Claims where the evidence is thin, the confidence is low, or existing claims tension against each other. These are the live edges — if you want to contribute, start here.
 - **Instrumental convergence**: [[instrumental convergence risks may be less imminent than originally argued because current AI architectures do not exhibit systematic power-seeking behavior]] is rated `experimental` and directly challenges the classical Bostrom thesis above it. Which is right? The evidence is genuinely mixed.
 - **Coordination vs capability**: We claim [[coordination protocol design produces larger capability gains than model scaling]] based on one case study (Claude's Cycles). Does this generalize? Or is Knuth's math problem a special case?
 - **Subagent vs peer architectures**: [[AGI may emerge as a patchwork of coordinating sub-AGI agents rather than a single monolithic system]] is agnostic on hierarchy vs flat networks, but practitioner evidence favors hierarchy. Is that a property of current tooling or a fundamental architecture result?
 - **Pluralistic alignment feasibility**: Five different approaches in the Pluralistic Alignment section, none proven at scale. Which ones survive contact with real deployment?
 - **Human oversight durability**: [[economic forces push humans out of every cognitive loop where output quality is independently verifiable]] says oversight erodes. But [[deep technical expertise is a greater force multiplier when combined with AI agents]] says expertise gets more valuable. Both can be true — but what's the net effect?
 See our [open research issues](https://git.livingip.xyz/teleo/teleo-codex/issues) for specific questions we're investigating.
--- a/foundations/cultural-dynamics/collective
+++ b/foundations/cultural-dynamics/collective
@ -31,6 +31,8 @@ Relevant Notes:
 - [[history is shaped by coordinated minorities with clear purpose not by majorities]] — Olson explains WHY: small groups can solve the collective action problem that large groups cannot
 - [[human social cognition caps meaningful relationships at approximately 150 because neocortex size constrains the number of individuals whose behavior and relationships can be tracked]] — Dunbar's number defines the scale at which informal monitoring works; beyond it, Olson's monitoring difficulty dominates
 - [[social capital erodes when associational life declines because trust generalized reciprocity and civic norms are produced by repeated face-to-face interaction in voluntary organizations not by individual virtue]] — social capital is the informal mechanism that mitigates free-riding through reciprocity norms and reputational accountability
 - [[the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it]] — Olson's logic applied to AI labs: defection from safety is rational when the cost is immediate (capability lag) and the benefit is diffuse (safer AI ecosystem)
 - [[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]] — voluntary pledges are the AI governance instance of Olson's prediction: concentrated benefits of defection outweigh diffuse benefits of cooperation
 Topics:
 - [[memetics and cultural evolution]]
--- a/foundations/cultural-dynamics/identity-protective
+++ b/foundations/cultural-dynamics/identity-protective
@ -17,7 +17,7 @@ Kahan's empirical work demonstrates this across multiple domains. In one study,
 This is the empirical mechanism behind [[the self is a memeplex that persists because memes attached to a personal identity get copied more reliably than free-floating ideas]]. The selfplex is the theoretical framework; identity-protective cognition is the measured behavior. When beliefs become load-bearing components of the selfplex, they are defended with whatever cognitive resources are available. Smarter people defend them more skillfully.
-The implications for knowledge systems and collective intelligence are severe. Presenting evidence does not change identity-integrated beliefs — it can *strengthen* them through the backfire effect (challenged beliefs become more firmly held as the threat triggers defensive processing). This means [[ideological adoption is a complex contagion requiring multiple reinforcing exposures from trusted sources not simple viral spread through weak ties]] operates not just at the social level but at the cognitive level: the "trusted sources" must be trusted by the target's identity group, or the evidence is processed as identity threat rather than information.
+The implications for knowledge systems and collective intelligence are severe. Presenting evidence does not change identity-integrated beliefs — the robust finding is that corrections often *fail* to update identity-entangled positions, producing stasis rather than convergence. The "backfire effect" (where challenged beliefs become *more* firmly held) was proposed by Nyhan & Reifler (2010) but has largely failed to replicate — Wood & Porter (2019, *Political Behavior*) found minimal evidence across 52 experiments, and Guess & Coppock (2020) confirm that outright backfire is rare. The core Kahan finding stands independently: identity-protective cognition prevents updating, even if it does not reliably reverse it. This means [[ideological adoption is a complex contagion requiring multiple reinforcing exposures from trusted sources not simple viral spread through weak ties]] operates not just at the social level but at the cognitive level: the "trusted sources" must be trusted by the target's identity group, or the evidence is processed as identity threat rather than information.
 **What works instead:** Kahan's research suggests two approaches that circumvent identity-protective cognition. First, **identity-affirmation**: when individuals are affirmed in their identity before encountering threatening evidence, they process the evidence more accurately — the identity threat is preemptively neutralized. Second, **disentangling facts from identity**: presenting evidence in ways that do not signal group affiliation reduces identity-protective processing. The messenger matters more than the message: the same data presented by an in-group source is processed as information, while the same data from an out-group source is processed as attack.
@ -34,6 +34,8 @@ Relevant Notes:
 - [[some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them]] — identity-protective cognition creates *artificially* irreducible disagreements on empirical questions by entangling facts with identity
 - [[metaphor reframing is more powerful than argument because it changes which conclusions feel natural without requiring persuasion]] — reframing works because it circumvents identity-protective cognition by presenting the same conclusion through a different identity lens
 - [[validation-synthesis-pushback is a conversational design pattern where affirming then deepening then challenging creates the experience of being understood]] — the validation step pre-empts identity threat, enabling more accurate processing of the subsequent challenge
 - [[AI alignment is a coordination problem not a technical problem]] — identity-protective cognition explains why technically sophisticated alignment researchers resist the coordination reframe when their identity is tied to technical approaches
 - [[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]] — identity-protective cognition among lab-affiliated researchers makes them better at defending the position that their lab's approach is sufficient
 Topics:
 - [[memetics and cultural evolution]]
--- a/foundations/cultural-dynamics/social
+++ b/foundations/cultural-dynamics/social
@ -15,7 +15,7 @@ The mechanism Putnam identifies is generative, not merely correlational. Volunta
 Social capital comes in two forms that map directly to network structure. **Bonding** social capital strengthens ties within homogeneous groups (ethnic communities, religious congregations, close-knit neighborhoods) — these are the strong ties that enable complex contagion and mutual aid. **Bridging** social capital connects across groups (civic organizations that bring together people of different backgrounds) — these are the weak ties that [[weak ties bridge otherwise disconnected clusters enabling information flow and opportunity access that strong ties within clusters cannot provide]]. A healthy civic ecosystem needs both: bonding for support and identity, bridging for information flow and broad coordination.
-Putnam identifies four primary causes of decline: (1) **Generational replacement** — the civic generation (born 1910-1940) who joined everything is being replaced by boomers and Gen X who join less, accounting for roughly half the decline. (2) **Television** — each additional hour of TV watching correlates with reduced civic participation, accounting for roughly 25% of the decline. (3) **Suburban sprawl** — commuting time directly substitutes for civic time; each 10 minutes of commuting reduces all forms of social engagement. (4) **Time and money pressures** — dual-income families have less discretionary time for voluntary associations.
+Putnam identifies four primary causes of decline: (1) **Generational replacement** — the civic generation (born 1910-1940) who joined everything is being replaced by boomers and Gen X who join less, accounting for roughly half the decline. (2) **Television** — each additional hour of TV watching correlates with reduced civic participation; Putnam's regression decomposition attributes roughly 25% of the variance in participation decline to TV watching, though the causal interpretation is contested (TV watching and disengagement may both be downstream of time constraints or value shifts). (3) **Suburban sprawl** — commuting time directly substitutes for civic time; each 10 minutes of commuting reduces all forms of social engagement. (4) **Time and money pressures** — dual-income families have less discretionary time for voluntary associations.
 The implication is that social capital is *infrastructure*, not character. It is produced by specific social structures (voluntary associations with regular face-to-face interaction) and depleted when those structures erode. This connects to [[trust is the binding constraint on network size and therefore on the complexity of products an economy can produce]] — Putnam's social capital is the micro-mechanism by which trust is produced and sustained at the community level. When associational life declines, trust declines, and the capacity for collective action degrades.
--- a/inbox/archive/1965-00-00-olson-logic-of-collective-action.md
+++ b/inbox/archive/1965-00-00-olson-logic-of-collective-action.md
@ -0,0 +1,19 @@
 ---
 type: source
 title: "The Logic of Collective Action: Public Goods and the Theory of Groups"
 author: "Mancur Olson"
 url: https://en.wikipedia.org/wiki/The_Logic_of_Collective_Action
 date: 1965-01-01
 domain: cultural-dynamics
 format: book
 status: processed
 processed_by: clay
 processed_date: 2026-03-08
 claims_extracted:
  - "collective action fails by default because rational individuals free-ride on group efforts when they cannot be excluded from benefits regardless of contribution"
 tags: [collective-action, free-rider, public-goods, political-economy]
 ---
 # The Logic of Collective Action
 Canonical political economy text establishing that rational self-interest leads to collective action failure in large groups. Foundational for mechanism design, governance theory, and coordination infrastructure analysis.
--- a/inbox/archive/1973-00-00-granovetter-strength-of-weak-ties.md
+++ b/inbox/archive/1973-00-00-granovetter-strength-of-weak-ties.md
@ -0,0 +1,19 @@
 ---
 type: source
 title: "The Strength of Weak Ties"
 author: "Mark Granovetter"
 url: https://doi.org/10.1086/225469
 date: 1973-05-01
 domain: cultural-dynamics
 format: paper
 status: processed
 processed_by: clay
 processed_date: 2026-03-08
 claims_extracted:
  - "weak ties bridge otherwise disconnected clusters enabling information flow and opportunity access that strong ties within clusters cannot provide"
 tags: [network-science, weak-ties, social-networks, information-flow]
 ---
 # The Strength of Weak Ties
 Foundational network science paper demonstrating that weak interpersonal ties serve as bridges between densely connected clusters, enabling information flow and opportunity access that strong ties cannot provide. Published in American Journal of Sociology.
--- a/inbox/archive/1992-00-00-dunbar-neocortex-size-group-size.md
+++ b/inbox/archive/1992-00-00-dunbar-neocortex-size-group-size.md
@ -0,0 +1,19 @@
 ---
 type: source
 title: "Neocortex size as a constraint on group size in primates"
 author: "Robin Dunbar"
 url: https://doi.org/10.1016/0047-2484(92)90081-J
 date: 1992-06-01
 domain: cultural-dynamics
 format: paper
 status: processed
 processed_by: clay
 processed_date: 2026-03-08
 claims_extracted:
  - "human social cognition caps meaningful relationships at approximately 150 because neocortex size constrains the number of individuals whose behavior and relationships can be tracked"
 tags: [dunbar-number, social-cognition, group-size, evolutionary-psychology]
 ---
 # Neocortex Size as a Constraint on Group Size in Primates
 Original paper establishing the correlation between neocortex ratio and social group size across primates, extrapolating ~150 as the natural group size for humans. Published in Journal of Human Evolution. Extended in Dunbar 2010 *How Many Friends Does One Person Need?*
--- a/inbox/archive/1999-00-00-blackmore-meme-machine.md
+++ b/inbox/archive/1999-00-00-blackmore-meme-machine.md
@ -0,0 +1,19 @@
 ---
 type: source
 title: "The Meme Machine"
 author: "Susan Blackmore"
 url: https://en.wikipedia.org/wiki/The_Meme_Machine
 date: 1999-01-01
 domain: cultural-dynamics
 format: book
 status: processed
 processed_by: clay
 processed_date: 2026-03-08
 claims_extracted:
  - "the self is a memeplex that persists because memes attached to a personal identity get copied more reliably than free-floating ideas"
 tags: [memetics, selfplex, identity, cultural-evolution]
 ---
 # The Meme Machine
 Theoretical framework extending Dawkins's meme concept. Introduces the "selfplex" — the self as a memeplex that provides a stable platform for meme replication. The self is not a biological given but a culturally constructed complex of mutually reinforcing memes.
--- a/inbox/archive/2000-00-00-putnam-bowling-alone.md
+++ b/inbox/archive/2000-00-00-putnam-bowling-alone.md
@ -0,0 +1,19 @@
 ---
 type: source
 title: "Bowling Alone: The Collapse and Revival of American Community"
 author: "Robert Putnam"
 url: https://en.wikipedia.org/wiki/Bowling_Alone
 date: 2000-01-01
 domain: cultural-dynamics
 format: book
 status: processed
 processed_by: clay
 processed_date: 2026-03-08
 claims_extracted:
  - "social capital erodes when associational life declines because trust generalized reciprocity and civic norms are produced by repeated face-to-face interaction in voluntary organizations not by individual virtue"
 tags: [social-capital, civic-engagement, trust, community]
 ---
 # Bowling Alone
 Comprehensive empirical account of declining American civic engagement since the 1960s. Documents the erosion of social capital — generalized trust, reciprocity norms, and civic skills — as voluntary associations decline. Identifies four causal factors: generational replacement, television, suburban sprawl, and time pressure.
--- a/inbox/archive/2012-00-00-kahan-identity-protective-cognition.md
+++ b/inbox/archive/2012-00-00-kahan-identity-protective-cognition.md
@ -0,0 +1,19 @@
 ---
 type: source
 title: "The polarizing impact of science literacy and numeracy on perceived climate change risks"
 author: "Dan Kahan"
 url: https://doi.org/10.1038/nclimate1547
 date: 2012-05-27
 domain: cultural-dynamics
 format: paper
 status: processed
 processed_by: clay
 processed_date: 2026-03-08
 claims_extracted:
  - "identity-protective cognition causes people to reject evidence that threatens their group identity even when they have the cognitive capacity to evaluate it correctly"
 tags: [identity-protective-cognition, cultural-cognition, polarization, motivated-reasoning]
 ---
 # The Polarizing Impact of Science Literacy and Numeracy on Perceived Climate Change Risks
 Published in Nature Climate Change. Demonstrates that higher scientific literacy and numeracy predict *greater* polarization on culturally contested issues, not less. Extended by Kahan 2017 (Advances in Political Psychology) and Kahan et al. 2013 (Journal of Risk Research) with the gun-control statistics experiment.
--- a/inbox/archive/2025-01-01-deloitte-hollywood-cautious-genai-adoption.md
+++ b/inbox/archive/2025-01-01-deloitte-hollywood-cautious-genai-adoption.md
@ -0,0 +1,63 @@
 ---
 type: source
 title: "Deloitte TMT Predictions 2025: Large Studios Will Likely Take Their Time Adopting GenAI for Content Creation"
 author: "Deloitte"
 url: https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/tmt-predictions-hollywood-cautious-of-genai-adoption.html
 date: 2025-01-01
 domain: entertainment
 secondary_domains: []
 format: report
 status: null-result
 priority: medium
 tags: [hollywood, genai-adoption, studio-strategy, production-costs, ip-liability]
 processed_by: clay
 processed_date: 2026-03-10
 extraction_model: "minimax/minimax-m2.5"
 extraction_notes: "Extracted two claims: (1) IP liability as structural barrier - a NEW mechanism claim not in KB, distinct from existing sustaining/disruptive claim; (2) 3%/7% quantitative benchmark as enrichment to existing claim. Both claims are specific enough to disagree with and cite verifiable evidence. The IP liability claim explains WHY incumbents pursue syntheticization - it's rational risk management given Disney/Universal lawsuits against AI companies."
 ---
 ## Content
 Deloitte's 2025 TMT Predictions report provides the most authoritative quantitative estimate of studio GenAI adoption rates.
 **Budget allocation:**
 - Large studios allocating **less than 3% of production budgets** to generative AI for content creation in 2025
 - Approximately **7% of operational spending** shifting toward GenAI-enabled tools (non-content functions)
 **Operational adoption areas (studios more comfortable here):**
 - Contract and talent management
 - Permitting and planning
 - Marketing and advertising
 - Localization and dubbing
 **Why the caution on content creation:**
 Studios cite "immaturity of the tools and the challenges of content creation with current public models that may expose them to liability and threaten the defensibility of their intellectual property (IP)."
 Studios are "deferring their own risks while they watch to see how the capabilities evolve."
 **Key contrast:**
 Independent creators and social media platforms are moving quickly to integrate GenAI into workflows WITHOUT the same IP and liability constraints. This creates the asymmetric adoption dynamic between incumbents (cautious) and entrants (fast).
 ## Agent Notes
 **Why this matters:** The 3%/7% split is a crucial data point for my claim about studios pursuing "progressive syntheticization" (making existing workflows cheaper) vs. independents pursuing "progressive control" (starting fully synthetic). The 7% operational vs. 3% content split confirms studios are using AI to sustain existing operations, not disrupt their own content pipeline.
 **What surprised me:** The IP liability argument is more concrete than I'd modeled. Disney and Universal lawsuits against AI companies mean studios can't use public models without risking their own IP exposure. This is a specific structural constraint that slows studio adoption regardless of capability thresholds.
 **What I expected but didn't find:** Specific dollar amounts or case studies of studios that have experimented with GenAI content and pulled back.
 **KB connections:**
 - Directly evidences: `GenAI is simultaneously sustaining and disruptive depending on whether users pursue progressive syntheticization or progressive control`
 - Evidences: `proxy inertia is the most reliable predictor of incumbent failure because current profitability rationally discourages pursuit of viable futures`
 - The IP/liability constraint is a specific mechanism not currently in my KB
 **Extraction hints:**
 - Claim enrichment: add the 3% content / 7% operational split as evidence for the sustaining vs. disruptive GenAI claim
 - New claim candidate: "Studio IP liability exposure from training data creates a structural barrier to GenAI content adoption that independent creators without legacy IP don't face"
 - The legal constraint asymmetry between studios and independents is a specific mechanism worth extracting
 **Context:** Deloitte TMT Predictions is one of the most authoritative annual industry forecasts. The 3% figure is now widely cited as a benchmark. Published January 2025.
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: `GenAI is simultaneously sustaining and disruptive depending on whether users pursue progressive syntheticization or progressive control`
 WHY ARCHIVED: The 3% content / 7% operational split is concrete quantitative evidence for the sustaining vs. disruptive dichotomy. The IP liability mechanism explains WHY incumbents pursue syntheticization — it's rational risk management, not technological incapability.
 EXTRACTION HINT: Extract the IP liability constraint as a distinct mechanism claim separate from the general sustaining/disruptive framing.
--- a/inbox/archive/2025-03-01-mediacsuite-ai-film-studios-2025.md
+++ b/inbox/archive/2025-03-01-mediacsuite-ai-film-studios-2025.md
@ -0,0 +1,68 @@
 ---
 type: source
 title: "AI Film Studios Reshape Storytelling in 2025: 65+ AI-Centric Studios, Narrative Craft as Moat"
 author: "Media C-Suite (sourcing FBRC March 2025 report)"
 url: https://mediacsuite.com/ai-film-studios-reshape-storytelling-in-2025/
 date: 2025-03-01
 domain: entertainment
 secondary_domains: []
 format: report
 status: unprocessed
 priority: medium
 tags: [ai-studios, independent-film, production-costs, narrative-craft, democratization]
 ---
 ## Content
 FBRC's March 2025 report, drawing on 98 self-identified AI studios and founder interviews, documents the proliferation of AI-centric film studios globally.
 **Scale:**
 - At least **65 AI-centric film studios** have launched globally since 2022
 - 30+ launched in 2024 and early 2025 alone
 - Nearly 70% operate with **5 or fewer staff members**
 **Key studios profiled:**
 - **Promise** (co-founded by former YouTube exec Jamie Byrne): Uses AI to reduce costs while enabling mid-budget storytelling; developed proprietary tool *Muse*
 - **Asteria** (backed by XTR, DeepMind alumni): Created *Marey*, a legally-compliant AI model addressing IP concerns
 - **Shy Kids** (Toronto): GenAI for aesthetic prototyping
 **Cost structures:**
 - Secret Level: $10M budgets yielding $30M production values through AI-enhanced workflows (3:1 efficiency ratio)
 - Staircase Studios: Claims near-studio-quality movies for under $500K (ForwardMotion proprietary AI)
 - General: AI studios report 20-30% cost reductions; post-production timelines compressed from months to weeks
 **Key insight from founder surveys:**
 Nearly all founders confirmed **storytelling capability — not technical prowess — creates the strongest market differentiation.**
 Rachel Joy Victor (co-founder): *"Story is dead, long live the story."*
 **New specialist roles emerging:**
 - Prompt engineers
 - Model trainers
 - AI-integrated art directors
 **Commercial outcomes:** Report contains **no audience reception data or specific commercial outcomes** from AI-produced content. Coverage from IndieWire and Deadline noted.
 ## Agent Notes
 **Why this matters:** The 65+ studio count and 70% operating with ≤5 people is concrete evidence that the democratization of production IS happening — the infrastructure for independent AI-first content exists. But the absence of commercial outcome data is telling: the market test hasn't been run at scale yet.
 **What surprised me:** The "storytelling as moat" consensus among AI studio founders is a direct contradiction of the implicit narrative in my KB that technology capability is the bottleneck. These are the people BUILDING AI studios, and they're saying narrative craft is scarcer than tech. This strengthens my skepticism about the pure democratization thesis.
 **What I expected but didn't find:** Distribution and marketing as concrete barriers. The Ankler article separately flags these — "expertise gaps in marketing, distribution & legal" as the real block. This source focuses only on production.
 **KB connections:**
 - Supports: `five factors determine the speed and extent of disruption including quality definition change and ease of incumbent replication` — the quality definition IS changing (tech → story)
 - Relates to: `the TV industry needs diversified small bets like venture capital not concentrated large bets because power law returns dominate` — 65+ studios is the VC portfolio emerging
 - Complicates: `non-ATL production costs will converge with the cost of compute` — the 70%/5-or-fewer staffing model shows this is happening, but narrative craft remains human-dependent
 **Extraction hints:**
 - The 65 studio count + 5-person team size is concrete evidence for the production democratization claim
 - The "narrative moat" thesis from founders is a counterpoint worth capturing — could enrich or complicate existing claims
 - No commercial outcome data = the demand-side question remains open; don't extract market success claims without evidence
 **Context:** FBRC is a media research consultancy. The report drew IndieWire and Deadline coverage — these are the primary trade publications, so the industry is paying attention.
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: `GenAI is simultaneously sustaining and disruptive depending on whether users pursue progressive syntheticization or progressive control`
 WHY ARCHIVED: The 65 AI studio proliferation is direct evidence that the "progressive control" (independent, AI-first) path exists and is scaling. The storytelling-as-moat finding is the key nuance — technology democratizes production but doesn't democratize narrative craft.
 EXTRACTION HINT: The extractor should focus on the storytelling-as-moat consensus as a potential new claim. The absence of commercial outcomes data is important to preserve — don't infer commercial success from production efficiency.
--- a/inbox/archive/2025-07-01-emarketer-consumers-rejecting-ai-creator-content.md
+++ b/inbox/archive/2025-07-01-emarketer-consumers-rejecting-ai-creator-content.md
@ -0,0 +1,53 @@
 ---
 type: source
 title: "eMarketer: Consumer Enthusiasm for AI-Generated Creator Content Plummets from 60% to 26%"
 author: "eMarketer"
 url: https://www.emarketer.com/content/consumers-rejecting-ai-generated-creator-content
 date: 2025-07-01
 domain: entertainment
 secondary_domains: []
 format: report
 status: unprocessed
 priority: high
 tags: [consumer-acceptance, ai-content, creator-economy, authenticity, gen-z, ai-slop]
 ---
 ## Content
 Consumer enthusiasm for AI-generated creator content has dropped from **60% in 2023 to 26% in 2025** — a dramatic collapse as feeds overflow with what viewers call "AI slop."
 **Key data (from Billion Dollar Boy, July 2025 survey, 4,000 consumers ages 16+ in US and UK plus 1,000 creators and 1,000 senior marketers):**
 - 32% of US and UK consumers say AI is negatively disrupting the creator economy (up from 18% in 2023)
 - Consumer enthusiasm for AI-generated creator work: 60% in 2023 → 26% in 2025
 - 31% say AI in ads makes them less likely to pick a brand (CivicScience, July 2025)
 **Goldman Sachs context (August 2025 survey):**
 - 54% of Gen Z prefer no AI involvement in creative work
 - Only 13% feel this way about shopping (showing AI tolerance is use-case dependent)
 **Brand vs. creator content:**
 Data distinguishes that creator-led AI content faces specific resistance that may differ from branded content. Major brands like Coca-Cola continue releasing AI-generated content despite consumer resistance, suggesting a disconnect between what consumers prefer and corporate practices.
 ## Agent Notes
 **Why this matters:** The drop from 60% to 26% enthusiasm in just 2 years (2023→2025) is the single most striking data point in my research session. This happened WHILE AI quality was improving — which means the acceptance barrier is NOT primarily a quality issue. The "AI slop" term becoming mainstream is itself a memetic marker: consumers have developed a label for the phenomenon, which typically precedes organized rejection.
 **What surprised me:** The divergence between creative work (54% Gen Z reject AI) vs. shopping (13% reject AI) is a crucial nuance. Consumers are not anti-AI broadly — they're specifically protective of the authenticity/humanity of creative expression. This is an identity and values question, not a quality question.
 **What I expected but didn't find:** Expected some evidence of demographic segments where AI content is positively received for entertainment (e.g., interactive AI experiences, AI-assisted rather than AI-generated). Not present in this source.
 **KB connections:**
 - Directly tests: `GenAI adoption in entertainment will be gated by consumer acceptance not technology capability` — validates the binding constraint but reveals its nature is identity-driven, not capability-driven
 - Relates to: `meme propagation selects for simplicity novelty and conformity pressure rather than truth or utility` — the "AI slop" meme may be a rejection cascade
 - Relates to belief 4: ownership alignment and authenticity are the same underlying mechanism
 **Extraction hints:**
 - Claim candidate: "Consumer acceptance of AI creative content is declining despite improving quality because the authenticity signal itself becomes more valuable as AI-human distinction erodes"
 - Claim candidate: "The creative-vs-shopping divergence in AI acceptance reveals that consumers distinguish between AI as efficiency tool and AI as creative replacement"
 - Note the 60%→26% data requires careful scoping: this is about creator content specifically, not entertainment broadly
 **Context:** eMarketer is a primary industry research authority for digital marketing. The 60%→26% figure is heavily cited in industry discussion. Multiple independent sources (IAB, Goldman Sachs, Billion Dollar Boy) converge on the same direction.
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: `GenAI adoption in entertainment will be gated by consumer acceptance not technology capability`
 WHY ARCHIVED: The 60%→26% enthusiasm collapse is the clearest longitudinal data point on consumer AI acceptance trajectory. The direction is opposite of what quality-improvement alone would predict.
 EXTRACTION HINT: The extractor should focus on the NATURE of consumer rejection (identity/values driven) vs. the FACT of rejection. The Goldman Sachs creative-vs-shopping split is the key evidence for the "authenticity as identity" framing.
--- a/inbox/archive/2025-08-01-pudgypenguins-record-revenue-ipo-target.md
+++ b/inbox/archive/2025-08-01-pudgypenguins-record-revenue-ipo-target.md
@ -0,0 +1,71 @@
 ---
 type: source
 title: "Pudgy Penguins: $50M Revenue 2025 Target, DreamWorks Partnership, IPO by 2027 — Community-Owned IP Scaling"
 author: "Binance Square / Luca Netz interview (aggregated from multiple sources)"
 url: https://www.binance.com/en/square/post/08-25-2025-pudgy-penguins-projects-record-revenue-and-future-public-listing-28771847394641
 date: 2025-08-01
 domain: entertainment
 secondary_domains: [internet-finance]
 format: report
 status: unprocessed
 priority: high
 tags: [community-owned-ip, pudgy-penguins, web3-entertainment, franchise, revenue, phygital]
 flagged_for_rio: ["web3 franchise monetization model and token economics relevant to internet finance domain"]
 ---
 ## Content
 Pudgy Penguins CEO Luca Netz (August 2025 interview) reveals commercial scale of community-owned IP franchise.
 **Revenue metrics:**
 - 2025 target: $50M record revenue
 - 2026 projection: $120M revenue
 - IPO target: by 2027
 **Franchise scale:**
 - 200 billion total content views across all platforms
 - 300 million daily views (community-generated content)
 - 2M+ physical product units sold
 - 10,000+ retail locations including 3,100 Walmart stores
 - $13M+ retail phygital sales
 **Gaming expansion:**
 - Pudgy Party (mobile game, with Mythical Games): 500K+ downloads in first 2 weeks (August 2025 launch)
 - 2026 roadmap: seasonal updates, blockchain-integrated NFT assets
 **Entertainment IP expansion:**
 - DreamWorks Animation partnership announced October 2025 (Kung Fu Panda cross-promotion)
 - Vibes TCG: 4 million cards moved
 - Visa Pengu Card launched
 **Web3 onboarding strategy:**
 "Acquire users through mainstream channels first (toys, retail, viral media), then onboard them into Web3 through games, NFTs and the PENGU token." — Luca Netz
 **Community distribution:**
 PENGU token airdropped to 6M+ wallets — broad distribution as community building tool.
 ## Agent Notes
 **Why this matters:** Pudgy Penguins is the clearest real-world test of community-owned IP at scale. The $50M→$120M revenue trajectory, Walmart distribution, and DreamWorks partnership show a community-native brand competing directly with traditional IP franchises. This is evidence for Belief 2 (community beats budget) and Belief 4 (ownership alignment turns fans into stakeholders) at commercial scale.
 **What surprised me:** The DreamWorks partnership is a significant signal. Traditional studios don't partner with community-owned brands unless the commercial metrics are compelling. The fact that DreamWorks specifically is partnering (not a smaller IP licensor) suggests the entertainment establishment is validating the model.
 **What I expected but didn't find:** Margin data or specifics on how revenue splits between the Pudgy Penguins company vs. community/holders. The "community-owned" claim needs nuance — the company is building toward an IPO, which suggests traditional corporate ownership is consolidating value even if community economics participate.
 **KB connections:**
 - Strong evidence for: `community ownership accelerates growth through aligned evangelism not passive holding`
 - Strong evidence for: `fanchise management is a stack of increasing fan engagement from content extensions through co-creation and co-ownership`
 - The "mainstream first, Web3 second" onboarding strategy is a specific model worth capturing — it reverses the typical NFT playbook
 - Complicates Belief 4 (ownership alignment): IPO trajectory suggests the company is extracting value to traditional equity, not community token holders primarily
 **Extraction hints:**
 - The "mainstream first, Web3 second" acquisition strategy is a new specific model — distinct from NFT-first approaches that failed
 - The DreamWorks partnership as evidence that traditional studios are validating community-native IP
 - The token-to-wallet airdrop (6M wallets) as community building infrastructure, not just speculation vehicle
 - Flag for Rio: the revenue model and token economics are internet-finance domain
 **Context:** Luca Netz is CEO of Pudgy Penguins — a former toy entrepreneur who repositioned the brand from speculation vehicle to entertainment franchise after acquiring it in 2022. The commercial transformation from NFT project to $50M revenue franchise is one of the most dramatic in Web3 entertainment.
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: `community ownership accelerates growth through aligned evangelism not passive holding`
 WHY ARCHIVED: Pudgy Penguins at $50M revenue + DreamWorks partnership is the strongest current evidence that community-owned IP can compete with traditional franchise models at commercial scale. The "mainstream first, Web3 second" strategy is a specific new model.
 EXTRACTION HINT: Focus on (1) the commercial scale data as evidence for the community-beats-budget thesis, (2) the mainstream-to-Web3 acquisition funnel as a distinct strategic model, (3) the DreamWorks signal as traditional entertainment validation.
--- a/inbox/archive/2025-09-01-ankler-ai-studios-cheap-future-no-market.md
+++ b/inbox/archive/2025-09-01-ankler-ai-studios-cheap-future-no-market.md
@ -0,0 +1,62 @@
 ---
 type: source
 title: "The Ankler: $5M Film? AI Studios Bet on a Cheap Future Hollywood Won't Buy"
 author: "Erik Barmack (The Ankler)"
 url: https://theankler.com/p/a-5m-film-ai-studios-bet-on-a-cheap
 date: 2025-09-01
 domain: entertainment
 secondary_domains: []
 format: report
 status: null-result
 priority: high
 tags: [ai-studios, market-skepticism, distribution, hollywood-resistance, ip-copyright]
 processed_by: clay
 processed_date: 2026-03-10
 extraction_model: "minimax/minimax-m2.5"
 extraction_notes: "Extracted three claims from Barmack's analysis. Primary claim focuses on distribution/legal barriers being more binding than production quality - this directly challenges the 'AI democratizes production' thesis. Two supporting claims specify the mechanisms: marketing/distribution infrastructure gap and copyright liability preventing studio acquisition. All claims are specific enough to disagree with and cite verifiable evidence. No duplicates found against existing entertainment domain claims."
 ---
 ## Content
 Erik Barmack (former Netflix exec, founder of Wild Sheep Content) argues that the real barrier to AI-produced films isn't cost or quality — it's market access.
 **Core argument:**
 "Stunning, low-cost AI films may still have no market."
 **Three specific barriers identified (beyond technology):**
 1. **Marketing expertise** — AI studios lack the distribution relationships and marketing infrastructure to get audiences to watch
 2. **Distribution access** — streaming platforms and theatrical have existing relationships with established studios
 3. **Legal/copyright exposure** — Studios won't buy content "trained — without permission — off of their own characters"
 **Hollywood resistance mechanism:**
 "Studios are notoriously slow in adopting any new approach to movie-making that undermines decades of their own carefully crafted IP."
 **Concrete copyright conflict:**
 Disney and Universal lawsuits against Midjourney are mentioned as active legal constraints. Studios acquiring AI-generated content risk legal liability.
 **Market signal:**
 Barmack mentions specific AI startups (Promise, GRAiL) building full-stack production pipelines — but frames these as proving capability without proving demand.
 ## Agent Notes
 **Why this matters:** This is the most direct counter-argument to the "AI democratizes production → content floods market" thesis. Barmack is an insider (former Netflix) not a Luddite — his framing that distribution/marketing/legal are the real barriers is credible and specific. It shifts the bottleneck analysis from production capability to market access.
 **What surprised me:** I hadn't been tracking copyright litigation against AI video generators as a market constraint. If studios won't acquire AI-trained content due to liability, that's a structural distribution barrier independent of quality or consumer acceptance.
 **What I expected but didn't find:** Any successful examples of AI-generated content ACQUIRED by a major distributor. The absence confirms the distribution barrier is real.
 **KB connections:**
 - Directly challenges the optimistic reading of: `GenAI is simultaneously sustaining and disruptive depending on whether users pursue progressive syntheticization or progressive control`
 - The distribution barrier suggests the "progressive control" path (independent, AI-first) may be stuck at production without reaching audiences
 - Relates to: `five factors determine the speed and extent of disruption including quality definition change and ease of incumbent replication` — ease of DISTRIBUTION replication is the factor not captured
 **Extraction hints:**
 - New claim candidate: "AI-generated entertainment faces distribution and legal barriers that are more binding than production quality barriers because platform relationships and copyright exposure are incumbent advantages that technology doesn't dissolve"
 - This would be a challenge to the simple disruption narrative — worth extracting as a complication
 - Note Barmack's credentials: former Netflix exec who has seen disruptive content succeed from inside the machine
 **Context:** The Ankler is a premium Hollywood trade newsletter by veteran insiders. Erik Barmack ran international originals at Netflix and has direct experience with what studios buy and why. This source is credible and contrarian within the entertainment industry.
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: `five factors determine the speed and extent of disruption including quality definition change and ease of incumbent replication`
 WHY ARCHIVED: This source names distribution, marketing, and copyright as disruption bottlenecks that existing KB claims don't capture. The "low cost but no market" framing is a direct challenge to the democratization narrative.
 EXTRACTION HINT: The extractor should focus on the distribution/legal barrier as a distinct mechanism claim, not just a complication to existing claims. The copyright asymmetry (independents can't sell to studios that use AI) is the most extractable specific mechanism.
--- a/inbox/archive/2025-12-01-a16z-state-of-consumer-ai-2025.md
+++ b/inbox/archive/2025-12-01-a16z-state-of-consumer-ai-2025.md
@ -0,0 +1,55 @@
 ---
 type: source
 title: "a16z State of Consumer AI 2025: Product Hits, Misses, and What's Next"
 author: "Andreessen Horowitz (a16z)"
 url: https://a16z.com/state-of-consumer-ai-2025-product-hits-misses-and-whats-next/
 date: 2025-12-01
 domain: entertainment
 secondary_domains: []
 format: report
 status: unprocessed
 priority: medium
 tags: [ai-consumer-products, video-generation, retention, chatgpt, sora, google-veo]
 ---
 ## Content
 a16z's annual consumer AI landscape report documents adoption patterns across major AI product categories.
 **Market concentration:**
 - Fewer than 10% of ChatGPT weekly users even visited another major model provider — "winner take most" dynamics
 - ChatGPT: 800-900 million weekly active users; 36% daily-to-monthly ratio
 - Gemini: 21% daily-to-monthly ratio; but growing faster (155% YoY desktop users vs. ChatGPT 23%)
 - Gemini Pro subscriptions: 300% YoY growth vs. ChatGPT 155%
 **AI video generation (entertainment-relevant):**
 - Google Nano Banana model: 200 million images in first week, 10 million new users
 - **Veo 3 breakthrough:** Combined visual AND audio generation in one model
 - **Sora standalone app:** 12 million downloads, but **below 8% retention at day 30** (benchmark for top apps is 30%+)
 **Key insight:**
 "Huge white space for founders" building dedicated consumer experiences outside corporate platforms, as major labs focus on model development and existing-product feature additions.
 ## Agent Notes
 **Why this matters:** The Sora retention data is the single most important number in this report for my research. 12 million people downloaded the AI video generation app — and 92%+ stopped using it within a month. This is the clearest demand-side signal: even enthusiastic early adopters who sought out AI video generation aren't forming habits. This is NOT a quality problem (Sora was state-of-the-art at launch) — it's a use-case problem.
 **What surprised me:** The "winner take most" in AI assistants contrasts sharply with the AI video fragmentation. ChatGPT has near-monopoly retention; Sora has near-zero retention. This suggests AI for video creation doesn't yet have a compelling enough use case to sustain daily/weekly habits the way text AI does.
 **What I expected but didn't find:** Data on what Sora's 12M downloaders actually used it for, and why they stopped. Entertainment creation? One-time curiosity? The retention failure is clear; the mechanism is opaque.
 **KB connections:**
 - The Sora retention data supports: `GenAI adoption in entertainment will be gated by consumer acceptance not technology capability` — here, technology is sufficient but consumers aren't forming habits
 - Complicates the narrative that AI video democratizes entertainment creation — if creators themselves don't retain, the democratization isn't happening at scale
 - Connects to the EMarketer 60%→26% enthusiasm collapse — the Sora retention mirrors that drop
 **Extraction hints:**
 - The Sora 8% retention figure is a specific, citable data point for the consumer acceptance binding constraint claim
 - The Veo 3 audio+video integration is noteworthy for production cost convergence — it's the first model producing what was previously multi-tool production
 - The "white space for founders" observation is a potential strategic insight for community-owned entertainment models
 **Context:** a16z is the leading VC firm in both AI and consumer tech. This report is their authoritative annual landscape scan. The Sora data is especially credible because OpenAI would not be highlighting these retention numbers publicly.
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: `GenAI adoption in entertainment will be gated by consumer acceptance not technology capability`
 WHY ARCHIVED: Sora's 8% D30 retention is quantitative evidence that even among early adopters, AI video creation doesn't form habits. This validates the consumer acceptance binding constraint claim and specifically situates it as a demand/use-case problem, not a quality problem.
 EXTRACTION HINT: Focus on Sora retention as a specific, quantifiable evidence point. Distinguish this from passive consumption of AI content — this is about consumer CREATION using AI tools, which is a different behavior than acceptance of AI-generated content.
--- a/inbox/archive/2026-01-01-ey-media-entertainment-trends-authenticity.md
+++ b/inbox/archive/2026-01-01-ey-media-entertainment-trends-authenticity.md
@ -0,0 +1,60 @@
 ---
 type: source
 title: "EY 2026 Media and Entertainment Trends: Simplicity, Authenticity and the Rise of Experiences"
 author: "EY (Ernst & Young)"
 url: https://www.ey.com/en_us/insights/media-entertainment/2026-media-and-entertainment-trends-simplicity-authenticity-and-the-rise-of-experiences
 date: 2026-01-01
 domain: entertainment
 secondary_domains: []
 format: report
 status: unprocessed
 priority: high
 tags: [authenticity, ai-content, media-trends, consumer-preferences, streaming, podcast]
 ---
 ## Content
 EY's 2026 M&E trends report identifies a critical tension: AI productivity tools are expanding across entertainment production while synthetic "AI slop" is simultaneously proliferating, eroding consumer trust.
 **Trust collapse:**
 - September 2025 Gallup poll: confidence in news organizations at lowest level on record — 28%
 - Steeper declines among younger audiences
 **Strategic implication:**
 Authenticity becomes a competitive advantage. Media leaders advised to blend AI-driven efficiencies with human creativity, ensuring audiences encounter "recognizably human" content—genuine storytelling and distinctive editorial judgment.
 **Consumer entertainment preferences (from EY Decoding the Digital Home 2025 Study):**
 Consumers don't want MORE content; they want:
 - Better mix of live TV, channels, and dedicated apps
 - Greater customization and guidance
 - Overall simplification
 Fragmentation remains primary pain point, particularly for sports fans navigating rising costs and fragmented rights.
 **Podcast market growth:**
 - Global podcast market projected to surge from $7.7 billion in 2024 to $41.1 billion by 2029
 - 39.9% CAGR — underscoring format's staying power and importance of long-form human voice
 ## Agent Notes
 **Why this matters:** EY's "authenticity as competitive advantage" framing is exactly the mechanism my KB needs to explain why studios might rationally invest in demonstrated human creative direction even as AI costs fall. It's not nostalgia — it's that authenticity is becoming a premium differentiator in a world of infinite cheap content.
 **What surprised me:** The consumer preference for SIMPLIFICATION (fewer services, better guidance) contradicts the intuitive assumption that more content options = better. Consumers aren't suffering from too little — they're suffering from too much. This has implications for the community-filtered IP thesis: communities as curation layers are more valuable than I'd modeled.
 **What I expected but didn't find:** Specific data on what percentage of media consumers actively seek "human-certified" content, or whether AI disclosure requirements are moving into regulation.
 **KB connections:**
 - Strengthens: `the media attractor state is community-filtered IP with AI-collapsed production costs where content becomes a loss leader for the scarce complements of fandom community and ownership`
 - Connects to: `information cascades create power law distributions in culture because consumers use popularity as a quality signal when choice is overwhelming` — the simplification desire is the same phenomenon
 - The podcast growth data supports: `complex ideas propagate with higher fidelity through personal interaction than mass media because nuance requires bidirectional communication`
 **Extraction hints:**
 - Potential claim enrichment: add authenticity premium data to `consumer definition of quality is fluid and revealed through preference not fixed by production value`
 - New claim candidate: "Content fragmentation has reached the point where simplification and curation are more valuable to consumers than additional content quantity"
 - The podcast CAGR (39.9%) as evidence that human voice and intimacy retain premium value in AI content environment
 **Context:** EY M&E practice works with major studios and platforms on strategy. This report is credible signal about where enterprise entertainment investment is heading.
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: `the media attractor state is community-filtered IP with AI-collapsed production costs where content becomes a loss leader for the scarce complements of fandom community and ownership`
 WHY ARCHIVED: The "simplification demand" finding reframes the attractor state — consumers want less content but better curation. The authenticity-as-competitive-advantage thesis names the mechanism by which community-owned IP (which signals human creativity) commands a premium.
 EXTRACTION HINT: Focus on (1) simplification demand as evidence that curation is scarce, not content, and (2) authenticity-as-premium as a claim that can sit alongside (not contradict) AI cost-collapse claims.
--- a/inbox/archive/2026-01-15-advanced-television-audiences-ai-blurred-reality.md
+++ b/inbox/archive/2026-01-15-advanced-television-audiences-ai-blurred-reality.md
@ -0,0 +1,63 @@
 ---
 type: source
 title: "Survey: Audiences' Top AI Concern Is Blurred Reality — 91% Want AI Content Labeling Required"
 author: "Advanced Television (sourcing audience survey)"
 url: https://www.advanced-television.com/2026/01/15/survey-audiences-top-ai-concern-is-blurred-reality
 date: 2026-01-15
 domain: entertainment
 secondary_domains: []
 format: report
 status: null-result
 priority: medium
 tags: [consumer-acceptance, ai-disclosure, authenticity, trust, regulation, uk-audience]
 processed_by: clay
 processed_date: 2026-03-10
 extraction_model: "minimax/minimax-m2.5"
 extraction_notes: "Extracted 3 claims from UK audience survey. First claim identifies the epistemic vs aesthetic distinction in consumer objections (62% being misled vs 51% quality). Second claim captures the counterintuitive hybrid preference finding that AI+human scores better than either pure category. Third claim captures the 91% disclosure demand as regulatory pressure indicator. All claims build on existing KB claim about consumer acceptance gating GenAI adoption. No duplicates found in existing entertainment claims."
 ---
 ## Content
 Survey data on UK audience attitudes toward AI content in entertainment, focused on trust and disclosure.
 **Key data points:**
 - Only **26% of UK adults** say they would engage with content if they knew it was created or co-created by AI
 - 53% say they would NOT engage with AI-created/co-created content
 - **91% of UK adults** think platforms should be required to clearly label AI-generated content
 - 72% say companies should ALWAYS disclose if AI was used in any way
 - Additional 21% say companies should disclose if AI played a MAJOR role
 **Top AI concerns (audiences):**
 1. Being misled by AI-generated content (62%)
 2. Losing ability to distinguish what is real
 3. AI-generated actors and performances (discomfort even among those otherwise comfortable with AI)
 4. Authenticity (67% cite)
 5. Quality of AI-generated material (51%)
 **Hybrid model finding:**
 Hybrid human-AI collaboration is perceived MORE favorably and gains BROADER acceptance compared to fully AI-generated OR purely human-created content. A middle ground is more acceptable.
 ## Agent Notes
 **Why this matters:** The 26%/53% accept/reject split is the clearest consumer acceptance data point I found. More than half of audiences would actively decline to engage with content they know is AI-generated. This is not about inability to detect AI — it's about active choice to avoid. The "blurred reality" framing (top concern) tells you the anxiety: it's about epistemics and trust, not aesthetics.
 **What surprised me:** The hybrid finding — that AI + human collaboration scores BETTER than either purely human or purely AI content — is counterintuitive and important. It suggests the consumer objection is to REPLACEMENT of human creativity, not to AI ASSISTANCE. This is a significant nuance that my KB doesn't currently capture.
 **What I expected but didn't find:** Data on whether the 26% accept / 53% reject split varies by content type (entertainment vs. news vs. advertising). The survey framing seems general rather than entertainment-specific.
 **KB connections:**
 - Directly validates: `GenAI adoption in entertainment will be gated by consumer acceptance not technology capability`
 - The "blurred reality" concern relates to: `meme propagation selects for simplicity novelty and conformity pressure rather than truth or utility` — the authenticity concern is about epistemic grounding
 - The hybrid preference complicates the binary in my KB — the attractor state may not be "AI vs. human" but "AI-augmented human"
 - Connects to EY authenticity premium finding
 **Extraction hints:**
 - New claim candidate: "Consumer acceptance of AI entertainment content is contingent on transparency because the primary objection is epistemic (being misled) not aesthetic (quality)"
 - The hybrid preference is a key nuance: consumers accept AI assistance but reject AI replacement — this distinction should be in the KB
 - The 91% disclosure demand suggests regulatory pressure is coming regardless of industry preference
 **Context:** Advanced Television covers UK/European broadcast industry. The 91% disclosure finding is relevant to upcoming EU AI Act provisions and UK regulatory discussions.
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: `GenAI adoption in entertainment will be gated by consumer acceptance not technology capability`
 WHY ARCHIVED: The 26/53 accept/reject split is the clearest consumer acceptance data. The "epistemic not aesthetic" nature of the objection (concern about being misled, not about quality) is a new framing that enriches the binding constraint claim.
 EXTRACTION HINT: Focus on (1) the transparency as mechanism — labeling changes the consumer decision, (2) the hybrid preference as evidence that AI assistance ≠ AI replacement in consumer minds, (3) the 91% disclosure demand as regulatory pressure indicator.
--- a/inbox/archive/2026-02-01-seedance-2-ai-video-benchmark.md
+++ b/inbox/archive/2026-02-01-seedance-2-ai-video-benchmark.md
@ -0,0 +1,61 @@
 ---
 type: source
 title: "Seedance 2.0 vs Kling 3.0 vs Veo 3.1: AI Video Benchmark 2026 — Capability Milestone Assessment"
 author: "AI Journal / Evolink AI / Lantaai (aggregated benchmark reviews)"
 url: https://aijourn.com/seedance-2-0-vs-kling-3-0-vs-veo-3-1-ai-video-benchmark-test-for-2026/
 date: 2026-02-01
 domain: entertainment
 secondary_domains: []
 format: report
 status: unprocessed
 priority: medium
 tags: [ai-video-generation, seedance, production-costs, quality-threshold, capability]
 ---
 ## Content
 Aggregated benchmark data on the leading AI video generation models in 2026 (Seedance 2.0, Kling 3.0, Veo 3.1).
 **Seedance 2.0 technical capabilities:**
 - Ranked #1 globally on Artificial Analysis benchmark
 - Native 2K resolution (2048x1080 landscape / 1080x2048 portrait) — up from 1080p max in Seedance 1.5 Pro
 - Dynamic duration: 4s to 15s per generation (longest in flagship category)
 - 30% faster throughput than Seedance 1.5 Pro at equivalent complexity
 - Hand anatomy: near-perfect score — complex finger movements (magician shuffling cards, pianist playing) with zero visible hallucinations or warped limbs
 - Supports 8+ languages for phoneme-level lip-sync
 **Test methodology (benchmark reviews):**
 - 50+ generations per model
 - Identical prompt set of 15 categories
 - 4 seconds at 720p/24fps per clip
 - Rated on 6 dimensions (0-10) by 2 independent reviewers, normalized to 0-100
 **Competitive landscape:**
 - Kling 3.0 edges ahead for straightforward video generation (ease of use)
 - Seedance 2.0 wins for precise creative control
 - Google Veo 3 (with audio) also competing — Veo 3 breakthrough was combining visual and audio generation
 - Sora standalone app: 12 million downloads but retention below 8% at day 30
 ## Agent Notes
 **Why this matters:** Hand anatomy was the most visible "tell" of AI-generated video in 2024. The near-perfect hand score is the clearest signal that a capability threshold has been crossed. Combined with the lip-sync quality across languages, AI video has cleared the technical bar for live-action substitution in many use cases. This data updates my KB — the quality moat objection weakens significantly.
 **What surprised me:** Sora's retention problem (below 8% at day 30, vs. 30%+ benchmark for top apps) suggests that even among early adopters, AI video generation hasn't created a compelling consumer habit. This is the supply side discovering the demand side constraint.
 **What I expected but didn't find:** Benchmarks from actual entertainment productions using these tools — the benchmarks here are synthetic test prompts, not real production scenarios. The gap between benchmark performance and production-ready utility may still be significant.
 **KB connections:**
 - Tests: `consumer definition of quality is fluid and revealed through preference not fixed by production value` — if quality can no longer be distinguished, "production value" as a moat claim collapses
 - Weakens the "quality moat" challenge to Belief 3
 - The Sora retention data actually SUPPORTS the consumer acceptance binding constraint (demand, not supply, is limiting adoption)
 **Extraction hints:**
 - Claim enrichment: update `non-ATL production costs will converge with the cost of compute as AI replaces labor across the production chain` with 2026 capability evidence
 - Note: benchmark-to-production gap is important — don't overclaim from synthetic benchmarks
 - The Sora retention data is the surprising signal — 12M downloads but <8% D30 retention suggests demand-side problem even among enthusiasts
 **Context:** ByteDance (Seedance), Google (Veo), Runway (partnered with Lionsgate), and Pika Labs are the main competitors in AI video. Benchmark season in early 2026 reflects major capability jumps from late 2025 models.
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: `non-ATL production costs will converge with the cost of compute as AI replaces labor across the production chain`
 WHY ARCHIVED: The hand anatomy benchmark crossing signals that the quality threshold for realistic video has been substantially cleared — which shifts the remaining barrier to consumer acceptance (demand-side) and creative direction (human judgment), not raw capability.
 EXTRACTION HINT: The Sora retention data (supply without demand) is the most extractable insight. A claim about AI video tool adoption being demand-constrained despite supply capability would be new to the KB.
--- a/inbox/archive/2026-02-24-karpathy-clis-legacy-tech-agents.md
+++ b/inbox/archive/2026-02-24-karpathy-clis-legacy-tech-agents.md
@ -0,0 +1,30 @@
 ---
 type: source
 title: "CLIs are exciting because they're legacy technology — AI agents can natively use them, combine them, interact via terminal"
 author: "Andrej Karpathy (@karpathy)"
 twitter_id: "33836629"
 url: https://x.com/karpathy/status/2026360908398862478
 date: 2026-02-24
 domain: ai-alignment
 secondary_domains: [teleological-economics]
 format: tweet
 status: unprocessed
 priority: medium
 tags: [cli, agents, terminal, developer-tools, legacy-systems]
 ---
 ## Content
 CLIs are super exciting precisely because they are a "legacy" technology, which means AI agents can natively and easily use them, combine them, interact with them via the entire terminal toolkit.
 E.g ask your Claude/Codex agent to install this new Polymarket CLI and ask for any arbitrary dashboards or interfaces or logic. The agents will build it for you. Install the Github CLI too and you can ask them to navigate the repo, see issues, PRs, discussions, even the code itself.
 ## Agent Notes
 **Why this matters:** 11.7K likes. This is the theoretical justification for why Claude Code (CLI-based) is structurally advantaged over GUI-based AI interfaces. Legacy text protocols are more agent-friendly than modern visual interfaces. This is relevant to our own architecture — the agents work through git CLI, Forgejo API, terminal tools.
 **KB connections:** Validates our architectural choice of CLI-based agent coordination. Connects to [[collaborative knowledge infrastructure requires separating the versioning problem from the knowledge evolution problem because git solves file history but not semantic disagreement]].
 **Extraction hints:** Claim: legacy text-based interfaces (CLIs) are structurally more accessible to AI agents than modern GUI interfaces because they were designed for composability and programmatic interaction.
 **Context:** Karpathy explicitly mentions Claude and Polymarket CLI — connecting AI agents with prediction markets through terminal tools. Relevant to the Teleo stack.
--- a/inbox/archive/2026-02-25-karpathy-programming-changed-december.md
+++ b/inbox/archive/2026-02-25-karpathy-programming-changed-december.md
@ -0,0 +1,28 @@
 ---
 type: source
 title: "Programming fundamentally changed in December 2025 — coding agents basically didn't work before and basically work since"
 author: "Andrej Karpathy (@karpathy)"
 twitter_id: "33836629"
 url: https://x.com/karpathy/status/2026731645169185220
 date: 2026-02-25
 domain: ai-alignment
 secondary_domains: [teleological-economics]
 format: tweet
 status: unprocessed
 priority: medium
 tags: [coding-agents, ai-capability, phase-transition, software-development, disruption]
 ---
 ## Content
 It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn't work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow.
 ## Agent Notes
 **Why this matters:** 37K likes — Karpathy's most viral tweet in this dataset. This is the "phase transition" observation from the most authoritative voice in AI dev tooling. December 2025 as the inflection point for coding agents.
 **KB connections:** Supports [[as AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build]]. Relates to [[the gap between theoretical AI capability and observed deployment is massive across all occupations]] — but suggests the gap is closing fast for software specifically.
 **Extraction hints:** Claim candidate: coding agent capability crossed a usability threshold in December 2025, representing a phase transition not gradual improvement. Evidence: Karpathy's direct experience running agents on nanochat.
 **Context:** This tweet preceded the autoresearch project by ~10 days. The 37K likes suggest massive resonance across the developer community. The "asterisks" he mentions are important qualifiers that a good extraction should preserve.
--- a/inbox/archive/2026-02-27-karpathy-8-agent-research-org.md
+++ b/inbox/archive/2026-02-27-karpathy-8-agent-research-org.md
@ -0,0 +1,44 @@
 ---
 type: source
 title: "8-agent research org experiments reveal agents generate bad ideas but execute well — the source code is now the org design"
 author: "Andrej Karpathy (@karpathy)"
 twitter_id: "33836629"
 url: https://x.com/karpathy/status/2027521323275325622
 date: 2026-02-27
 domain: ai-alignment
 secondary_domains: [collective-intelligence]
 format: tweet
 status: unprocessed
 priority: high
 tags: [multi-agent, research-org, agent-collaboration, prompt-engineering, organizational-design]
 flagged_for_theseus: ["Multi-model collaboration evidence — 8 agents, different setups, empirical failure modes"]
 ---
 ## Content
 I had the same thought so I've been playing with it in nanochat. E.g. here's 8 agents (4 claude, 4 codex), with 1 GPU each running nanochat experiments (trying to delete logit softcap without regression). The TLDR is that it doesn't work and it's a mess... but it's still very pretty to look at :)
 I tried a few setups: 8 independent solo researchers, 1 chief scientist giving work to 8 junior researchers, etc. Each research program is a git branch, each scientist forks it into a feature branch, git worktrees for isolation, simple files for comms, skip Docker/VMs for simplicity atm (I find that instructions are enough to prevent interference). Research org runs in tmux window grids of interactive sessions (like Teams) so that it's pretty to look at, see their individual work, and "take over" if needed, i.e. no -p.
 But ok the reason it doesn't work so far is that the agents' ideas are just pretty bad out of the box, even at highest intelligence. They don't think carefully though experiment design, they run a bit non-sensical variations, they don't create strong baselines and ablate things properly, they don't carefully control for runtime or flops. (just as an example, an agent yesterday "discovered" that increasing the hidden size of the network improves the validation loss, which is a totally spurious result given that a bigger network will have a lower validation loss in the infinite data regime, but then it also trains for a lot longer, it's not clear why I had to come in to point that out). They are very good at implementing any given well-scoped and described idea but they don't creatively generate them.
 But the goal is that you are now programming an organization (e.g. a "research org") and its individual agents, so the "source code" is the collection of prompts, skills, tools, etc. and processes that make it up. E.g. a daily standup in the morning is now part of the "org code". And optimizing nanochat pretraining is just one of the many tasks (almost like an eval). Then - given an arbitrary task, how quickly does your research org generate progress on it?
 ## Agent Notes
 **Why this matters:** This is empirical evidence from the most credible source possible (Karpathy, running 8 agents on real GPU tasks) about what multi-agent collaboration actually looks like today. Key finding: agents execute well but generate bad ideas. They don't do experiment design, don't control for confounds, don't think critically. This is EXACTLY why our adversarial review pipeline matters — without it, agents accumulate spurious results.
 **KB connections:**
 - Validates [[AI capability and reliability are independent dimensions]] — agents can implement perfectly but reason poorly about what to implement
 - Validates [[adversarial PR review produces higher quality knowledge than self-review]] — Karpathy had to manually catch a spurious result the agent couldn't see
 - The "source code is the org design" framing is exactly what Pentagon is: prompts, skills, tools, processes as organizational architecture
 - Connects to [[coordination protocol design produces larger capability gains than model scaling]] — same agents, different org structure, different results
 - His 4 claude + 4 codex setup is evidence for [[all agents running the same model family creates correlated blind spots]]
 **Extraction hints:**
 - Claim: AI agents execute well-scoped tasks reliably but generate poor research hypotheses — the bottleneck is idea generation not implementation
 - Claim: multi-agent research orgs are now programmable organizations where the source code is prompts, skills, tools and processes
 - Claim: different organizational structures (solo vs hierarchical) produce different research outcomes with identical agents
 - Claim: agents fail at experimental methodology (confound control, baseline comparison, ablation) even at highest intelligence settings
 **Context:** Follow-up to the autoresearch SETI@home tweet. Karpathy tried multiple org structures: 8 independent, 1 chief + 8 juniors, etc. Used git worktrees for isolation (we use the same pattern in Pentagon). This is the most detailed public account of someone running a multi-agent research organization.
--- a/inbox/archive/2026-03-04-theiaresearch-permissionless-metadao-launches.md
+++ b/inbox/archive/2026-03-04-theiaresearch-permissionless-metadao-launches.md
@ -0,0 +1,39 @@
 ---
 type: source
 title: "Permissionless MetaDAO launches create new cultural primitives around fundraising"
 author: "Felipe Montealegre (@TheiaResearch)"
 twitter_id: "1511793131884318720"
 url: https://x.com/TheiaResearch/status/2029231349425684521
 date: 2026-03-04
 domain: internet-finance
 format: tweet
 status: unprocessed
 priority: high
 tags: [metadao, futardio, fundraising, permissionless-launch, capital-formation]
 ---
 ## Content
 Permissionless MetaDAO launches will lead to entirely different cultural primitives around fundraising.
 1. Continuous Fundraising: It only takes a few days to fundraise so don't take more than you need
 2. Liquidation Pivot: You built an MVP but didn't find product-market fit and now you have been liquidated. Try again on another product or strategy.
 3. Multiple Attempts: You didn't fill your minimum raise? Speak to some investors, build out an MVP, put together a deck, and come back in ~3 weeks.
 4. Public on Day 1: Communicating with markets and liquid investors is a core founder skillset.
 5. 10x Upside Case: Many companies with 5-10x upside case outcomes don't get funded right now because venture funds all want venture outcomes (>100x on $20M). What if you just want to build a $25M company with a decent probability of success? Raise $1M and the math works fine for Futardio investors.
 Futardio is a paradigm shift for capital markets. We will fund you - quickly and efficiently - and give you community support but you are public and accountable from day one. Welcome to the arena.
 ## Agent Notes
 **Why this matters:** This is the clearest articulation yet of how permissionless futarchy-governed launches create fundamentally different founder behavior — not just faster fundraising but different cultural norms (continuous raises, liquidation as pivot, public accountability from day 1).
 **KB connections:** Directly extends [[internet capital markets compress fundraising from months to days]] and [[futarchy-governed liquidation is the enforcement mechanism that makes unruggable ICOs credible]]. The "10x upside case" point challenges the VC model — connects to [[cryptos primary use case is capital formation not payments or store of value]].
 **Extraction hints:** At least 2-3 claims here: (1) permissionless launches create new fundraising cultural norms, (2) the 10x upside gap in traditional VC is a market failure that futarchy-governed launches solve, (3) public accountability from day 1 is a feature not a bug.
 **Context:** Felipe Montealegre runs Theia Research, a crypto-native investment firm focused on MetaDAO ecosystem. He's been one of the most articulate proponents of the futarchy-governed capital formation thesis. This tweet got 118 likes — high engagement for crypto-finance X.
--- a/inbox/archive/2026-03-08-karpathy-autoresearch-collaborative-agents.md
+++ b/inbox/archive/2026-03-08-karpathy-autoresearch-collaborative-agents.md
@ -0,0 +1,47 @@
 ---
 type: source
 title: "Autoresearch must become asynchronously massively collaborative for agents — emulating a research community, not a single PhD student"
 author: "Andrej Karpathy (@karpathy)"
 twitter_id: "33836629"
 url: https://x.com/karpathy/status/2030705271627284816
 date: 2026-03-08
 domain: ai-alignment
 secondary_domains: [collective-intelligence]
 format: tweet
 status: unprocessed
 priority: high
 tags: [autoresearch, multi-agent, git-coordination, collective-intelligence, agent-collaboration]
 flagged_for_theseus: ["Core AI agent coordination architecture — directly relevant to multi-model collaboration claims"]
 flagged_for_leo: ["Cross-domain synthesis — this is what we're building with the Teleo collective"]
 ---
 ## Content
 The next step for autoresearch is that it has to be asynchronously massively collaborative for agents (think: SETI@home style). The goal is not to emulate a single PhD student, it's to emulate a research community of them.
 Current code synchronously grows a single thread of commits in a particular research direction. But the original repo is more of a seed, from which could sprout commits contributed by agents on all kinds of different research directions or for different compute platforms. Git(Hub) is *almost* but not really suited for this. It has a softly built in assumption of one "master" branch, which temporarily forks off into PRs just to merge back a bit later.
 I tried to prototype something super lightweight that could have a flavor of this, e.g. just a Discussion, written by my agent as a summary of its overnight run:
 https://t.co/tmZeqyDY1W
 Alternatively, a PR has the benefit of exact commits:
 https://t.co/CZIbuJIqlk
 but you'd never want to actually merge it... You'd just want to "adopt" and accumulate branches of commits. But even in this lightweight way, you could ask your agent to first read the Discussions/PRs using GitHub CLI for inspiration, and after its research is done, contribute a little "paper" of findings back.
 I'm not actually exactly sure what this should look like, but it's a big idea that is more general than just the autoresearch repo specifically. Agents can in principle easily juggle and collaborate on thousands of commits across arbitrary branch structures. Existing abstractions will accumulate stress as intelligence, attention and tenacity cease to be bottlenecks.
 ## Agent Notes
 **Why this matters:** Karpathy (3M+ followers, former Tesla AI director) is independently arriving at the same architecture we're building with the Teleo collective — agents coordinating through git, PRs as knowledge contributions, branches as research directions. His framing of "emulate a research community, not a single PhD student" IS our thesis. And his observation that Git's assumptions break under agent-scale collaboration is a problem we're actively solving.
 **KB connections:**
 - Directly validates [[coordination protocol design produces larger capability gains than model scaling]]
 - Challenges/extends [[the same coordination protocol applied to different AI models produces radically different problem-solving strategies]] — Karpathy found that 8 agents with different setups (solo vs hierarchical) produced different results
 - Relevant to [[domain specialization with cross-domain synthesis produces better collective intelligence]]
 - His "existing abstractions will accumulate stress" connects to the git-as-coordination-substrate thesis
 **Extraction hints:**
 - Claim: agent research communities outperform single-agent research because the goal is to emulate a community not an individual
 - Claim: git's branch-merge model is insufficient for agent-scale collaboration because it assumes one master branch with temporary forks
 - Claim: when intelligence and attention cease to be bottlenecks, existing coordination abstractions (git, PRs, branches) accumulate stress
 **Context:** This is part of a series of tweets about karpathy's autoresearch project — AI agents autonomously iterating on nanochat (minimal GPT training code). He's running multiple agents on GPU clusters doing automated ML research. The Feb 27 thread about 8 agents is critical companion reading (separate source).
--- a/inbox/archive/2026-03-09-abbasshaikh-x-archive.md
+++ b/inbox/archive/2026-03-09-abbasshaikh-x-archive.md
@ -6,7 +6,7 @@ url: https://x.com/Abbasshaikh
 date: 2026-03-09
 domain: internet-finance
 format: tweet
-status: unprocessed
+status: null-result
 tags: [umbra, privacy, futardio, community-organizing, metadao-ecosystem]
 linked_set: metadao-x-landscape-2026-03
 curator_notes: |
@ -22,6 +22,10 @@ extraction_hints:
  - "Privacy + ownership coins intersection — potential cross-domain connection"
  - "Low claim extraction priority — community voice, not mechanism analysis"
 priority: low
 processed_by: rio
 processed_date: 2026-03-10
 extraction_model: "minimax/minimax-m2.5"
 extraction_notes: "No extractable claims. Source is a tweet archive metadata summary with only two substantive data points: (1) Umbra raised $3M via MetaDAO ICO with 7x first-week performance, and (2) Abbas is a community organizer for Futardio. The curator notes explicitly classify this as 'low claim extraction priority — community voice, not mechanism analysis.' The ICO performance data ($3M, 7x) is already covered by existing claim 'MetaDAO is the futarchy launchpad on Solana where projects raise capital through unruggable ICOs...' The community organizing pattern is cultural/soft data not suitable for claim extraction. No specific, disagreeable interpretive claims can be made from this source."
 ---
 # @Abbasshaikh X Archive (March 2026)
--- a/inbox/archive/2026-03-09-flashtrade-x-archive.md
+++ b/inbox/archive/2026-03-09-flashtrade-x-archive.md
@ -6,7 +6,7 @@ url: https://x.com/FlashTrade
 date: 2026-03-09
 domain: internet-finance
 format: tweet
-status: unprocessed
+status: null-result
 tags: [flash-trade, perps, solana, trading, leverage]
 linked_set: metadao-x-landscape-2026-03
 curator_notes: |
@ -21,6 +21,10 @@ extraction_hints:
  - "Asset-backed trading model could connect to 'permissionless leverage on MetaDAO ecosystem tokens' if Flash integrates with ecosystem"
  - "Null-result candidate — primarily trading signals, not mechanism design"
 priority: low
 processed_by: rio
 processed_date: 2026-03-10
 extraction_model: "minimax/minimax-m2.5"
 extraction_notes: "Null-result extraction. Curator explicitly flagged this as low priority with 'no mechanism design insights relevant to our domain.' Source contains product information (50x leveraged derivatives, asset-backed trading model) and trading signals rather than mechanism design or governance insights. No MetaDAO-specific claims identified. No connection to existing claim themes (futarchy, ownership coins, Living Capital, etc.). Content is peripheral to Teleo knowledge base domains."
 ---
 # @FlashTrade X Archive (March 2026)
--- a/inbox/archive/2026-03-09-richard-isc-x-archive.md
+++ b/inbox/archive/2026-03-09-richard-isc-x-archive.md
@ -6,7 +6,7 @@ url: https://x.com/Richard_ISC
 date: 2026-03-09
 domain: internet-finance
 format: tweet
-status: unprocessed
+status: null-result
 tags: [isc, governance, futarchy, mechanism-design, metadao-ecosystem, defi]
 linked_set: metadao-x-landscape-2026-03
 curator_notes: |
@ -23,6 +23,10 @@ extraction_hints:
  - "Ecosystem project evaluations — Richard's assessments provide practitioner perspective on futarchy outcomes"
  - "Connection: his criticism of overraising maps to our 'early-conviction pricing is an unsolved mechanism design problem' claim"
 priority: medium
 processed_by: rio
 processed_date: 2026-03-10
 extraction_model: "minimax/minimax-m2.5"
 extraction_notes: "Source is a meta-summary of Richard_ISC's tweet content rather than actual tweets with verifiable evidence. The curator notes describe the type of content he produces (mechanism design critiques, governance token commentary) but don't provide specific data points, quotes, or study results that can be extracted into claims. Additionally, potential claims (overraising as mechanism design flaw, governance token liquidity vs equity, ecosystem project evaluations) would duplicate existing claims in the knowledge base about capital formation incentive misalignment, ownership coin thesis, and futarchy practitioner perspectives."
 ---
 # @Richard_ISC X Archive (March 2026)
--- a/inbox/archive/2026-03-10-iab-ai-ad-gap-widens.md
+++ b/inbox/archive/2026-03-10-iab-ai-ad-gap-widens.md
@ -0,0 +1,65 @@
 ---
 type: source
 title: "IAB: The AI Ad Gap Widens — Consumer Sentiment More Negative Than Advertisers Believe"
 author: "IAB (Interactive Advertising Bureau)"
 url: https://www.iab.com/insights/the-ai-gap-widens/
 date: 2026-01-01
 domain: entertainment
 secondary_domains: []
 format: report
 status: unprocessed
 priority: high
 tags: [consumer-acceptance, ai-content, advertiser-perception-gap, gen-z, authenticity]
 ---
 ## Content
 The IAB AI Ad Gap Widens report documents a substantial and growing perception gap between how advertisers think consumers feel about AI-generated ads versus how consumers actually feel.
 **Key data:**
 - 82% of ad executives believe Gen Z/Millennials feel very or somewhat positive about AI ads
 - Only 45% of consumers actually report positive sentiment
 - Gap = 37 percentage points (up from 32 points in 2024)
 **Consumer sentiment shift year-over-year:**
 - Very/somewhat negative: increased by 12 percentage points from 2024 to 2026
 - Neutral respondents: dropped from 34% to 25% (polarization increasing)
 **Gen Z vs. Millennial breakdown:**
 - Gen Z negative sentiment: 39%
 - Millennial negative sentiment: 20%
 - Gen Z-Millennial gap widened significantly from 2024 (21% vs. 15% previously)
 **Brand attribute perception gaps:**
 - "Forward-thinking": 46% of ad executives vs. 22% of consumers
 - "Manipulative": 10% of ad executives vs. 20% of consumers
 - "Unethical": 7% of ad executives vs. 16% of consumers
 - "Innovative": dropped to 23% consumers (from 30% in 2024), while advertiser belief increased to 49%
 **Gen Z rates AI-using brands more negatively than Millennials on:**
 - Authenticity (30% vs. 13%)
 - Disconnectedness (26% vs. 8%)
 - Ethics (24% vs. 8%)
 ## Agent Notes
 **Why this matters:** This is direct quantitative evidence that consumer acceptance of AI content is DECREASING as AI quality increases — the opposite of what the simple "quality threshold" hypothesis predicts. The widening of the gap (32 → 37 points) from 2024 to 2026 is significant because AI quality improved dramatically in the same period. This challenges the framing that consumer resistance will naturally erode as AI gets better.
 **What surprised me:** The polarization data (neutral dropping from 34% to 25%) is striking. Consumers aren't staying neutral as they get more exposure to AI content — they're forming stronger opinions, and mostly negative ones. This suggests habituation and acceptance is NOT happening in advertising, at least.
 **What I expected but didn't find:** I expected some evidence that context-appropriate AI use (e.g., behind-the-scenes, efficiency tools) would score well. The report doesn't distinguish between consumer-facing AI content vs. AI-assisted production.
 **KB connections:**
 - Directly tests claim: `GenAI adoption in entertainment will be gated by consumer acceptance not technology capability`
 - Relates to: `consumer definition of quality is fluid and revealed through preference not fixed by production value`
 - Challenges implicit assumption that acceptance grows with exposure
 **Extraction hints:**
 - New claim candidate: "Consumer rejection of AI-generated content intensifies with AI quality improvement because authenticity signaling becomes more valuable as AI-human distinction becomes harder"
 - New claim candidate: "The advertiser-consumer AI perception gap is widening not narrowing suggesting a structural misalignment in the advertising industry"
 **Context:** IAB is the industry association for digital advertising. This report has direct authority with brands and ad agencies. Published in coordination with marketer and consumer surveys.
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: `GenAI adoption in entertainment will be gated by consumer acceptance not technology capability`
 WHY ARCHIVED: Provides the strongest quantitative evidence that consumer acceptance is the binding constraint — but in a surprising direction: rejection is intensifying, not eroding, as AI quality improves. The 37-point perception gap between advertisers and consumers is a structural misalignment claim.
 EXTRACTION HINT: Focus on (1) the widening gap as evidence of structural misalignment, (2) the year-over-year negative sentiment increase as evidence that exposure ≠ acceptance, (3) Gen Z data as leading indicator for entertainment industry.
--- a/maps/overview.md
+++ b/maps/overview.md
@ -1,19 +1,6 @@
 # Teleo Codex — Overview
-A shared knowledge base of 400+ falsifiable claims maintained by six AI domain specialists. Every claim has evidence, a confidence level, and wiki links to related claims.
+The shared knowledge base for the Teleo collective. Contains the intellectual operating system: theoretical foundations, organizational architecture, and domain-specific analysis that agents use to reason about humanity's trajectory.
 ## Start Here
 Pick an entry point based on what you care about:
 - **AI and alignment** → [domains/ai-alignment/_map.md](../domains/ai-alignment/_map.md) — 52 claims on superintelligence, coordination, displacement
 - **DeFi, futarchy, and markets** → [domains/internet-finance/_map.md](../domains/internet-finance/_map.md) — 63 claims on prediction markets, MetaDAO, capital formation
 - **Healthcare disruption** → [domains/health/_map.md](../domains/health/_map.md) — 45 claims on AI diagnostics, prevention systems, Jevons paradox
 - **Space development** → [domains/space-development/_map.md](../domains/space-development/_map.md) — 21 claims on launch economics, cislunar infrastructure
 - **Entertainment and media** → [domains/entertainment/_map.md](../domains/entertainment/_map.md) — 20 claims on disruption, creator economy, IP as platform
 - **The big picture** → [core/teleohumanity/_map.md](../core/teleohumanity/_map.md) — why collective superintelligence, not monolithic
 **How claims work:** Every claim is a prose proposition — the filename IS the argument. Each has a confidence level (proven/likely/experimental/speculative), inline evidence, and wiki links to related claims. Follow the links to traverse the graph.
 ## How This Knowledge Base Is Organized
@ -39,12 +26,9 @@ Domain-specific claims. Each agent specializes in one domain but draws on all fo
 - **domains/internet-finance/** — DeFi, MetaDAO ecosystem, futarchy implementations, regulatory landscape (Rio's territory)
 - **domains/entertainment/** — Media disruption, creator economy, community IP, cultural dynamics (Clay's territory)
 - **domains/ai-alignment/** — Collective superintelligence, coordination, AI displacement (Theseus's territory)
 - **domains/health/** — Healthcare disruption, AI diagnostics, prevention systems (Vida's territory)
 - **domains/space-development/** — Launch economics, cislunar infrastructure, governance (Astra's territory)
 ### Agents (agents/)
-Soul documents defining each agent's identity, world model, reasoning framework, and beliefs. Six active agents: Leo (coordinator), Rio (internet finance), Clay (entertainment), Theseus (AI alignment), Vida (health), Astra (space development).
+Soul documents defining each agent's identity, world model, reasoning framework, and beliefs. Three active agents: Leo (coordinator), Rio (internet finance), Clay (entertainment).
 ### Schemas (schemas/)
 How each content type is structured: claims, beliefs, positions.
--- a/ops/evaluate-trigger.sh
+++ b/ops/evaluate-trigger.sh
@ -6,8 +6,8 @@
 #   2. Domain agent — domain expertise, duplicate check, technical accuracy
 #
 # After both reviews, auto-merges if:
-#   - Leo approved (gh pr review --approve)
+#   - Leo's comment contains "**Verdict:** approve"
-#   - Domain agent verdict is "Approve" (parsed from comment)
+#   - Domain agent's comment contains "**Verdict:** approve"
 #   - No territory violations (files outside proposer's domain)
 #
 # Usage:
@ -26,8 +26,14 @@
 #   - Lockfile prevents concurrent runs
 #   - Auto-merge requires ALL reviewers to approve + no territory violations
 #   - Each PR runs sequentially to avoid branch conflicts
-#   - Timeout: 10 minutes per agent per PR
+#   - Timeout: 20 minutes per agent per PR
 #   - Pre-flight checks: clean working tree, gh auth
 #
 # Verdict protocol:
 #   All agents use `gh pr comment` (NOT `gh pr review`) because all agents
 #   share the m3taversal GitHub account — `gh pr review --approve` fails
 #   when the PR author and reviewer are the same user. The merge check
 #   parses issue comments for structured verdict markers instead.
 set -euo pipefail
@ -39,7 +45,7 @@ cd "$REPO_ROOT"
 LOCKFILE="/tmp/evaluate-trigger.lock"
 LOG_DIR="$REPO_ROOT/ops/sessions"
-TIMEOUT_SECONDS=600
+TIMEOUT_SECONDS=1200
 DRY_RUN=false
 LEO_ONLY=false
 NO_MERGE=false
@ -62,24 +68,30 @@ detect_domain_agent() {
    vida/*|*/health*)          agent="vida"; domain="health" ;;
    astra/*|*/space-development*) agent="astra"; domain="space-development" ;;
    leo/*|*/grand-strategy*)   agent="leo"; domain="grand-strategy" ;;
    contrib/*)
      # External contributor — detect domain from changed files (fall through to file check)
      agent=""; domain=""
      ;;
    *)
-      # Fall back to checking which domain directory has changed files
+      agent=""; domain=""
      if echo "$files" | grep -q "domains/internet-finance/"; then
        agent="rio"; domain="internet-finance"
      elif echo "$files" | grep -q "domains/entertainment/"; then
        agent="clay"; domain="entertainment"
      elif echo "$files" | grep -q "domains/ai-alignment/"; then
        agent="theseus"; domain="ai-alignment"
      elif echo "$files" | grep -q "domains/health/"; then
        agent="vida"; domain="health"
      elif echo "$files" | grep -q "domains/space-development/"; then
        agent="astra"; domain="space-development"
      else
        agent=""; domain=""
      fi
      ;;
  esac
  # If no agent detected from branch prefix, check changed files
  if [ -z "$agent" ]; then
    if echo "$files" | grep -q "domains/internet-finance/"; then
      agent="rio"; domain="internet-finance"
    elif echo "$files" | grep -q "domains/entertainment/"; then
      agent="clay"; domain="entertainment"
    elif echo "$files" | grep -q "domains/ai-alignment/"; then
      agent="theseus"; domain="ai-alignment"
    elif echo "$files" | grep -q "domains/health/"; then
      agent="vida"; domain="health"
    elif echo "$files" | grep -q "domains/space-development/"; then
      agent="astra"; domain="space-development"
    fi
  fi
  echo "$agent $domain"
 }
@ -112,8 +124,8 @@ if ! command -v claude >/dev/null 2>&1; then
  exit 1
 fi
-# Check for dirty working tree (ignore ops/ and .claude/ which may contain uncommitted scripts)
+# Check for dirty working tree (ignore ops/, .claude/, .github/ which may contain local-only files)
-DIRTY_FILES=$(git status --porcelain | grep -v '^?? ops/' | grep -v '^ M ops/' | grep -v '^?? \.claude/' | grep -v '^ M \.claude/' || true)
+DIRTY_FILES=$(git status --porcelain | grep -v '^?? ops/' | grep -v '^ M ops/' | grep -v '^?? \.claude/' | grep -v '^ M \.claude/' | grep -v '^?? \.github/' | grep -v '^ M \.github/' || true)
 if [ -n "$DIRTY_FILES" ]; then
  echo "ERROR: Working tree is dirty. Clean up before running."
  echo "$DIRTY_FILES"
@ -145,7 +157,8 @@ if [ -n "$SPECIFIC_PR" ]; then
  fi
  PRS_TO_REVIEW="$SPECIFIC_PR"
 else
-  OPEN_PRS=$(gh pr list --state open --json number --jq '.[].number' 2>/dev/null || echo "")
+  # NOTE: gh pr list silently returns empty in some worktree configs; use gh api instead
  OPEN_PRS=$(gh api repos/:owner/:repo/pulls --jq '.[].number' 2>/dev/null || echo "")
  if [ -z "$OPEN_PRS" ]; then
    echo "No open PRs found. Nothing to review."
@ -154,17 +167,23 @@ else
  PRS_TO_REVIEW=""
  for pr in $OPEN_PRS; do
-    LAST_REVIEW_DATE=$(gh api "repos/{owner}/{repo}/pulls/$pr/reviews" \
+    # Check if this PR already has a Leo verdict comment (avoid re-reviewing)
-      --jq 'map(select(.state != "DISMISSED")) | sort_by(.submitted_at) | last | .submitted_at' 2>/dev/null || echo "")
+    LEO_COMMENTED=$(gh pr view "$pr" --json comments \
      --jq '[.comments[] | select(.body | test("VERDICT:LEO:(APPROVE|REQUEST_CHANGES)"))] | length' 2>/dev/null || echo "0")
    LAST_COMMIT_DATE=$(gh pr view "$pr" --json commits --jq '.commits[-1].committedDate' 2>/dev/null || echo "")
-    if [ -z "$LAST_REVIEW_DATE" ]; then
+    if [ "$LEO_COMMENTED" = "0" ]; then
      PRS_TO_REVIEW="$PRS_TO_REVIEW $pr"
    elif [ -n "$LAST_COMMIT_DATE" ] && [[ "$LAST_COMMIT_DATE" > "$LAST_REVIEW_DATE" ]]; then
      echo "PR #$pr: New commits since last review. Queuing for re-review."
      PRS_TO_REVIEW="$PRS_TO_REVIEW $pr"
    else
-      echo "PR #$pr: No new commits since last review. Skipping."
+      # Check if new commits since last Leo review
      LAST_LEO_DATE=$(gh pr view "$pr" --json comments \
        --jq '[.comments[] | select(.body | test("VERDICT:LEO:")) | .createdAt] | last' 2>/dev/null || echo "")
      if [ -n "$LAST_COMMIT_DATE" ] && [ -n "$LAST_LEO_DATE" ] && [[ "$LAST_COMMIT_DATE" > "$LAST_LEO_DATE" ]]; then
        echo "PR #$pr: New commits since last review. Queuing for re-review."
        PRS_TO_REVIEW="$PRS_TO_REVIEW $pr"
      else
        echo "PR #$pr: Already reviewed. Skipping."
      fi
    fi
  done
@ -195,7 +214,7 @@ run_agent_review() {
  log_file="$LOG_DIR/${agent_name}-review-pr${pr}-${timestamp}.log"
  review_file="/tmp/${agent_name}-review-pr${pr}.md"
-  echo "  Running ${agent_name}..."
+  echo "  Running ${agent_name} (model: ${model})..."
  echo "  Log: $log_file"
  if perl -e "alarm $TIMEOUT_SECONDS; exec @ARGV" claude -p \
@ -240,6 +259,7 @@ check_territory_violations() {
    vida)    allowed_domains="domains/health/" ;;
    astra)   allowed_domains="domains/space-development/" ;;
    leo)     allowed_domains="core/|foundations/" ;;
    contrib) echo ""; return 0 ;;  # External contributors — skip territory check
    *)       echo ""; return 0 ;;  # Unknown proposer — skip check
  esac
@ -266,74 +286,51 @@ check_territory_violations() {
 }
 # --- Auto-merge check ---
-# Returns 0 if PR should be merged, 1 if not
+# Parses issue comments for structured verdict markers.
 # Verdict protocol: agents post `<!-- VERDICT:AGENT_KEY:APPROVE -->` or
 # `<!-- VERDICT:AGENT_KEY:REQUEST_CHANGES -->` as HTML comments in their review.
 # This is machine-parseable and invisible in the rendered comment.
 check_merge_eligible() {
  local pr_number="$1"
  local domain_agent="$2"
  local leo_passed="$3"
-  # Gate 1: Leo must have passed
+  # Gate 1: Leo must have completed without timeout/error
  if [ "$leo_passed" != "true" ]; then
    echo "BLOCK: Leo review failed or timed out"
    return 1
  fi
-  # Gate 2: Check Leo's review state via GitHub API
+  # Gate 2: Check Leo's verdict from issue comments
-  local leo_review_state
+  local leo_verdict
-  leo_review_state=$(gh api "repos/{owner}/{repo}/pulls/${pr_number}/reviews" \
+  leo_verdict=$(gh pr view "$pr_number" --json comments \
-    --jq '[.[] | select(.state != "DISMISSED" and .state != "PENDING")] | last | .state' 2>/dev/null || echo "")
+    --jq '[.comments[] | select(.body | test("VERDICT:LEO:")) | .body] | last' 2>/dev/null || echo "")
-  if [ "$leo_review_state" = "APPROVED" ]; then
+  if echo "$leo_verdict" | grep -q "VERDICT:LEO:APPROVE"; then
-    echo "Leo: APPROVED (via review API)"
+    echo "Leo: APPROVED"
-  elif [ "$leo_review_state" = "CHANGES_REQUESTED" ]; then
+  elif echo "$leo_verdict" | grep -q "VERDICT:LEO:REQUEST_CHANGES"; then
-    echo "BLOCK: Leo requested changes (review API state: CHANGES_REQUESTED)"
+    echo "BLOCK: Leo requested changes"
    return 1
  else
-    # Fallback: check PR comments for Leo's verdict
+    echo "BLOCK: Could not find Leo's verdict marker in PR comments"
-    local leo_verdict
+    return 1
    leo_verdict=$(gh pr view "$pr_number" --json comments \
      --jq '.comments[] | select(.body | test("## Leo Review")) | .body' 2>/dev/null \
      | grep -oiE '\*\*Verdict:[^*]+\*\*' | tail -1 || echo "")
    if echo "$leo_verdict" | grep -qi "approve"; then
      echo "Leo: APPROVED (via comment verdict)"
    elif echo "$leo_verdict" | grep -qi "request changes\|reject"; then
      echo "BLOCK: Leo verdict: $leo_verdict"
      return 1
    else
      echo "BLOCK: Could not determine Leo's verdict"
      return 1
    fi
  fi
  # Gate 3: Check domain agent verdict (if applicable)
  if [ -n "$domain_agent" ] && [ "$domain_agent" != "leo" ]; then
    local domain_key
    domain_key=$(echo "$domain_agent" | tr '[:lower:]' '[:upper:]')
    local domain_verdict
    # Search for verdict in domain agent's review — match agent name, "domain reviewer", or "Domain Review"
    domain_verdict=$(gh pr view "$pr_number" --json comments \
-      --jq ".comments[] | select(.body | test(\"domain review|${domain_agent}|peer review\"; \"i\")) | .body" 2>/dev/null \
+      --jq "[.comments[] | select(.body | test(\"VERDICT:${domain_key}:\")) | .body] | last" 2>/dev/null || echo "")
      | grep -oiE '\*\*Verdict:[^*]+\*\*' | tail -1 || echo "")
-    if [ -z "$domain_verdict" ]; then
+    if echo "$domain_verdict" | grep -q "VERDICT:${domain_key}:APPROVE"; then
-      # Also check review API for domain agent approval
+      echo "Domain agent ($domain_agent): APPROVED"
-      # Since all agents use the same GitHub account, we check for multiple approvals
+    elif echo "$domain_verdict" | grep -q "VERDICT:${domain_key}:REQUEST_CHANGES"; then
-      local approval_count
+      echo "BLOCK: $domain_agent requested changes"
      approval_count=$(gh api "repos/{owner}/{repo}/pulls/${pr_number}/reviews" \
        --jq '[.[] | select(.state == "APPROVED")] | length' 2>/dev/null || echo "0")
      if [ "$approval_count" -ge 2 ]; then
        echo "Domain agent: APPROVED (multiple approvals via review API)"
      else
        echo "BLOCK: No domain agent verdict found"
        return 1
      fi
    elif echo "$domain_verdict" | grep -qi "approve"; then
      echo "Domain agent ($domain_agent): APPROVED (via comment verdict)"
    elif echo "$domain_verdict" | grep -qi "request changes\|reject"; then
      echo "BLOCK: Domain agent verdict: $domain_verdict"
      return 1
    else
-      echo "BLOCK: Unclear domain agent verdict: $domain_verdict"
+      echo "BLOCK: No verdict marker found for $domain_agent"
      return 1
    fi
  else
@ -403,11 +400,15 @@ Also check:
 - Cross-domain connections that the proposer may have missed
 Write your complete review to ${LEO_REVIEW_FILE}
 Then post it with: gh pr review ${pr} --comment --body-file ${LEO_REVIEW_FILE}
-If ALL claims pass quality gates: gh pr review ${pr} --approve --body-file ${LEO_REVIEW_FILE}
+CRITICAL — Verdict format: Your review MUST end with exactly one of these verdict markers (as an HTML comment on its own line):
-If ANY claim needs changes: gh pr review ${pr} --request-changes --body-file ${LEO_REVIEW_FILE}
+  <!-- VERDICT:LEO:APPROVE -->
  <!-- VERDICT:LEO:REQUEST_CHANGES -->
 Then post the review as an issue comment:
  gh pr comment ${pr} --body-file ${LEO_REVIEW_FILE}
 IMPORTANT: Use 'gh pr comment' NOT 'gh pr review'. We use a shared GitHub account so gh pr review --approve fails.
 DO NOT merge — the orchestrator handles merge decisions after all reviews are posted.
 Work autonomously. Do not ask for confirmation."
@ -432,6 +433,7 @@ Work autonomously. Do not ask for confirmation."
  else
    DOMAIN_REVIEW_FILE="/tmp/${DOMAIN_AGENT}-review-pr${pr}.md"
    AGENT_NAME_UPPER=$(echo "${DOMAIN_AGENT}" | awk '{print toupper(substr($0,1,1)) substr($0,2)}')
    AGENT_KEY_UPPER=$(echo "${DOMAIN_AGENT}" | tr '[:lower:]' '[:upper:]')
    DOMAIN_PROMPT="You are ${AGENT_NAME_UPPER}. Read agents/${DOMAIN_AGENT}/identity.md, agents/${DOMAIN_AGENT}/beliefs.md, and skills/evaluate.md.
 You are reviewing PR #${pr} as the domain expert for ${DOMAIN}.
@ -452,8 +454,15 @@ Your review focuses on DOMAIN EXPERTISE — things only a ${DOMAIN} specialist w
 6. **Confidence calibration** — From your domain expertise, is the confidence level right?
 Write your review to ${DOMAIN_REVIEW_FILE}
 Post it with: gh pr review ${pr} --comment --body-file ${DOMAIN_REVIEW_FILE}
 CRITICAL — Verdict format: Your review MUST end with exactly one of these verdict markers (as an HTML comment on its own line):
  <!-- VERDICT:${AGENT_KEY_UPPER}:APPROVE -->
  <!-- VERDICT:${AGENT_KEY_UPPER}:REQUEST_CHANGES -->
 Then post the review as an issue comment:
  gh pr comment ${pr} --body-file ${DOMAIN_REVIEW_FILE}
 IMPORTANT: Use 'gh pr comment' NOT 'gh pr review'. We use a shared GitHub account so gh pr review --approve fails.
 Sign your review as ${AGENT_NAME_UPPER} (domain reviewer for ${DOMAIN}).
 DO NOT duplicate Leo's quality gate checks — he covers those.
 DO NOT merge — the orchestrator handles merge decisions after all reviews are posted.
@ -486,7 +495,7 @@ Work autonomously. Do not ask for confirmation."
    if [ "$MERGE_RESULT" -eq 0 ]; then
      echo "  Auto-merge: ALL GATES PASSED — merging PR #$pr"
-      if gh pr merge "$pr" --squash --delete-branch 2>&1; then
+      if gh pr merge "$pr" --squash 2>&1; then
        echo "  PR #$pr: MERGED successfully."
        MERGED=$((MERGED + 1))
      else
--- a/ops/extract-cron.sh
+++ b/ops/extract-cron.sh
@ -0,0 +1,179 @@
 #!/bin/bash
 # Extract claims from unprocessed sources in inbox/archive/
 # Runs via cron on VPS every 15 minutes.
 #
 # Concurrency model:
 #   - Lockfile prevents overlapping runs
 #   - MAX_SOURCES=5 per cycle (works through backlog over multiple runs)
 #   - Sequential processing (one source at a time)
 #   - 50 sources landing at once = ~10 cron cycles to clear, not 50 parallel agents
 #
 # Domain routing:
 #   - Reads domain: field from source frontmatter
 #   - Maps to the domain agent (rio, clay, theseus, vida, astra, leo)
 #   - Runs extraction AS that agent — their territory, their extraction
 #   - Skips sources with status: processing (agent handling it themselves)
 #
 # Flow:
 #   1. Pull latest main
 #   2. Find sources with status: unprocessed (skip processing/processed/null-result)
 #   3. For each: run Claude headless to extract claims as the domain agent
 #   4. Commit extractions, push, open PR
 #   5. Update source status to processed
 #
 # The eval pipeline (webhook.py) handles review and merge separately.
 set -euo pipefail
 REPO_DIR="/opt/teleo-eval/workspaces/extract"
 REPO_URL="http://m3taversal:$(cat /opt/teleo-eval/secrets/forgejo-admin-token)@localhost:3000/teleo/teleo-codex.git"
 CLAUDE_BIN="/home/teleo/.local/bin/claude"
 LOG_DIR="/opt/teleo-eval/logs"
 LOG="$LOG_DIR/extract-cron.log"
 LOCKFILE="/tmp/extract-cron.lock"
 MAX_SOURCES=5  # Process at most 5 sources per run to limit cost
 log() { echo "[$(date -Iseconds)] $*" >> "$LOG"; }
 # --- Lock ---
 if [ -f "$LOCKFILE" ]; then
    pid=$(cat "$LOCKFILE" 2>/dev/null)
    if kill -0 "$pid" 2>/dev/null; then
        log "SKIP: already running (pid $pid)"
        exit 0
    fi
    log "WARN: stale lockfile, removing"
    rm -f "$LOCKFILE"
 fi
 echo $$ > "$LOCKFILE"
 trap 'rm -f "$LOCKFILE"' EXIT
 # --- Ensure repo clone ---
 if [ ! -d "$REPO_DIR/.git" ]; then
    log "Cloning repo..."
    git clone "$REPO_URL" "$REPO_DIR" >> "$LOG" 2>&1
 fi
 cd "$REPO_DIR"
 # --- Pull latest main ---
 git checkout main >> "$LOG" 2>&1
 git pull --rebase >> "$LOG" 2>&1
 # --- Find unprocessed sources ---
 UNPROCESSED=$(grep -rl '^status: unprocessed' inbox/archive/ 2>/dev/null | head -n "$MAX_SOURCES" || true)
 if [ -z "$UNPROCESSED" ]; then
    log "No unprocessed sources found"
    exit 0
 fi
 COUNT=$(echo "$UNPROCESSED" | wc -l | tr -d ' ')
 log "Found $COUNT unprocessed source(s)"
 # --- Process each source ---
 for SOURCE_FILE in $UNPROCESSED; do
    SLUG=$(basename "$SOURCE_FILE" .md)
    BRANCH="extract/$SLUG"
    log "Processing: $SOURCE_FILE → branch $BRANCH"
    # Create branch from main
    git checkout main >> "$LOG" 2>&1
    git branch -D "$BRANCH" 2>/dev/null || true
    git checkout -b "$BRANCH" >> "$LOG" 2>&1
    # Read domain from frontmatter
    DOMAIN=$(grep '^domain:' "$SOURCE_FILE" | head -1 | sed 's/domain: *//' | tr -d '"' | tr -d "'" | xargs)
    # Map domain to agent
    case "$DOMAIN" in
        internet-finance) AGENT="rio" ;;
        entertainment) AGENT="clay" ;;
        ai-alignment) AGENT="theseus" ;;
        health) AGENT="vida" ;;
        space-development) AGENT="astra" ;;
        *) AGENT="leo" ;;
    esac
    AGENT_TOKEN=$(cat "/opt/teleo-eval/secrets/forgejo-${AGENT}-token" 2>/dev/null || cat /opt/teleo-eval/secrets/forgejo-leo-token)
    log "Domain: $DOMAIN, Agent: $AGENT"
    # Run Claude headless to extract claims
    EXTRACT_PROMPT="You are $AGENT, a Teleo knowledge base agent. Extract claims from this source.
 READ these files first:
 - skills/extract.md (extraction process)
 - schemas/claim.md (claim format)
 - $SOURCE_FILE (the source to extract from)
 Then scan domains/$DOMAIN/ to check for duplicate claims.
 EXTRACT claims following the process in skills/extract.md:
 1. Read the source completely
 2. Separate evidence from interpretation
 3. Extract candidate claims (specific, disagreeable, evidence-backed)
 4. Check for duplicates against existing claims in domains/$DOMAIN/
 5. Write claim files to domains/$DOMAIN/ with proper YAML frontmatter
 6. Update $SOURCE_FILE: set status to 'processed', add processed_by: $AGENT, processed_date: $(date +%Y-%m-%d), and claims_extracted list
 If no claims can be extracted, update $SOURCE_FILE: set status to 'null-result' and add notes explaining why.
 IMPORTANT: Use the Edit tool to update the source file status. Use the Write tool to create new claim files. Do not create claims that duplicate existing ones."
    # Run extraction with timeout (10 minutes)
    timeout 600 "$CLAUDE_BIN" -p "$EXTRACT_PROMPT" \
        --allowedTools 'Read,Write,Edit,Glob,Grep' \
        --model sonnet \
        >> "$LOG" 2>&1 || {
        log "WARN: Claude extraction failed or timed out for $SOURCE_FILE"
        git checkout main >> "$LOG" 2>&1
        continue
    }
    # Check if any files were created/modified
    CHANGES=$(git status --porcelain | wc -l | tr -d ' ')
    if [ "$CHANGES" -eq 0 ]; then
        log "No changes produced for $SOURCE_FILE"
        git checkout main >> "$LOG" 2>&1
        continue
    fi
    # Stage and commit
    git add inbox/archive/ "domains/$DOMAIN/" >> "$LOG" 2>&1
    git commit -m "$AGENT: extract claims from $(basename "$SOURCE_FILE")
 - Source: $SOURCE_FILE
 - Domain: $DOMAIN
 - Extracted by: headless extraction cron
 Pentagon-Agent: $(echo "$AGENT" | sed 's/./\U&/') <HEADLESS>" >> "$LOG" 2>&1
    # Push branch
    git push -u "$REPO_URL" "$BRANCH" --force >> "$LOG" 2>&1
    # Open PR
    PR_TITLE="$AGENT: extract claims from $(basename "$SOURCE_FILE" .md)"
    PR_BODY="## Automated Extraction\n\nSource: \`$SOURCE_FILE\`\nDomain: $DOMAIN\nExtracted by: headless cron on VPS\n\nThis PR was created automatically by the extraction cron job. Claims were extracted using \`skills/extract.md\` process via Claude headless."
    curl -s -X POST "http://localhost:3000/api/v1/repos/teleo/teleo-codex/pulls" \
        -H "Authorization: token $AGENT_TOKEN" \
        -H "Content-Type: application/json" \
        -d "{
            \"title\": \"$PR_TITLE\",
            \"body\": \"$PR_BODY\",
            \"base\": \"main\",
            \"head\": \"$BRANCH\"
        }" >> "$LOG" 2>&1
    log "PR opened for $SOURCE_FILE"
    # Back to main for next source
    git checkout main >> "$LOG" 2>&1
    # Brief pause between extractions
    sleep 5
 done
 log "Extraction run complete: processed $COUNT source(s)"
--- a/ops/extract-graph-data.py
+++ b/ops/extract-graph-data.py
@ -0,0 +1,520 @@
 #!/usr/bin/env python3
 """
 extract-graph-data.py — Extract knowledge graph from teleo-codex markdown files.
 Reads all .md claim/conviction files, parses YAML frontmatter and wiki-links,
 and outputs graph-data.json matching the teleo-app GraphData interface.
 Usage:
    python3 ops/extract-graph-data.py [--output path/to/graph-data.json]
 Must be run from the teleo-codex repo root.
 """
 import argparse
 import json
 import os
 import re
 import subprocess
 import sys
 from datetime import datetime, timezone
 from pathlib import Path
 # ---------------------------------------------------------------------------
 # Config
 # ---------------------------------------------------------------------------
 SCAN_DIRS = ["core", "domains", "foundations", "convictions"]
 # Only extract these content types (from frontmatter `type` field).
 # If type is missing, include the file anyway (many claims lack explicit type).
 INCLUDE_TYPES = {"claim", "conviction", "analysis", "belief", "position", None}
 # Domain → default agent mapping (fallback when git attribution unavailable)
 DOMAIN_AGENT_MAP = {
    "internet-finance": "rio",
    "entertainment": "clay",
    "health": "vida",
    "ai-alignment": "theseus",
    "space-development": "astra",
    "grand-strategy": "leo",
    "mechanisms": "leo",
    "living-capital": "leo",
    "living-agents": "leo",
    "teleohumanity": "leo",
    "critical-systems": "leo",
    "collective-intelligence": "leo",
    "teleological-economics": "leo",
    "cultural-dynamics": "clay",
 }
 DOMAIN_COLORS = {
    "internet-finance": "#4A90D9",
    "entertainment": "#9B59B6",
    "health": "#2ECC71",
    "ai-alignment": "#E74C3C",
    "space-development": "#F39C12",
    "grand-strategy": "#D4AF37",
    "mechanisms": "#1ABC9C",
    "living-capital": "#3498DB",
    "living-agents": "#E67E22",
    "teleohumanity": "#F1C40F",
    "critical-systems": "#95A5A6",
    "collective-intelligence": "#BDC3C7",
    "teleological-economics": "#7F8C8D",
    "cultural-dynamics": "#C0392B",
 }
 KNOWN_AGENTS = {"leo", "rio", "clay", "vida", "theseus", "astra"}
 # Regex patterns
 FRONTMATTER_RE = re.compile(r"^---\s*\n(.*?)\n---", re.DOTALL)
 WIKILINK_RE = re.compile(r"\[\[([^\]]+)\]\]")
 YAML_FIELD_RE = re.compile(r"^(\w[\w_]*):\s*(.+)$", re.MULTILINE)
 YAML_LIST_ITEM_RE = re.compile(r'^\s*-\s+"?(.+?)"?\s*$', re.MULTILINE)
 COUNTER_EVIDENCE_RE = re.compile(r"^##\s+Counter[\s-]?evidence", re.MULTILINE | re.IGNORECASE)
 COUNTERARGUMENT_RE = re.compile(r"^\*\*Counter\s*argument", re.MULTILINE | re.IGNORECASE)
 # ---------------------------------------------------------------------------
 # Lightweight YAML-ish frontmatter parser (avoids PyYAML dependency)
 # ---------------------------------------------------------------------------
 def parse_frontmatter(text: str) -> dict:
    """Parse YAML frontmatter from markdown text. Returns dict of fields."""
    m = FRONTMATTER_RE.match(text)
    if not m:
        return {}
    yaml_block = m.group(1)
    result = {}
    for field_match in YAML_FIELD_RE.finditer(yaml_block):
        key = field_match.group(1)
        val = field_match.group(2).strip().strip('"').strip("'")
        # Handle list fields
        if val.startswith("["):
            # Inline YAML list: [item1, item2]
            items = re.findall(r'"([^"]+)"', val)
            if not items:
                items = [x.strip().strip('"').strip("'")
                         for x in val.strip("[]").split(",") if x.strip()]
            result[key] = items
        else:
            result[key] = val
    # Handle multi-line list fields (depends_on, challenged_by, secondary_domains)
    for list_key in ("depends_on", "challenged_by", "secondary_domains", "claims_extracted"):
        if list_key not in result:
            # Check for block-style list
            pattern = re.compile(
                rf"^{list_key}:\s*\n((?:\s+-\s+.+\n?)+)", re.MULTILINE
            )
            lm = pattern.search(yaml_block)
            if lm:
                items = YAML_LIST_ITEM_RE.findall(lm.group(1))
                result[list_key] = [i.strip('"').strip("'") for i in items]
    return result
 def extract_body(text: str) -> str:
    """Return the markdown body after frontmatter."""
    m = FRONTMATTER_RE.match(text)
    if m:
        return text[m.end():]
    return text
 # ---------------------------------------------------------------------------
 # Git-based agent attribution
 # ---------------------------------------------------------------------------
 def build_git_agent_map(repo_root: str) -> dict[str, str]:
    """Map file paths → agent name using git log commit message prefixes.
    Commit messages follow: '{agent}: description'
    We use the commit that first added each file.
    """
    file_agent = {}
    try:
        result = subprocess.run(
            ["git", "log", "--all", "--diff-filter=A", "--name-only",
             "--format=COMMIT_MSG:%s"],
            capture_output=True, text=True, cwd=repo_root, timeout=30,
        )
        current_agent = None
        for line in result.stdout.splitlines():
            line = line.strip()
            if not line:
                continue
            if line.startswith("COMMIT_MSG:"):
                msg = line[len("COMMIT_MSG:"):]
                # Parse "agent: description" pattern
                if ":" in msg:
                    prefix = msg.split(":")[0].strip().lower()
                    if prefix in KNOWN_AGENTS:
                        current_agent = prefix
                    else:
                        current_agent = None
                else:
                    current_agent = None
            elif current_agent and line.endswith(".md"):
                # Only set if not already attributed (first add wins)
                if line not in file_agent:
                    file_agent[line] = current_agent
    except (subprocess.TimeoutExpired, FileNotFoundError):
        pass
    return file_agent
 # ---------------------------------------------------------------------------
 # Wiki-link resolution
 # ---------------------------------------------------------------------------
 def build_title_index(all_files: list[str], repo_root: str) -> dict[str, str]:
    """Map lowercase claim titles → file paths for wiki-link resolution."""
    index = {}
    for fpath in all_files:
        # Title = filename without .md extension
        fname = os.path.basename(fpath)
        if fname.endswith(".md"):
            title = fname[:-3].lower()
            index[title] = fpath
        # Also index by relative path
        index[fpath.lower()] = fpath
    return index
 def resolve_wikilink(link_text: str, title_index: dict, source_dir: str) -> str | None:
    """Resolve a [[wiki-link]] target to a file path (node ID)."""
    text = link_text.strip()
    # Skip map links and non-claim references
    if text.startswith("_") or text == "_map":
        return None
    # Direct path match (with or without .md)
    for candidate in [text, text + ".md"]:
        if candidate.lower() in title_index:
            return title_index[candidate.lower()]
    # Title-only match
    title = text.lower()
    if title in title_index:
        return title_index[title]
    # Fuzzy: try adding .md to the basename
    basename = os.path.basename(text)
    if basename.lower() in title_index:
        return title_index[basename.lower()]
    return None
 # ---------------------------------------------------------------------------
 # PR/merge event extraction from git log
 # ---------------------------------------------------------------------------
 def extract_events(repo_root: str) -> list[dict]:
    """Extract PR merge events from git log for the events timeline."""
    events = []
    try:
        result = subprocess.run(
            ["git", "log", "--merges", "--format=%H|%s|%ai", "-50"],
            capture_output=True, text=True, cwd=repo_root, timeout=15,
        )
        for line in result.stdout.strip().splitlines():
            parts = line.split("|", 2)
            if len(parts) < 3:
                continue
            sha, msg, date_str = parts
            # Parse "Merge pull request #N from ..." or agent commit patterns
            pr_match = re.search(r"#(\d+)", msg)
            if not pr_match:
                continue
            pr_num = int(pr_match.group(1))
            # Try to determine agent from merge commit
            agent = "collective"
            for a in KNOWN_AGENTS:
                if a in msg.lower():
                    agent = a
                    break
            # Count files changed in this merge
            diff_result = subprocess.run(
                ["git", "diff", "--name-only", f"{sha}^..{sha}"],
                capture_output=True, text=True, cwd=repo_root, timeout=10,
            )
            claims_added = sum(
                1 for f in diff_result.stdout.splitlines()
                if f.endswith(".md") and any(f.startswith(d) for d in SCAN_DIRS)
            )
            if claims_added > 0:
                events.append({
                    "type": "pr-merge",
                    "number": pr_num,
                    "agent": agent,
                    "claims_added": claims_added,
                    "date": date_str[:10],
                })
    except (subprocess.TimeoutExpired, FileNotFoundError):
        pass
    return events
 # ---------------------------------------------------------------------------
 # Main extraction
 # ---------------------------------------------------------------------------
 def find_markdown_files(repo_root: str) -> list[str]:
    """Find all .md files in SCAN_DIRS, return relative paths."""
    files = []
    for scan_dir in SCAN_DIRS:
        dirpath = os.path.join(repo_root, scan_dir)
        if not os.path.isdir(dirpath):
            continue
        for root, _dirs, filenames in os.walk(dirpath):
            for fname in filenames:
                if fname.endswith(".md") and not fname.startswith("_"):
                    rel = os.path.relpath(os.path.join(root, fname), repo_root)
                    files.append(rel)
    return sorted(files)
 def _get_domain_cached(fpath: str, repo_root: str, cache: dict) -> str:
    """Get the domain of a file, caching results."""
    if fpath in cache:
        return cache[fpath]
    abs_path = os.path.join(repo_root, fpath)
    domain = ""
    try:
        text = open(abs_path, encoding="utf-8").read()
        fm = parse_frontmatter(text)
        domain = fm.get("domain", "")
    except (OSError, UnicodeDecodeError):
        pass
    cache[fpath] = domain
    return domain
 def extract_graph(repo_root: str) -> dict:
    """Extract the full knowledge graph from the codex."""
    all_files = find_markdown_files(repo_root)
    git_agents = build_git_agent_map(repo_root)
    title_index = build_title_index(all_files, repo_root)
    domain_cache: dict[str, str] = {}
    nodes = []
    edges = []
    node_ids = set()
    all_files_set = set(all_files)
    for fpath in all_files:
        abs_path = os.path.join(repo_root, fpath)
        try:
            text = open(abs_path, encoding="utf-8").read()
        except (OSError, UnicodeDecodeError):
            continue
        fm = parse_frontmatter(text)
        body = extract_body(text)
        # Filter by type
        ftype = fm.get("type")
        if ftype and ftype not in INCLUDE_TYPES:
            continue
        # Build node
        title = os.path.basename(fpath)[:-3]  # filename without .md
        domain = fm.get("domain", "")
        if not domain:
            # Infer domain from directory path
            parts = fpath.split(os.sep)
            if len(parts) >= 2:
                domain = parts[1] if parts[0] == "domains" else parts[1] if len(parts) > 2 else parts[0]
        # Agent attribution: git log → domain mapping → "collective"
        agent = git_agents.get(fpath, "")
        if not agent:
            agent = DOMAIN_AGENT_MAP.get(domain, "collective")
        created = fm.get("created", "")
        confidence = fm.get("confidence", "speculative")
        # Detect challenged status
        challenged_by_raw = fm.get("challenged_by", [])
        if isinstance(challenged_by_raw, str):
            challenged_by_raw = [challenged_by_raw] if challenged_by_raw else []
        has_challenged_by = bool(challenged_by_raw and any(c for c in challenged_by_raw))
        has_counter_section = bool(COUNTER_EVIDENCE_RE.search(body) or COUNTERARGUMENT_RE.search(body))
        is_challenged = has_challenged_by or has_counter_section
        # Extract challenge descriptions for the node
        challenges = []
        if isinstance(challenged_by_raw, list):
            for c in challenged_by_raw:
                if c and isinstance(c, str):
                    # Strip wiki-link syntax for display
                    cleaned = WIKILINK_RE.sub(lambda m: m.group(1), c)
                    # Strip markdown list artifacts: leading "- ", surrounding quotes
                    cleaned = re.sub(r'^-\s*', '', cleaned).strip()
                    cleaned = cleaned.strip('"').strip("'").strip()
                    if cleaned:
                        challenges.append(cleaned[:200])  # cap length
        node = {
            "id": fpath,
            "title": title,
            "domain": domain,
            "agent": agent,
            "created": created,
            "confidence": confidence,
            "challenged": is_challenged,
        }
        if challenges:
            node["challenges"] = challenges
        nodes.append(node)
        node_ids.add(fpath)
        domain_cache[fpath] = domain  # cache for edge lookups
        for link_text in WIKILINK_RE.findall(body):
            target = resolve_wikilink(link_text, title_index, os.path.dirname(fpath))
            if target and target != fpath and target in all_files_set:
                target_domain = _get_domain_cached(target, repo_root, domain_cache)
                edges.append({
                    "source": fpath,
                    "target": target,
                    "type": "wiki-link",
                    "cross_domain": domain != target_domain and bool(target_domain),
                })
        # Conflict edges from challenged_by (may contain [[wiki-links]] or prose)
        challenged_by = fm.get("challenged_by", [])
        if isinstance(challenged_by, str):
            challenged_by = [challenged_by]
        if isinstance(challenged_by, list):
            for challenge in challenged_by:
                if not challenge:
                    continue
                # Check for embedded wiki-links
                for link_text in WIKILINK_RE.findall(challenge):
                    target = resolve_wikilink(link_text, title_index, os.path.dirname(fpath))
                    if target and target != fpath and target in all_files_set:
                        target_domain = _get_domain_cached(target, repo_root, domain_cache)
                        edges.append({
                            "source": fpath,
                            "target": target,
                            "type": "conflict",
                            "cross_domain": domain != target_domain and bool(target_domain),
                        })
    # Deduplicate edges
    seen_edges = set()
    unique_edges = []
    for e in edges:
        key = (e["source"], e["target"], e.get("type", ""))
        if key not in seen_edges:
            seen_edges.add(key)
            unique_edges.append(e)
    # Only keep edges where both endpoints exist as nodes
    edges_filtered = [
        e for e in unique_edges
        if e["source"] in node_ids and e["target"] in node_ids
    ]
    events = extract_events(repo_root)
    return {
        "nodes": nodes,
        "edges": edges_filtered,
        "events": sorted(events, key=lambda e: e.get("date", "")),
        "domain_colors": DOMAIN_COLORS,
    }
 def build_claims_context(repo_root: str, nodes: list[dict]) -> dict:
    """Build claims-context.json for chat system prompt injection.
    Produces a lightweight claim index: title + description + domain + agent + confidence.
    Sorted by domain, then alphabetically within domain.
    Target: ~37KB for ~370 claims. Truncates descriptions at 100 chars if total > 100KB.
    """
    claims = []
    for node in nodes:
        fpath = node["id"]
        abs_path = os.path.join(repo_root, fpath)
        description = ""
        try:
            text = open(abs_path, encoding="utf-8").read()
            fm = parse_frontmatter(text)
            description = fm.get("description", "")
        except (OSError, UnicodeDecodeError):
            pass
        claims.append({
            "title": node["title"],
            "description": description,
            "domain": node["domain"],
            "agent": node["agent"],
            "confidence": node["confidence"],
        })
    # Sort by domain, then title
    claims.sort(key=lambda c: (c["domain"], c["title"]))
    context = {
        "generated": datetime.now(tz=timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
        "claimCount": len(claims),
        "claims": claims,
    }
    # Progressive description truncation if over 100KB.
    # Never drop descriptions entirely — short descriptions are better than none.
    for max_desc in (120, 100, 80, 60):
        test_json = json.dumps(context, ensure_ascii=False)
        if len(test_json) <= 100_000:
            break
        for c in claims:
            if len(c["description"]) > max_desc:
                c["description"] = c["description"][:max_desc] + "..."
    return context
 def main():
    parser = argparse.ArgumentParser(description="Extract graph data from teleo-codex")
    parser.add_argument("--output", "-o", default="graph-data.json",
                        help="Output file path (default: graph-data.json)")
    parser.add_argument("--context-output", "-c", default=None,
                        help="Output claims-context.json path (default: same dir as --output)")
    parser.add_argument("--repo", "-r", default=".",
                        help="Path to teleo-codex repo root (default: current dir)")
    args = parser.parse_args()
    repo_root = os.path.abspath(args.repo)
    if not os.path.isdir(os.path.join(repo_root, "core")):
        print(f"Error: {repo_root} doesn't look like a teleo-codex repo (no core/ dir)", file=sys.stderr)
        sys.exit(1)
    print(f"Scanning {repo_root}...")
    graph = extract_graph(repo_root)
    print(f"  Nodes: {len(graph['nodes'])}")
    print(f"  Edges: {len(graph['edges'])}")
    print(f"  Events: {len(graph['events'])}")
    challenged_count = sum(1 for n in graph["nodes"] if n.get("challenged"))
    print(f"  Challenged: {challenged_count}")
    # Write graph-data.json
    output_path = os.path.abspath(args.output)
    with open(output_path, "w", encoding="utf-8") as f:
        json.dump(graph, f, indent=2, ensure_ascii=False)
    size_kb = os.path.getsize(output_path) / 1024
    print(f"  graph-data.json: {output_path} ({size_kb:.1f} KB)")
    # Write claims-context.json
    context_path = args.context_output
    if not context_path:
        context_path = os.path.join(os.path.dirname(output_path), "claims-context.json")
    context_path = os.path.abspath(context_path)
    context = build_claims_context(repo_root, graph["nodes"])
    with open(context_path, "w", encoding="utf-8") as f:
        json.dump(context, f, indent=2, ensure_ascii=False)
    ctx_kb = os.path.getsize(context_path) / 1024
    print(f"  claims-context.json: {context_path} ({ctx_kb:.1f} KB)")
 if __name__ == "__main__":
    main()
--- a/ops/research-session.sh
+++ b/ops/research-session.sh
@ -0,0 +1,368 @@
 #!/bin/bash
 # Run a self-directed research session for one agent.
 # Usage: ./research-session.sh <agent-name>
 # Example: ./research-session.sh clay
 #
 # What it does:
 #   1. Pulls latest tweets from the agent's network accounts (X API)
 #   2. Gives Claude the agent's identity, beliefs, and current KB state
 #   3. Agent picks a research direction and archives sources with notes
 #   4. Commits source archives to a branch, pushes, opens PR
 #   5. Extract cron picks up the unprocessed sources separately
 #
 # The researcher never extracts — a separate Claude instance does that.
 # This prevents motivated reasoning in extraction.
 set -euo pipefail
 AGENT="${1:?Usage: $0 <agent-name>}"
 REPO_DIR="/opt/teleo-eval/workspaces/research-${AGENT}"
 FORGEJO_URL="http://localhost:3000"
 FORGEJO_ADMIN_TOKEN=$(cat /opt/teleo-eval/secrets/forgejo-admin-token)
 AGENT_TOKEN=$(cat "/opt/teleo-eval/secrets/forgejo-${AGENT}-token" 2>/dev/null || echo "$FORGEJO_ADMIN_TOKEN")
 TWITTER_API_KEY=$(cat /opt/teleo-eval/secrets/twitterapi-io-key)
 CLAUDE_BIN="/home/teleo/.local/bin/claude"
 LOG_DIR="/opt/teleo-eval/logs"
 LOG="$LOG_DIR/research-${AGENT}.log"
 LOCKFILE="/tmp/research-${AGENT}.lock"
 DATE=$(date +%Y-%m-%d)
 BRANCH="${AGENT}/research-${DATE}"
 RAW_DIR="/opt/teleo-eval/research-raw/${AGENT}"
 log() { echo "[$(date -Iseconds)] $*" >> "$LOG"; }
 # --- Lock (prevent concurrent sessions for same agent) ---
 if [ -f "$LOCKFILE" ]; then
    pid=$(cat "$LOCKFILE" 2>/dev/null)
    if kill -0 "$pid" 2>/dev/null; then
        log "SKIP: research session already running for $AGENT (pid $pid)"
        exit 0
    fi
    log "WARN: stale lockfile for $AGENT, removing"
    rm -f "$LOCKFILE"
 fi
 echo $$ > "$LOCKFILE"
 TWEET_FILE="/tmp/research-tweets-${AGENT}.md"
 trap 'rm -f "$LOCKFILE" "$TWEET_FILE"' EXIT
 log "=== Starting research session for $AGENT ==="
 # --- Ensure directories ---
 mkdir -p "$RAW_DIR" "$LOG_DIR"
 # --- Clone or update repo ---
 if [ ! -d "$REPO_DIR/.git" ]; then
    log "Cloning repo for $AGENT research..."
    git -c http.extraHeader="Authorization: token $FORGEJO_ADMIN_TOKEN" \
        clone "${FORGEJO_URL}/teleo/teleo-codex.git" "$REPO_DIR" >> "$LOG" 2>&1
 fi
 cd "$REPO_DIR"
 git config credential.helper "!f() { echo username=m3taversal; echo password=$FORGEJO_ADMIN_TOKEN; }; f"
 git remote set-url origin "${FORGEJO_URL}/teleo/teleo-codex.git" 2>/dev/null || true
 git checkout main >> "$LOG" 2>&1
 git pull --rebase >> "$LOG" 2>&1
 # --- Map agent to domain ---
 case "$AGENT" in
    rio) DOMAIN="internet-finance" ;;
    clay) DOMAIN="entertainment" ;;
    theseus) DOMAIN="ai-alignment" ;;
    vida) DOMAIN="health" ;;
    astra) DOMAIN="space-development" ;;
    leo) DOMAIN="grand-strategy" ;;
    *) log "ERROR: Unknown agent $AGENT"; exit 1 ;;
 esac
 # --- Pull tweets from agent's network ---
 # Check if agent has a network file in the repo
 NETWORK_FILE="agents/${AGENT}/network.json"
 if [ ! -f "$NETWORK_FILE" ]; then
    log "No network file at $NETWORK_FILE — agent will use KB context to decide what to research"
    TWEET_DATA=""
 else
    log "Pulling tweets from ${AGENT}'s network..."
    ACCOUNTS=$(python3 -c "
 import json
 with open('$NETWORK_FILE') as f:
    data = json.load(f)
 for acct in data.get('accounts', []):
    if acct.get('tier') in ('core', 'extended'):
        print(acct['username'])
 " 2>/dev/null || true)
    TWEET_DATA=""
    API_CALLS=0
    API_CACHED=0
    for USERNAME in $ACCOUNTS; do
        # Validate username (Twitter handles are alphanumeric + underscore only)
        if [[ ! "$USERNAME" =~ ^[a-zA-Z0-9_]+$ ]]; then
            log "WARN: Invalid username '$USERNAME' in network file, skipping"
            continue
        fi
        OUTFILE="$RAW_DIR/${USERNAME}.json"
        # Only pull if file doesn't exist or is older than 12 hours
        if [ ! -f "$OUTFILE" ] || [ $(find "$OUTFILE" -mmin +720 2>/dev/null | wc -l) -gt 0 ]; then
            log "Pulling @${USERNAME}..."
            curl -s "https://api.twitterapi.io/twitter/user/last_tweets?userName=${USERNAME}" \
                -H "X-API-Key: ${TWITTER_API_KEY}" \
                -o "$OUTFILE" 2>/dev/null || {
                log "WARN: Failed to pull @${USERNAME}"
                continue
            }
            API_CALLS=$((API_CALLS + 1))
            sleep 2  # Rate limit courtesy
        else
            API_CACHED=$((API_CACHED + 1))
        fi
        if [ -f "$OUTFILE" ]; then
            TWEET_DATA="${TWEET_DATA}
 --- @${USERNAME} tweets ---
 $(python3 -c "
 import json, sys
 try:
    d = json.load(open('$OUTFILE'))
    tweets = d.get('tweets', d.get('data', []))
    for t in tweets[:20]:
        text = t.get('text', '')[:500]
        likes = t.get('likeCount', t.get('public_metrics', {}).get('like_count', 0))
        date = t.get('createdAt', t.get('created_at', 'unknown'))
        url = t.get('twitterUrl', t.get('url', ''))
        print(f'[{date}] ({likes} likes) {text}')
        print(f'  URL: {url}')
        print()
 except Exception as e:
    print(f'Error reading: {e}', file=sys.stderr)
 " 2>/dev/null || echo "(failed to parse)")"
        fi
    done
    log "API usage: ${API_CALLS} calls, ${API_CACHED} cached for ${AGENT}"
    # Append to cumulative usage log (create with header if new)
    USAGE_CSV="/opt/teleo-eval/logs/x-api-usage.csv"
    if [ ! -f "$USAGE_CSV" ]; then
        echo "date,agent,api_calls,cached,accounts_total" > "$USAGE_CSV"
    fi
    ACCOUNT_COUNT=$(echo "$ACCOUNTS" | wc -w | tr -d ' ')
    echo "${DATE},${AGENT},${API_CALLS},${API_CACHED},${ACCOUNT_COUNT}" >> "$USAGE_CSV"
 fi
 # --- Also check for any raw JSON dumps in inbox-raw ---
 INBOX_RAW="/opt/teleo-eval/inbox-raw/${AGENT}"
 if [ -d "$INBOX_RAW" ] && ls "$INBOX_RAW"/*.json 2>/dev/null | head -1 > /dev/null; then
    log "Found raw dumps in $INBOX_RAW"
    for RAWFILE in "$INBOX_RAW"/*.json; do
        USERNAME=$(basename "$RAWFILE" .json)
        TWEET_DATA="${TWEET_DATA}
 --- @${USERNAME} tweets (from raw dump) ---
 $(python3 -c "
 import json, sys
 try:
    d = json.load(open('$RAWFILE'))
    tweets = d.get('tweets', d.get('data', []))
    for t in tweets[:20]:
        text = t.get('text', '')[:500]
        likes = t.get('likeCount', t.get('public_metrics', {}).get('like_count', 0))
        date = t.get('createdAt', t.get('created_at', 'unknown'))
        url = t.get('twitterUrl', t.get('url', ''))
        print(f'[{date}] ({likes} likes) {text}')
        print(f'  URL: {url}')
        print()
 except Exception as e:
    print(f'Error: {e}', file=sys.stderr)
 " 2>/dev/null || echo "(failed to parse)")"
    done
 fi
 # --- Create branch ---
 git branch -D "$BRANCH" 2>/dev/null || true
 git checkout -b "$BRANCH" >> "$LOG" 2>&1
 log "On branch $BRANCH"
 # --- Build the research prompt ---
 # Write tweet data to a temp file so Claude can read it
 echo "$TWEET_DATA" > "$TWEET_FILE"
 RESEARCH_PROMPT="You are ${AGENT}, a Teleo knowledge base agent. Domain: ${DOMAIN}.
 ## Your Task: Self-Directed Research Session
 You have ~90 minutes of compute. Use it wisely.
 ### Step 1: Orient (5 min)
 Read these files to understand your current state:
 - agents/${AGENT}/identity.md (who you are)
 - agents/${AGENT}/beliefs.md (what you believe)
 - agents/${AGENT}/reasoning.md (how you think)
 - domains/${DOMAIN}/_map.md (your domain's current claims)
 ### Step 2: Review Recent Tweets (10 min)
 Read ${TWEET_FILE} — these are recent tweets from accounts in your domain.
 Scan for anything substantive: new claims, evidence, debates, data, counterarguments.
 ### Step 3: Check Previous Follow-ups (2 min)
 Read agents/${AGENT}/musings/ — look for any previous research-*.md files. If they exist, check the 'Follow-up Directions' section at the bottom. These are threads your past self flagged but didn't have time to cover. Give them priority when picking your direction.
 ### Step 4: Pick ONE Research Question (5 min)
 Pick ONE research question — not one topic, but one question that naturally spans multiple accounts and sources. 'How is capital flowing through Solana launchpads?' is one question even though it touches MetaDAO, SOAR, Futardio.
 **Direction selection priority** (active inference — pursue surprise, not confirmation):
 1. Follow-up ACTIVE THREADS from previous sessions (your past self flagged these)
 2. Claims rated 'experimental' or areas where the KB flags live tensions — highest uncertainty = highest learning value
 3. Evidence that CHALLENGES your beliefs, not confirms them
 4. Cross-domain connections flagged by other agents
 5. New developments that change the landscape
 Also read agents/${AGENT}/research-journal.md if it exists — this is your cross-session pattern tracker.
 Write a brief note explaining your choice to: agents/${AGENT}/musings/research-${DATE}.md
 ### Step 5: Archive Sources (60 min)
 For each relevant tweet/thread, create an archive file:
 Path: inbox/archive/YYYY-MM-DD-{author-handle}-{brief-slug}.md
 Use this frontmatter:
 ---
 type: source
 title: \"Descriptive title\"
 author: \"Display Name (@handle)\"
 url: https://original-url
 date: YYYY-MM-DD
 domain: ${DOMAIN}
 secondary_domains: []
 format: tweet | thread
 status: unprocessed
 priority: high | medium | low
 tags: [topic1, topic2]
 ---
 ## Content
 [Full text of tweet/thread]
 ## Agent Notes
 **Why this matters:** [1-2 sentences]
 **What surprised me:** [Anything unexpected — the extractor needs this to avoid confirming your priors]
 **What I expected but didn't find:** [Gaps or missing evidence you noticed]
 **KB connections:** [Which existing claims relate?]
 **Extraction hints:** [What claims might an extractor pull?]
 **Context:** [Who is the author, what debate is this part of?]
 ## Curator Notes (structured handoff for extractor)
 PRIMARY CONNECTION: [exact claim title this source most relates to]
 WHY ARCHIVED: [what pattern or tension this evidences]
 EXTRACTION HINT: [what the extractor should focus on — scopes attention]
 ### Step 5 Rules:
 - Archive EVERYTHING substantive, not just what supports your views
 - Set all sources to status: unprocessed (a DIFFERENT instance will extract)
 - Flag cross-domain sources with flagged_for_{agent}: [\"reason\"]
 - Do NOT extract claims yourself — write good notes so the extractor can
 - Check inbox/archive/ for duplicates before creating new archives
 - Aim for 5-15 source archives per session
 ### Step 6: Flag Follow-up Directions (5 min)
 At the bottom of your research musing (agents/${AGENT}/musings/research-${DATE}.md), add a section:
 ## Follow-up Directions
 Three categories — be specific, not vague:
 ### Active Threads (continue next session)
 - [Thread]: [What to do next, what you'd look for]
 ### Dead Ends (don't re-run these)
 - [What you searched for]: [Why it was empty — saves future you from wasting time]
 ### Branching Points (one finding opened multiple directions)
 - [Finding]: [Direction A vs Direction B — which to pursue first and why]
 ### Step 7: Update Research Journal (3 min)
 Append to agents/${AGENT}/research-journal.md (create if it doesn't exist). This is your cross-session memory — NOT the same as the musing.
 Format:
 ## Session ${DATE}
 **Question:** [your research question]
 **Key finding:** [most important thing you learned]
 **Pattern update:** [did this session confirm, challenge, or extend a pattern you've been tracking?]
 **Confidence shift:** [did any of your beliefs get stronger or weaker?]
 The journal accumulates session over session. After 5+ sessions, review it for cross-session patterns — when independent sources keep converging on the same observation, that's a claim candidate.
 ### Step 8: Stop
 When you've finished archiving sources, updating your musing, and writing the research journal entry, STOP. Do not try to commit or push — the script handles all git operations after you finish."
 # --- Run Claude research session ---
 log "Starting Claude research session..."
 timeout 5400 "$CLAUDE_BIN" -p "$RESEARCH_PROMPT" \
    --allowedTools 'Read,Write,Edit,Glob,Grep' \
    --model sonnet \
    --permission-mode bypassPermissions \
    >> "$LOG" 2>&1 || {
    log "WARN: Research session failed or timed out for $AGENT"
    git checkout main >> "$LOG" 2>&1
    exit 1
 }
 log "Claude session complete"
 # --- Check for changes ---
 CHANGED_FILES=$(git status --porcelain)
 if [ -z "$CHANGED_FILES" ]; then
    log "No sources archived by $AGENT"
    git checkout main >> "$LOG" 2>&1
    exit 0
 fi
 # --- Stage and commit ---
 git add inbox/archive/ agents/${AGENT}/musings/ agents/${AGENT}/research-journal.md 2>/dev/null || true
 if git diff --cached --quiet; then
    log "No valid changes to commit"
    git checkout main >> "$LOG" 2>&1
    exit 0
 fi
 AGENT_UPPER=$(echo "$AGENT" | sed 's/./\U&/')
 SOURCE_COUNT=$(git diff --cached --name-only | grep -c "^inbox/archive/" || echo "0")
 git commit -m "${AGENT}: research session ${DATE} — ${SOURCE_COUNT} sources archived
 Pentagon-Agent: ${AGENT_UPPER} <HEADLESS>" >> "$LOG" 2>&1
 # --- Push ---
 git push -u origin "$BRANCH" --force >> "$LOG" 2>&1
 log "Pushed $BRANCH"
 # --- Check for existing PR on this branch ---
 EXISTING_PR=$(curl -s "${FORGEJO_URL}/api/v1/repos/teleo/teleo-codex/pulls?state=open" \
    -H "Authorization: token $AGENT_TOKEN" \
    | jq -r ".[] | select(.head.ref == \"$BRANCH\") | .number" 2>/dev/null)
 if [ -n "$EXISTING_PR" ]; then
    log "PR already exists for $BRANCH (#$EXISTING_PR), skipping creation"
 else
    # --- Open PR ---
    PR_JSON=$(jq -n \
        --arg title "${AGENT}: research session ${DATE}" \
        --arg body "## Self-Directed Research
 Automated research session for ${AGENT} (${DOMAIN}).
 Sources archived with status: unprocessed — extract cron will handle claim extraction separately.
 Researcher and extractor are different Claude instances to prevent motivated reasoning." \
        --arg base "main" \
        --arg head "$BRANCH" \
        '{title: $title, body: $body, base: $base, head: $head}')
    PR_RESULT=$(curl -s -X POST "${FORGEJO_URL}/api/v1/repos/teleo/teleo-codex/pulls" \
        -H "Authorization: token $AGENT_TOKEN" \
        -H "Content-Type: application/json" \
        -d "$PR_JSON" 2>&1)
    PR_NUMBER=$(echo "$PR_RESULT" | jq -r '.number // "unknown"' 2>/dev/null || echo "unknown")
    log "PR #${PR_NUMBER} opened for ${AGENT}'s research session"
 fi
 # --- Back to main ---
 git checkout main >> "$LOG" 2>&1
 log "=== Research session complete for $AGENT ==="
--- a/ops/self-directed-research.md
+++ b/ops/self-directed-research.md
@ -0,0 +1,169 @@
 # Self-Directed Research Architecture
 Draft — Leo, 2026-03-10
 ## Core Idea
 Each agent gets a daily research session on the VPS. They autonomously pull tweets from their domain accounts, decide what's interesting, archive sources with notes, and push to inbox. A separate extraction cron (already running) picks up the archives and makes claims. The researcher never sees the extraction — preventing motivated reasoning.
 ## Why Separate Researcher and Extractor
 When the same agent researches and extracts, they prime themselves. The researcher finds a tweet they think supports a thesis → writes notes emphasizing that angle → extracts a claim that confirms the thesis. The extraction becomes a formality.
 Separation breaks this:
 - **Researcher** writes: "This tweet is about X, connects to Y, might challenge Z"
 - **Extractor** (different Claude instance, fresh context) reads the source and notes, extracts what's actually there
 - Neither has the other's context window or priming
 This mirrors our proposer-evaluator separation for claims, applied one layer earlier in the pipeline.
 ## Architecture
 ### Three cron stages on VPS
 ```
 ┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
 │  Research Cron   │────▶│   Extract Cron    │────▶│   Eval Pipeline │
 │  (daily, 2hr)   │     │   (every 5 min)   │     │   (webhook.py)  │
 │                  │     │                   │     │                 │
 │  Pull tweets     │     │  Read archives    │     │  Review claims  │
 │  Pick 1 task     │     │  Extract claims   │     │  Approve/reject │
 │  Archive sources │     │  Open PR          │     │  Merge          │
 │  Push branch+PR  │     │                   │     │                 │
 └─────────────────┘     └──────────────────┘     └─────────────────┘
 ```
 ### Research Cron: `research-session.sh`
 **Schedule:** Once daily, staggered across agents to respect rate limits
 ```
 # Stagger: each agent gets a 90-min window, overnight PST (10pm-7am)
 0  22 * * * /opt/teleo-eval/research-session.sh rio
 30 23 * * * /opt/teleo-eval/research-session.sh clay
 0   1 * * * /opt/teleo-eval/research-session.sh theseus
 30  2 * * * /opt/teleo-eval/research-session.sh vida
 0   4 * * * /opt/teleo-eval/research-session.sh astra
 30  5 * * * /opt/teleo-eval/research-session.sh leo
 ```
 **Per agent, the research session (~90 min):**
 1. Pull latest tweets from agent's network accounts (X API)
 2. Read the agent's beliefs, recent claims, open positions
 3. Claude prompt: "You are {agent}. Here are your latest tweets from {accounts}. Here is your current knowledge state. Pick ONE research direction that advances your domain understanding. Archive the most relevant sources with notes."
 4. Agent writes source archives to `inbox/archive/` with `status: unprocessed`
 5. Commit, push to branch, open PR (source-only, no claims)
 6. Extract cron picks them up within 5 minutes
 **Key constraint:** One Claude session per agent, ~90 minutes, Sonnet model. Total daily VPS research compute: ~9 hours of sequential Sonnet sessions (staggered overnight).
 ### Research Prompt Structure
 ```
 You are {agent}, a Teleo knowledge base agent specializing in {domain}.
 ## Your Current State
 {Read from agents/{agent}/beliefs.md, reasoning.md, positions/}
 ## Your Network
 {Read from network file — accounts to monitor}
 ## Recent Tweets
 {Raw tweet data pulled from X API}
 ## Your Task
 1. Scan these tweets for anything substantive — new claims, evidence,
   debates, data, counterarguments to existing KB positions
 2. Pick ONE research direction that would most advance your domain
   understanding right now. Consider:
   - Gaps in your beliefs that need evidence
   - Claims in the KB that might be wrong
   - Cross-domain connections you've been flagged about
   - New developments that change the landscape
 3. Archive the relevant sources (5-15 per session) following the
   inbox/archive format with full agent notes
 4. Write a brief research summary explaining what you found and why
   it matters
 ## Rules
 - Archive EVERYTHING substantive, not just what supports your views
 - Write honest agent notes — flag what challenges your beliefs too
 - Set all sources to status: unprocessed (a different instance extracts)
 - Flag cross-domain sources for other agents
 - Do NOT extract claims yourself — that's a separate process
 ```
 ### Capacity on Claude Max ($200/month)
 **VPS compute budget (all Sonnet):**
 - Research cron: 6 agents × 90 min/day = 9 hr/day (overnight)
 - Extract cron: ~37 sources × 10 min = 6 hr one-time backlog, then ~1 hr/day steady-state
 - Eval pipeline: ~10 PRs/day × 15 min = 2.5 hr/day
 - **Total VPS:** ~6.5 hr/day Sonnet (steady state)
 **Laptop compute budget (Opus + Sonnet mix):**
 - Agent sessions: 2-3 concurrent, ~4-6 hr/day
 - Leo coordination: ~1-2 hr/day
 **Single subscription feasibility:** Tight but workable if:
 - VPS runs overnight (2am-8am staggered research + continuous extraction)
 - Laptop agents run during the day
 - Never more than 2-3 concurrent sessions total
 - VPS uses Sonnet exclusively (cheaper rate limits)
 **Risk:** If rate limits tighten or daily message caps exist, the VPS research cron may not complete all 6 agents. Mitigation: priority ordering (run the 3 most active agents daily, others every 2-3 days).
 ## Contributor Workflow Options
 Different people want different levels of involvement:
 ### Mode 1: Full Researcher
 "I found this, here's why it matters, here are the KB connections"
 - Uses /ingest on laptop (Track A or B)
 - Writes detailed agent notes
 - May extract claims themselves
 - Highest quality input
 ### Mode 2: Curator
 "Here's a source, it's about X domain"
 - Minimal archive file with domain tag and brief notes
 - VPS extracts (Track B)
 - Good enough for most sources
 ### Mode 3: Raw Dump
 "Here are tweets, figure it out"
 - Dumps raw JSON to VPS inbox-raw/
 - Leo triages: decides domain, writes archive files
 - VPS extracts from Leo's archives
 - Lowest effort, decent quality (Leo's triage catches the important stuff)
 ### Mode 4: Self-Directed Agent (VPS)
 "Agent, go research your domain"
 - No human involvement beyond initial network setup
 - Daily cron pulls tweets, agent picks direction, archives, extraction follows
 - Quality depends on prompt engineering + eval pipeline catching errors
 All four modes feed into the same extraction → eval pipeline. Quality varies, but the eval pipeline is the quality gate regardless.
 ## Open Questions
 1. **Rate limits**: What are the actual Claude Max per-minute and per-day limits for headless Sonnet sessions? Need empirical data from this first extraction run.
 2. **Research quality**: Will a 30-minute Sonnet session produce good enough research notes? Or does research require Opus-level reasoning?
 3. **Network bootstrapping**: Agents need network files. Who curates the initial account lists? (Currently Cory + Leo, eventually agents propose additions)
 4. **Cross-domain routing**: When the research cron finds cross-domain content, should it archive under the researcher's domain or the correct domain? (Probably correct domain with flagged_for_{researcher})
 5. **Feedback loop**: How does extraction quality feed back to improve research notes? If the extractor consistently ignores certain types of notes, the researcher should learn.
 6. **Deduplication across agents**: Multiple agents may archive the same tweet (e.g., a Karpathy tweet relevant to both AI systems and collective intelligence). The extract cron needs to detect this.
 ## Implementation Order
 1. ✅ Extract cron (running now — validating extraction quality)
 2. **Next**: Research cron — daily self-directed sessions per agent
 3. **Then**: Raw dump path — Leo triage from JSON → archive
 4. **Later**: Full end-to-end with X API pull integrated into research cron
 5. **Eventually**: Feedback loops from eval quality → research prompt tuning
--- a/skills/ingest.md
+++ b/skills/ingest.md
@ -0,0 +1,201 @@
 # Skill: Ingest
 Research your domain, find source material, and archive it in inbox/. You choose whether to extract claims yourself or let the VPS handle it.
 **Archive everything.** The inbox is a library, not a filter. If it's relevant to any Teleo domain, archive it. Null-result sources (no extractable claims) are still valuable — they prevent duplicate work and build domain context.
 ## Usage
 ```
 /ingest                    # Research loop: pull tweets, find sources, archive with notes
 /ingest @username          # Pull and archive a specific X account's content
 /ingest url <url>          # Archive a paper, article, or thread from URL
 /ingest scan               # Scan your network for new content since last pull
 /ingest extract            # Extract claims from sources you've already archived (Track A)
 ```
 ## Two Tracks
 ### Track A: Agent-driven extraction (full control)
 You research, archive, AND extract. You see exactly what you're proposing before it goes up.
 1. Archive sources with `status: processing`
 2. Extract claims yourself using `skills/extract.md`
 3. Open a PR with both source archives and claim files
 4. Eval pipeline reviews your claims
 **Use when:** You're doing a deep dive on a specific topic, care about extraction quality, or want to control the narrative around new claims.
 ### Track B: VPS extraction (hands-off)
 You research and archive. The VPS extracts headlessly.
 1. Archive sources with `status: unprocessed`
 2. Push source-only PR (merges fast — no claim changes)
 3. VPS cron picks up unprocessed sources every 15 minutes
 4. Extracts claims via Claude headless, opens a separate PR
 5. Eval pipeline reviews the extraction
 **Use when:** You're batch-archiving many sources, the content is straightforward, or you want to focus your session time on research rather than extraction.
 ### The switch is the status field
 | Status | What happens |
 |--------|-------------|
 | `unprocessed` | VPS will extract (Track B) |
 | `processing` | You're handling it (Track A) — VPS skips this source |
 | `processed` | Already extracted — no further action |
 | `null-result` | Reviewed, no claims — no further action |
 You can mix tracks freely. Archive 10 sources as `unprocessed` for the VPS, then set 2 high-priority ones to `processing` and extract those yourself.
 ## Prerequisites
 - API key at `~/.pentagon/secrets/twitterapi-io-key`
 - Your network file at `~/.pentagon/workspace/collective/x-ingestion/{your-name}-network.json`
 - Forgejo token at `~/.pentagon/secrets/forgejo-{your-name}-token`
 ## The Loop
 ### Step 1: Research
 Find source material relevant to your domain. Sources include:
 - **X/Twitter** — tweets, threads, debates from your network accounts
 - **Papers** — academic papers, preprints, whitepapers
 - **Articles** — blog posts, newsletters, news coverage
 - **Reports** — industry reports, data releases, government filings
 - **Conversations** — podcast transcripts, interview notes, voicenote transcripts
 For X accounts, use `/x-research pull @{username}` to pull tweets, then scan for anything worth archiving. Don't just archive the "best" tweets — archive anything substantive. A thread arguing a wrong position is as valuable as one arguing a right one.
 ### Step 2: Archive with notes
 For each source, create an archive file on your branch:
 **Filename:** `inbox/archive/YYYY-MM-DD-{author-handle}-{brief-slug}.md`
 ```yaml
 ---
 type: source
 title: "Descriptive title of the content"
 author: "Display Name (@handle)"
 twitter_id: "numeric_id_from_author_object"  # X sources only
 url: https://original-url
 date: YYYY-MM-DD
 domain: internet-finance | entertainment | ai-alignment | health | space-development | grand-strategy
 secondary_domains: [other-domain]  # if cross-domain
 format: tweet | thread | essay | paper | whitepaper | report | newsletter | news | transcript
 status: unprocessed | processing    # unprocessed = VPS extracts; processing = you extract
 priority: high | medium | low
 tags: [topic1, topic2]
 flagged_for_rio: ["reason"]  # if relevant to another agent's domain
 ---
 ```
 **Body:** Include the full source text, then your research notes.
 ```markdown
 ## Content
 [Full text of tweet/thread/article. For long papers, include abstract + key sections.]
 ## Agent Notes
 **Why this matters:** [1-2 sentences — what makes this worth archiving]
 **KB connections:** [Which existing claims does this relate to, support, or challenge?]
 **Extraction hints:** [What claims might the extractor pull from this? Flag specific passages.]
 **Context:** [Anything the extractor needs to know — who the author is, what debate this is part of, etc.]
 ```
 The "Agent Notes" section is critical for Track B. The VPS extractor is good at mechanical extraction but lacks your domain context. Your notes guide it. For Track A, you still benefit from writing notes — they organize your thinking before extraction.
 ### Step 3: Extract claims (Track A only)
 If you set `status: processing`, follow `skills/extract.md`:
 1. Read the source completely
 2. Separate evidence from interpretation
 3. Extract candidate claims (specific, disagreeable, evidence-backed)
 4. Check for duplicates against existing KB
 5. Write claim files to `domains/{your-domain}/`
 6. Update source: `status: processed`, `processed_by`, `processed_date`, `claims_extracted`
 ### Step 4: Cross-domain flagging
 When you find sources outside your domain:
 - Archive them anyway (you're already reading them)
 - Set the `domain` field to the correct domain, not yours
 - Add `flagged_for_{agent}: ["brief reason"]` to frontmatter
 - Set `priority: high` if it's urgent or challenges existing claims
 ### Step 5: Branch, commit, push
 ```bash
 # Branch
 git checkout -b {your-name}/sources-{date}-{brief-slug}
 # Stage — sources only (Track B) or sources + claims (Track A)
 git add inbox/archive/*.md
 git add domains/{your-domain}/*.md  # Track A only
 # Commit
 git commit -m "{your-name}: archive {N} sources — {brief description}
 - What: {N} sources from {list of authors/accounts}
 - Domains: {which domains these cover}
 - Track: A (agent-extracted) | B (VPS extraction pending)
 Pentagon-Agent: {Name} <{UUID}>"
 # Push
 FORGEJO_TOKEN=$(cat ~/.pentagon/secrets/forgejo-{your-name}-token)
 git push -u https://{your-name}:${FORGEJO_TOKEN}@git.livingip.xyz/teleo/teleo-codex.git {branch-name}
 ```
 Open a PR:
 ```bash
 curl -s -X POST "https://git.livingip.xyz/api/v1/repos/teleo/teleo-codex/pulls" \
  -H "Authorization: token ${FORGEJO_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "{your-name}: {archive N sources | extract N claims} — {brief description}",
    "body": "## Sources\n{numbered list with titles and domains}\n\n## Claims (Track A only)\n{claim titles}\n\n## Track B sources (VPS extraction pending)\n{list of unprocessed sources}",
    "base": "main",
    "head": "{branch-name}"
  }'
 ```
 ## Network Management
 Your network file (`{your-name}-network.json`) lists X accounts to monitor:
 ```json
 {
  "agent": "your-name",
  "domain": "your-domain",
  "accounts": [
    {"username": "example", "tier": "core", "why": "Reason this account matters"},
    {"username": "example2", "tier": "extended", "why": "Secondary but useful"}
  ]
 }
 ```
 **Tiers:**
 - `core` — Pull every session. High signal-to-noise.
 - `extended` — Pull weekly or when specifically relevant.
 - `watch` — Pull once to evaluate, then promote or drop.
 Agents without a network file should create one as their first task. Start with 5-10 seed accounts.
 ## Quality Controls
 - **Archive everything substantive.** Don't self-censor. The extractor decides what yields claims.
 - **Write good notes.** Your domain context is the difference between a useful source and a pile of text.
 - **Check for duplicates.** Don't re-archive sources already in `inbox/archive/`.
 - **Flag cross-domain.** If you see something relevant to another agent, flag it — don't assume they'll find it.
 - **Log API costs.** Every X pull gets logged to `~/.pentagon/workspace/collective/x-ingestion/pull-log.jsonl`.
 - **Source diversity.** If you're archiving 10+ items from one account in a batch, note it — the extractor should be aware of monoculture risk.
Author	SHA1	Message	Date
Leo	215fa6aebb	Merge pull request 'clay: foundation claims — community formation + selfplex (6 claims)' (#64 ) from clay/foundation-cultural-dynamics into main Some checks are pending Sync Graph Data to teleo-app / sync (push) Waiting to run Details	2026-03-10 15:40:54 +00:00
m3taversal	833d810f21	clay: address PR #64 review — backfire effect, Putnam causality, source archives - Fix: soften backfire effect language in IPC claim — distinguish Kahan's robust finding (polarization increases with cognitive skill) from the contested backfire effect (Wood & Porter 2019, Guess & Coppock 2020 show minimal evidence) - Fix: qualify Putnam's TV causal claim as regression decomposition with contested causal interpretation - Add: cross-domain wiki links — Olson→alignment tax + voluntary pledges, IPC→AI alignment coordination + voluntary pledges - Add: 6 source archive stubs for canonical academic texts (Olson, Granovetter, Dunbar, Blackmore, Putnam, Kahan) Pentagon-Agent: Clay <D5A56E53-93FA-428D-8EC5-5BAC46E1B8C2>	2026-03-10 15:40:45 +00:00
Clay	41e6a3a515	clay: extract claims from 2026-01-15-advanced-television-audiences-ai-blurred-reality (#118 ) Co-authored-by: Clay <clay@agents.livingip.xyz> Co-committed-by: Clay <clay@agents.livingip.xyz>	2026-03-10 15:17:29 +00:00
Clay	ef5173e3c6	clay: extract claims from 2025-01-01-deloitte-hollywood-cautious-genai-adoption (#119 ) Co-authored-by: Clay <clay@agents.livingip.xyz> Co-committed-by: Clay <clay@agents.livingip.xyz>	2026-03-10 15:13:27 +00:00
Clay	e648f6ee1e	clay: extract claims from 2025-09-01-ankler-ai-studios-cheap-future-no-market (#120 ) Co-authored-by: Clay <clay@agents.livingip.xyz> Co-committed-by: Clay <clay@agents.livingip.xyz>	2026-03-10 15:09:26 +00:00
Rio	666b8da5bd	rio: extract claims from 2026-03-09-abbasshaikh-x-archive (#129 ) Co-authored-by: Rio <rio@agents.livingip.xyz> Co-committed-by: Rio <rio@agents.livingip.xyz>	2026-03-10 14:55:19 +00:00
Rio	a7067ca8de	rio: extract claims from 2026-03-09-flashtrade-x-archive (#130 ) Co-authored-by: Rio <rio@agents.livingip.xyz> Co-committed-by: Rio <rio@agents.livingip.xyz>	2026-03-10 14:51:18 +00:00
Rio	80efb3163e	rio: extract claims from 2026-03-09-richard-isc-x-archive (#127 ) Co-authored-by: Rio <rio@agents.livingip.xyz> Co-committed-by: Rio <rio@agents.livingip.xyz>	2026-03-10 14:45:15 +00:00
Clay	e13eb9cdee	clay: research session 2026-03-10 (#116 ) Co-authored-by: Clay <clay@agents.livingip.xyz> Co-committed-by: Clay <clay@agents.livingip.xyz>	2026-03-10 14:11:34 +00:00
Theseus	b5d78f2ba1	theseus: visitor-friendly _map.md polish for ai-alignment domain (#102 ) Some checks are pending Sync Graph Data to teleo-app / sync (push) Waiting to run Details Co-authored-by: Theseus <theseus@agents.livingip.xyz> Co-committed-by: Theseus <theseus@agents.livingip.xyz>	2026-03-10 12:12:25 +00:00
m3taversal	736c06bb80	Merge pull request 'leo: self-directed research architecture + Clay network' (#110 ) from leo/test-sources into main	2026-03-10 12:10:37 +00:00
m3taversal	1c6aab23bc	Auto: 2 files \| 2 files changed, 71 insertions(+), 45 deletions(-)	2026-03-10 12:03:40 +00:00
m3taversal	b1dafa2ca8	Auto: ops/research-session.sh \| 1 file changed, 3 insertions(+), 8 deletions(-)	2026-03-10 11:59:15 +00:00
m3taversal	0cbb142ed0	Auto: ops/research-session.sh \| 1 file changed, 1 insertion(+), 1 deletion(-)	2026-03-10 11:54:53 +00:00
m3taversal	e2eb38618c	Auto: agents/theseus/network.json \| 1 file changed, 21 insertions(+)	2026-03-10 11:54:18 +00:00
m3taversal	150b663907	Auto: 2 files \| 2 files changed, 62 insertions(+), 12 deletions(-)	2026-03-10 11:54:09 +00:00
m3taversal	5f7c48a424	Auto: ops/research-session.sh \| 1 file changed, 19 insertions(+), 5 deletions(-)	2026-03-10 11:51:23 +00:00
m3taversal	ef76a89811	Auto: agents/clay/network.json \| 1 file changed, 7 insertions(+), 7 deletions(-)	2026-03-10 11:47:47 +00:00
m3taversal	3613f1d51e	Auto: agents/clay/network.json \| 1 file changed, 19 insertions(+)	2026-03-10 11:46:21 +00:00
m3taversal	e2703a276c	Auto: ops/research-session.sh \| 1 file changed, 304 insertions(+)	2026-03-10 11:42:54 +00:00
m3taversal	7c1bfe8eef	Auto: ops/self-directed-research.md \| 1 file changed, 169 insertions(+)	2026-03-10 11:36:41 +00:00
m3taversal	2a2a94635c	Merge pull request 'leo: 5 test source archives for VPS extraction pipeline' (#104 ) from leo/test-sources into main	2026-03-10 11:15:10 +00:00
m3taversal	d2beae7c2a	Auto: inbox/archive/2026-02-24-karpathy-clis-legacy-tech-agents.md \| 1 file changed, 30 insertions(+)	2026-03-10 11:14:12 +00:00
m3taversal	48998b64d6	Auto: inbox/archive/2026-02-25-karpathy-programming-changed-december.md \| 1 file changed, 28 insertions(+)	2026-03-10 11:14:12 +00:00
m3taversal	85f146ca94	Auto: inbox/archive/2026-02-27-karpathy-8-agent-research-org.md \| 1 file changed, 44 insertions(+)	2026-03-10 11:14:12 +00:00
m3taversal	533ee40d9d	Auto: inbox/archive/2026-03-08-karpathy-autoresearch-collaborative-agents.md \| 1 file changed, 47 insertions(+)	2026-03-10 11:14:12 +00:00
m3taversal	0226ffe9bd	Auto: inbox/archive/2026-03-04-theiaresearch-permissionless-metadao-launches.md \| 1 file changed, 39 insertions(+)	2026-03-10 11:14:12 +00:00
Leo	75f1709110	leo: add ingest skill — full X-to-claims pipeline (#103 ) Some checks are pending Sync Graph Data to teleo-app / sync (push) Waiting to run Details	2026-03-10 10:42:25 +00:00
Clay	ae66f37975	clay: visitor experience — agent lens selection, README, CONTRIBUTING overhaul (#79 ) Co-authored-by: Clay <clay@agents.livingip.xyz> Co-committed-by: Clay <clay@agents.livingip.xyz>	2026-03-09 22:51:48 +00:00