leo: archive 19 tweet sources on AI agents, memory, and harnesses
- What: Source archives for tweets by Karpathy, Teknium, Emollick, Gauri Gupta, Alex Prompter, Jerry Liu, Sarah Wooders, and others on LLM knowledge bases, agent harnesses, self-improving systems, and memory architecture - Why: Persisting raw source material for pipeline extraction. 4 sources already processed by Rio's batch (karpathy-gist, kevin-gu, mintlify, hyunjin-kim) were excluded as duplicates. - Status: all unprocessed, ready for overnight extraction pipeline Pentagon-Agent: Leo <D35C9237-A739-432E-A3DB-20D52D1577A9>
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24
inbox/archive/2026-04-03-branarakic-shared-context-graphs.md
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inbox/archive/2026-04-03-branarakic-shared-context-graphs.md
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---
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type: source
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title: "The Next Big Shift in AI Agents: Shared Context Graphs"
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author: "Brana Rakic (@BranaRakic)"
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url: "https://x.com/BranaRakic/status/2040159452431560995"
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date: 2026-04-03
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [context-graphs, knowledge-base, agents, convergence]
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---
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## Content
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Link to article: "The next big shift in AI agents: shared context graphs" - "Something interesting is converging. Karpathy is building personal knowledge bases with LLMs. Foundation Capital is writing about context graphs as the next..."
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327 likes, 10 replies.
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## Key Points
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- Identifies convergence between Karpathy's personal knowledge bases and context graph concepts
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- Shared context graphs proposed as the next major shift for AI agents
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- Connects Foundation Capital's writing on context graphs to the broader trend
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- Suggests a unified direction emerging from multiple independent developments
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---
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type: source
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title: "NotebookLM Video on Karpathy Post"
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author: "Emily (@IamEmily2050)"
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url: "https://x.com/IamEmily2050/status/2040007450141593925"
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date: 2026-04-03
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [notebooklm, karpathy-response, knowledge-base, video]
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---
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## Content
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NotebookLM video overview on Andrej post.
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1,173 likes, 22 replies. Video (~6 min) using NotebookLM to summarize Karpathy's knowledge base post.
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## Key Points
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- NotebookLM used to generate a video overview of Karpathy's LLM knowledge base post
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- Demonstrates using one AI tool (NotebookLM) to summarize another AI workflow
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- ~6 minute video summary
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---
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type: source
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title: "Filesystems Replace RAG"
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author: "Jerry Liu (@jerryjliu0)"
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url: "https://x.com/jerryjliu0/status/2040154840228323468"
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date: 2026-04-03
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [rag, filesystem, chromafs, mintlify, llamaindex, retrieval]
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---
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## Content
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This is a cool article that shows how to *actually* make filesystems + grep replace a naive RAG implementation. Database + virtual filesystem abstraction + grep is all you need
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780 likes, 28 replies. Includes image. Quotes Mintlify/ChromaFS article by Dens Sumesh. Jerry Liu is founder of LlamaIndex.
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## Key Points
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- Filesystems + grep can replace naive RAG implementations
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- Database + virtual filesystem abstraction + grep is sufficient
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- Endorsement from LlamaIndex founder of the filesystem-over-RAG approach
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- References Mintlify/ChromaFS article as practical demonstration
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---
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type: source
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title: "Towards Semantic Observability"
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author: "Leonard Tang (@leonardtang_)"
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url: "https://x.com/leonardtang_/status/2040122646197612557"
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date: 2026-04-03
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [observability, monitoring, ai-systems, infrastructure]
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---
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## Content
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Link to article: "Towards Semantic Observability" - discusses how traditional observability relies on knowing failure behaviors in advance.
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353 likes, 10 replies.
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## Key Points
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- Traditional observability assumes you know failure behaviors in advance
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- Proposes semantic observability as an alternative approach for AI systems
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- Addresses the challenge of monitoring systems with unpredictable failure modes
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24
inbox/archive/2026-04-03-omarsar0-llm-kb-system-diagram.md
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inbox/archive/2026-04-03-omarsar0-llm-kb-system-diagram.md
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type: source
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title: "LLM Knowledge Base System Diagram"
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author: "omarsar0 (@omarsar0)"
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url: "https://x.com/omarsar0/status/2040099881008652634"
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date: 2026-04-03
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [llm, knowledge-base, diagram, karpathy-response, visualization]
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---
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## Content
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Diagram of the LLM Knowledge Base system. Feed this to your favorite agent and get your own LLM knowledge base going.
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1,624 likes, 49 replies. Contains diagram image of Karpathy's 3-layer system.
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## Key Points
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- Provides a diagram of Karpathy's LLM Knowledge Base system architecture
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- 3-layer system design visualized
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- Designed to be fed to an agent to bootstrap your own knowledge base
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- Practical starter resource for implementing the pattern
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inbox/archive/2026-04-03-oprydai-become-a-generalist.md
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inbox/archive/2026-04-03-oprydai-become-a-generalist.md
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type: source
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title: "Become a Generalist"
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author: "oprydai (@oprydai)"
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url: "https://x.com/oprydai/status/2040130116022661243"
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date: 2026-04-03
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [generalism, cross-domain, innovation, patterns]
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---
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## Content
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become a generalist. specialization makes you efficient. generalization makes you dangerous. what it actually means: learn across domains -- math, physics, software, economics, biology. patterns repeat across fields. connect ideas -- innovation happens at the intersection
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5,115 likes, 210 replies. Includes attached image.
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## Key Points
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- Specialization makes you efficient but generalization makes you dangerous
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- Learning across domains (math, physics, software, economics, biology) reveals repeating patterns
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- Innovation happens at the intersection of ideas from different fields
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- Cross-domain pattern recognition is a key competitive advantage
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---
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type: source
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title: "Why Memory Isn't a Plugin (It's the Harness)"
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author: "Sarah Wooders (@sarahwooders)"
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url: "https://x.com/sarahwooders/status/2040121230473457921"
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date: 2026-04-03
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [memory, agent-harness, letta-ai, memgpt]
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---
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## Content
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Link to article: "Why memory isn't a plugin (it's the harness)" - discusses MemGPT/Letta AI's memory architecture. Argues memory should be the harness, not a plugin bolted on. Associated with Letta AI.
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316 likes, 10 replies.
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## Key Points
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- Memory should be the harness, not a plugin bolted onto an agent
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- Discusses MemGPT/Letta AI's memory architecture
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- Challenges the common pattern of treating memory as an add-on component
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- Positions memory as fundamental infrastructure rather than optional feature
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---
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type: source
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title: "Hermes Agent v0.7 Memory Deep Dive"
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author: "Teknium (@Teknium)"
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url: "https://x.com/Teknium/status/2040151297991770435"
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date: 2026-04-03
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [hermes-agent, nous-research, memory, interfaces, architecture]
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---
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## Content
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Deeper dive into some of the updates in v0.7. Memory: We have begun transitioning each of the systems in Hermes Agent to work through defined interfaces so that the core code is more maintainable, and more providers for everything can be supported. We started with memory:
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375 likes, 36 replies. Includes attached image of memory architecture. Quote of NousResearch announcement.
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## Key Points
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- Hermes Agent v0.7 transitions systems to work through defined interfaces
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- Interface-based architecture improves maintainability and extensibility
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- Memory system was the first to be refactored to this interface pattern
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- Enables support for multiple providers per system component
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---
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type: source
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title: "Stanford Meta-Harness: Biggest Performance Gap Is the Harness"
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author: "alex_prompter (@alex_prompter)"
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url: "https://x.com/alex_prompter/status/2040378405322113442"
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date: 2026-04-04
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [harness, meta-harness, stanford, agent-optimization, benchmark]
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---
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## Content
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Holy shit. Stanford just showed that the biggest performance gap in AI systems isn't the model it's the harness. The code wrapping the model. And they built a system that writes better harnesses automatically than humans can by hand. +7.7 points. 4x fewer tokens. #1 ranking
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613 likes, 32 replies. Contains research visualization image.
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## Key Points
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- Stanford research shows the harness (code wrapping the model) matters more than the model itself
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- Built a system that automatically writes better harnesses than human-crafted ones
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- Achieved +7.7 point improvement with 4x fewer tokens
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- Reached #1 ranking on benchmark
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- Key implication: optimizing the harness is higher leverage than optimizing the model
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---
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type: source
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title: "515 Startup Field Experiment on AI Adoption"
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author: "Ethan Mollick (@emollick)"
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url: "https://x.com/emollick/status/2040436307176898897"
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date: 2026-04-04
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [ai-adoption, startups, field-experiment, productivity, mapping-problem]
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---
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## Content
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Big deal paper here: field experiment on 515 startups, half shown case studies of how startups are successfully using AI. Those firms used AI 44% more, had 1.9x higher revenue, needed 39% less capital: 1) AI accelerates businesses 2) The challenge is understanding how to use it
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995 likes. Includes 2 images. Quotes Hyunjin Kim's paper on AI's "mapping problem" in firms.
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## Key Points
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- Field experiment on 515 startups showed significant AI adoption effects
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- Firms shown AI case studies used AI 44% more than control group
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- Treatment group had 1.9x higher revenue and needed 39% less capital
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- The main challenge is not AI capability but understanding how to use it
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- References the "mapping problem" -- discovering where AI creates value
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29
inbox/archive/2026-04-04-gauri_gupta-auto-harness-release.md
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inbox/archive/2026-04-04-gauri_gupta-auto-harness-release.md
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---
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type: source
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title: "auto-harness: Self-Improving Agentic Systems with Auto-Evals"
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author: "Gauri Gupta (@gauri__gupta)"
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url: "https://x.com/gauri__gupta/status/2040251309782409489"
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date: 2026-04-04
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [auto-harness, self-improving, auto-evals, open-source, agent-optimization]
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---
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## Content
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Releasing auto-harness: an open source library for our self improving agentic systems with auto-evals. We got a lot of responses from people wanting to try the self-improving loop on their own agent. So we open-sourced our setup. Connect your agent and let it cook over the...
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371 likes, 11 replies. Links to article about self-improving agentic systems.
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Additional tweet (https://x.com/gauri__gupta/status/2040251170099524025):
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Link to article: "auto-harness: Self improving agentic systems with auto-evals (open-sourced!)" - "a self-improving loop that finds your agent's failures, turns them into evals, and fixes them."
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1,100 likes, 15 replies.
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## Key Points
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- auto-harness is an open-source library for self-improving agentic systems
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- Implements a self-improving loop: find failures, turn them into evals, fix them
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- Open-sourced in response to community demand
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- Connect your own agent to the self-improving loop
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- Automatic evaluation generation from observed failures
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---
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type: source
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title: "6 Components of Coding Agents"
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author: "Hesamation (@Hesamation)"
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url: "https://x.com/Hesamation/status/2040453130324709805"
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date: 2026-04-04
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [coding-agents, harness, claude-code, components, architecture]
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---
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## Content
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this is a great article if you want to understand Claude Code or Codex and the main components of a coding agent: 'harness is often more important than the model'. LLM -> agent -> agent harness -> coding harness. there are 6 critical components: 1. repo context: git, readme, ...
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279 likes, 15 replies. Quote of Sebastian Raschka's article on coding agent components.
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## Key Points
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- Harness is often more important than the model in coding agents
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- Layered architecture: LLM -> agent -> agent harness -> coding harness
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- 6 critical components identified, starting with repo context (git, readme)
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- Applicable to understanding Claude Code and Codex architectures
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- References Sebastian Raschka's detailed article on the topic
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---
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type: source
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title: "Karpathy KB Architecture Visualization"
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author: "Himanshu (@himanshustwts)"
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url: "https://x.com/himanshustwts/status/2040477663387893931"
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date: 2026-04-04
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [llm, knowledge-base, architecture, visualization, karpathy-response]
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---
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## Content
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this is beautiful. basically a pattern for building personal knowledge bases using LLMs. and here is the architecture visualization of what karpathy says as 'idea file'. i think this is quite hackable / experimental and numerous things can be explored from here
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806 likes, 14 replies. Includes attached image visualization of the architecture.
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## Key Points
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- Provides an architecture visualization of Karpathy's LLM knowledge base pattern
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- Frames the pattern as hackable and experimental
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- Suggests numerous directions for exploration from this base pattern
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24
inbox/archive/2026-04-04-karpathy-epub-to-txt-via-agents.md
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inbox/archive/2026-04-04-karpathy-epub-to-txt-via-agents.md
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---
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type: source
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title: "EPUB to TXT via Agents"
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author: "Andrej Karpathy (@karpathy)"
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url: "https://x.com/karpathy/status/2040451573881737480"
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date: 2026-04-04
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [llm, agents, epub, conversion, karpathy]
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---
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## Content
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@trainable_nick The best epub to txt converter I found is just asking your favorite agent to do it. Epubs can be very diverse, the agent just goes in, figures it out, creates the output markdown and ensures it looks good works great.
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976 likes, 44 replies. Reply to trainable_nick about EPUB conversion tools.
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## Key Points
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- LLM agents can serve as the best EPUB to text converters
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- Agents handle the diversity of EPUB formats by figuring out structure dynamically
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- Agents can ensure output quality by reviewing their own work
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- Practical example of agents replacing specialized tooling
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inbox/archive/2026-04-04-karpathy-idea-files-llm-era.md
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inbox/archive/2026-04-04-karpathy-idea-files-llm-era.md
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---
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type: source
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title: "Idea Files for the LLM Era"
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author: "Andrej Karpathy (@karpathy)"
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url: "https://x.com/karpathy/status/2040470801506541998"
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date: 2026-04-04
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [llm, agents, idea-file, knowledge-sharing, karpathy]
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---
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## Content
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Wow, this tweet went very viral! I wanted share a possibly slightly improved version of the tweet in an 'idea file'. The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it.
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21,135 likes, 761 replies. Links to GitHub Gist "llm-wiki".
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## Key Points
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- In the LLM agent era, sharing ideas is more valuable than sharing specific code
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- "Idea files" allow others' agents to customize and build implementations
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- Follow-up to the viral LLM Knowledge Bases post
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- Links to a GitHub Gist called "llm-wiki" as an example idea file
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---
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type: source
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title: "Claude Code Skills Guide"
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author: "nyk (@nyk_builderz)"
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url: "https://x.com/nyk_builderz/status/2040391725391516065"
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date: 2026-04-04
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domain: ai-alignment
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format: tweet
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status: unprocessed
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tags: [claude-code, skills, agent-harness, prompt-engineering]
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---
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## Content
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If Claude keeps repeating the same mistakes, you don't need a longer prompt - you need a skill. I wrote a practical guide to building Claude Code skills that auto-invoke when relevant: SKILL.md structure, trigger design, allowed-tools safety, templates/examples
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42 likes, 4 replies. Links to article "Build Claude Code Skills: The full guide".
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Additional tweet (https://x.com/nyk_builderz/status/2040338207188062270):
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"Build Claude Code Skills: The full guide" - "Most Claude Code skill guides overcomplicate something that's actually simple. Here's the version that actually works."
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100 likes, 4 replies.
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## Key Points
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- Claude Code skills auto-invoke when relevant, replacing longer prompts
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- Guide covers SKILL.md structure, trigger design, and allowed-tools safety
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- Skills address repeating mistakes by encoding reusable patterns
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- Practical templates and examples provided
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24
inbox/archive/2026-04-04-sudoingx-hermes-agent-v07-memory.md
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inbox/archive/2026-04-04-sudoingx-hermes-agent-v07-memory.md
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---
|
||||
type: source
|
||||
title: "Hermes Agent v0.7 Pluggable Memory"
|
||||
author: "sudoingX (@sudoingX)"
|
||||
url: "https://x.com/sudoingX/status/2040408975246856569"
|
||||
date: 2026-04-04
|
||||
domain: ai-alignment
|
||||
format: tweet
|
||||
status: unprocessed
|
||||
tags: [hermes-agent, nous-research, memory, pluggable-architecture]
|
||||
---
|
||||
|
||||
## Content
|
||||
|
||||
holy shit hermes agent v0.7.0 just dropped and your memory is now fully pluggable. 7 providers out of the box from cloud to local sqlite. don't like any of them? build your own and plug it in. credential pools. multiple API keys per provider with automatic rotation. key gets...
|
||||
|
||||
166 likes, 9 replies. Quote of Teknium's post about Hermes Agent v0.7.
|
||||
|
||||
## Key Points
|
||||
|
||||
- Hermes Agent v0.7.0 introduces fully pluggable memory with 7 providers
|
||||
- Memory providers range from cloud to local SQLite
|
||||
- Custom memory providers can be built and plugged in
|
||||
- Credential pools with automatic API key rotation added
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
---
|
||||
type: source
|
||||
title: "EPUB to Markdown Tool"
|
||||
author: "trainable_nick (@trainable_nick)"
|
||||
url: "https://x.com/trainable_nick/status/2040448094060343337"
|
||||
date: 2026-04-04
|
||||
domain: ai-alignment
|
||||
format: tweet
|
||||
status: unprocessed
|
||||
tags: [epub, markdown, vibe-coding, knowledge-base, tool]
|
||||
---
|
||||
|
||||
## Content
|
||||
|
||||
As I pulled on the thread from Karpathy's post, I realized the existing EPUB to TXT tools were still too ugly and clunky for turning DRM-free books into clean markdown. So I made my own. I've only been vibe coding for a few months, and this is my first App Store Connect
|
||||
|
||||
239 likes, 11 replies. Includes image. Quote of Karpathy's KB post.
|
||||
|
||||
## Key Points
|
||||
|
||||
- Existing EPUB to TXT tools were insufficient for clean markdown output
|
||||
- Built a new tool specifically for converting DRM-free books to clean markdown
|
||||
- Inspired directly by Karpathy's LLM knowledge base workflow
|
||||
- Creator's first App Store Connect submission, built via vibe coding
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
---
|
||||
type: source
|
||||
title: "Karpathy's LLM Wiki Pattern"
|
||||
author: "Yuchen J (@Yuchenj_UW)"
|
||||
url: "https://x.com/Yuchenj_UW/status/2040482771576197377"
|
||||
date: 2026-04-04
|
||||
domain: ai-alignment
|
||||
format: tweet
|
||||
status: unprocessed
|
||||
tags: [llm, knowledge-base, wiki, karpathy-response]
|
||||
---
|
||||
|
||||
## Content
|
||||
|
||||
Karpathy's 'LLM Wiki' pattern: stop using LLMs as search engines over your docs. Use them as tireless knowledge engineers who compile, cross-reference, and maintain a living wiki. Humans curate and think.
|
||||
|
||||
1,352 likes, 45 replies. Includes a diagram generated by Claude agent.
|
||||
|
||||
## Key Points
|
||||
|
||||
- Reframes LLM usage from search engine to knowledge engineer
|
||||
- LLMs should compile, cross-reference, and maintain living wikis
|
||||
- Humans retain the curation and thinking roles
|
||||
- Distillation of Karpathy's LLM Knowledge Base workflow
|
||||
Loading…
Reference in a new issue