m3taversal c42b775f18 theseus: 6 collaboration taxonomy claims from X ingestion (karpathy, swyx, simonw, DrJimFan)

- What: 6 new claims + 4 X archive sources + _map.md update for collaboration taxonomy thread
- Claims: implementation-creativity gap, expertise as multiplier, capability-matched escalation,
  subagent hierarchy thesis, cognitive debt, accountability gap
- Sources: @karpathy (21 relevant/43 unique), @swyx (26/100), @simonw (25/60), @DrJimFan (2/22)
- Why: First batch of Thread 1 (Human-AI Collaboration Taxonomy) from AI capability evidence
  research program. Practitioner-observed patterns from production AI use complement the
  academic Claude's Cycles evidence already in the KB.
- All archives include tweet handle + status ID for traceability
- All 15 wiki links verified — 0 broken

Pentagon-Agent: Theseus <25B96405-E50F-45ED-9C92-D8046DFAAD00>

2026-03-09 16:10:13 +00:00

2.3 KiB

Raw Blame History

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@DrJimFan X archive — 100 most recent tweets

Jim Fan (@DrJimFan), NVIDIA GEAR Lab

https://x.com/DrJimFan

2026-03-09

ai-alignment

processed

theseus

2026-03-09

embodied-ai

robotics

human-data-scaling

motor-control

theseus-x-collab-taxonomy-2026-03

Very thin for collaboration taxonomy claims. Only 22 unique tweets out of 100 (78 duplicates from API pagination). Of 22 unique, only 2 are substantive — both NVIDIA robotics announcements (EgoScale, SONIC). The remaining 20 are congratulations, emoji reactions, and brief replies. EgoScale's "humans are the most scalable embodiment" thesis has alignment relevance but is primarily a robotics capability claim. No content on AI coding tools, multi-agent systems, collective intelligence, or formal verification. May yield claims in a future robotics-focused extraction pass.

@DrJimFan X Archive (Feb 20 – Mar 6, 2026)

Substantive Tweets

EgoScale: Human Video Pre-training for Robot Dexterity

(status/2026709304984875202, 1,686 likes): "We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop. Humans are the most scalable embodiment on the planet. We discovered a near-perfect log-linear scaling law (R^2 = 0.998) between human video volume and action prediction loss [...] Most surprising result: a single teleop demo is sufficient to learn a never-before-seen task."

SONIC: 42M Transformer for Humanoid Whole-Body Control

(status/2026350142652383587, 1,514 likes): "What can half of GPT-1 do? We trained a 42M transformer called SONIC to control the body of a humanoid robot. [...] We scaled humanoid motion RL to an unprecedented scale: 100M+ mocap frames and 500,000+ parallel robots across 128 GPUs. [...] After 3 days of training, the neural net transfers zero-shot to the real G1 robot with no finetuning. 100% success rate across 50 diverse real-world motion sequences."

Filtered Out

~20 tweets: congratulations, emoji reactions, "OSS ftw!!", thanks, team shoutouts.

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