Building an AI Operating System
summary
Defines an AI operating system as a horizontal API layer that stitches LLMs, narrow models and external services into task-specific agents, outlines a four‑step build loop: research → discovery → development → scaling, cites Karpathy's OS metaphor and Claude/Gemini tool use (Claude claims 300 tools simultaneously), shows a concrete demo: Gemini 1.5 Pro converts a 21m30s workout video into JSON with detected exercises, weights and two‑sentence form critiques, and recommends using RAG, LLaMA/GPT APIs and narrow ML for cheap, testable predictions.
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chapters (16)
- 0:00AI operating system concept
- 1:53Karpathy's AI OS vision
- 4:01Gaps in AI OS adoption
- 5:46AI-native applications and API interconnectivity
- 7:53Human-AI interaction
- 9:18Narrow vs general AI
- 11:27Agentic AI behavior
- 13:36Gemini 1.5 Pro example
- 16:44LLM emergent capabilities
- 19:16AI's future revolution
- 20:19Cost of intelligence impact
- 23:08AI-native startup framework
- 26:21Mapping work to AI
- 28:50Building AI-native apps
- 30:39AI safety architecture
- 31:39Q&A
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