Hundreds of builders just admitted LLMs don't think. Their fix: manually writing thinking architecture files (PROGRESS.md, DECISIONS.md, IDENTITY.md) for every project. Most AI advice still chases better prompts. The real work moved up a layer. Here's what changed and what it
@godofprompt
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12 Key Takeaways from the 2026 AI Index Report
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source: https://
hai.stanford.edu/news/inside-th
e-ai-index-12-takeaways-from-the-2026-report
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Stanford AI Index: Model race over per Elo ratings
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The AI model race is over. Most people won’t realize it for another six months. Stanford’s 2026 AI Index published the numbers two weeks ago. Arena Elo ratings across every major lab: Anthropic 1,503. xAI 1,495. Google 1,494. OpenAI 1,481. Alibaba 1,449. DeepSeek 1,424.
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Build AI Marketing Agency with Custom Generative Flows for Clients
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Or start setting the infra (custom generative flows) for clients to produce the deliverables within that content strategy of yours? Get your set up fee, maintenance retainer. Copy 85% and repeat across new clients. Now you’re an AI Marketing Agency
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Using AI for React application interface development
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It truly does amazing react applications and should be reserved for polishing the interfaces then
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Managing and Securing AI Prompt Libraries
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Hey! There weren’t any prompts shared from this account and as the message states, all the focus went into rebuilding the prompt library and securing it online for the users!
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Building a Decentralized Cross-Platform AI Alternative
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There are memory solutions that are fully hashed and will never belong to anyone. But the point here is not privacy – it’s dependency on the provider And we are building something in between them all. Something even OpenAI can’t kill, cause it will never be cross-platform
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Architectural Principles for Building AI Agents and Automations
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What this means practically for anyone building agents, prompts, or automations right now: – Stop treating memory as a storage problem – Stop renting your agent's intelligence from the labs – Instrument outcomes, not just inputs – Every interaction should be a labeled
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The Architectural Trade-off Between Retrieval and Learning Systems
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The moat math is brutal once you see it. A retrieval system plateaus at the quality of its extractor. As good on day 1,000 as day 1. And most of what it "knows" belongs to whichever lab hosts it. A learning system you control compounds every interaction into a corpus of (state
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The Strategic Risks of AI Agent Dependency and Model Lock-in
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Think about what that means on a 2-year horizon: – Your agent's accumulated intelligence is stuck in one provider – Switch models, lose the learning – Pricing page changes, your moat changes with it – They deprecate a feature, your product bleeds The intelligence you thought