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  "path": "/t/the-openai-api-unlocked-a-whole-new-layer-of-building-for-me/1380174#post_8",
  "publishedAt": "2026-05-03T08:56:36.000Z",
  "site": "https://community.openai.com",
  "textContent": "**The ‘Context Window as a Workbench’ is a perfect analogy. It highlights that the LLM is a tool for transformation, not a database.**\n\n**Keeping a hard separation between ‘Explicit State’ (status, decisions) and ‘Soft Context’ (summaries, notes) is likely why your system is so repeatable. Most people fail because they let the model hallucinate the project’s progress. By forcing the runtime to handle ‘decisions’ and ‘objectives’ as explicit data, you’ve essentially built a Compiler for creative work.**\n\n**I’m really curious about the ‘engine’ under the hood. To manage this kind of ‘durable state’ and ‘selective rehydration’, what does your stack look like? Are you running this on Node/Python, and what are you using for the state layer? Is it a classic SQL/NoSQL setup, or are you leaning into something more specialized for agentic workflows?**\n\n**Thanks for sharing these insights, Andy. This thread is a goldmine for anyone moving past simple RAG into actual AI production systems.**",
  "title": "The OpenAI API unlocked a whole new layer of building for me"
}