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Ask HN: How you manage local long lived research projects and LLM's?

A Hacker News user asks the community for patterns or structures to manage long-lived research or non-coding projects using LLMs, citing their own example of collecting nut-free restaurant data in Chicago. They are curious if others have standard formats or working examples for using LLMs to organize data and answer questions over an extended period.

Background

- The author is asking Hacker News for advice on managing long-running, non-coding research projects (e.g., tracking health data, collecting restaurant info) with LLMs like ChatGPT or Copilot.<br>- Hacker News is a tech forum run by Y Combinator. "Ask HN" posts are direct questions to the community.<br>- The referenced thread (48605117) was a popular post where someone tracked personal fatigue over time — an example of personal research outside software.<br>- The author's project (chicago-allergy-eats on GitHub) collects nut-free restaurants in Chicago. They describe feeding raw material (reviews, call notes, emails) into Copilot repeatedly and asking it to organize the data — effective but ad-hoc.<br>- The core problem: LLMs have limited context windows and no built-in persistent memory, so multi-session projects require manual re-uploading or workarounds. There is no standard format for this kind of iterative, mixed-media research.

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