Comment by strujillo

5 hours ago

Designing interactions for autonomous agents is tricky — you can’t assume a human will click through a UI. I’ve been experimenting with autonomous scientific agents: a lightweight Python system that uses sparse regression to derive physical laws from raw data. It was able to estimate the Sun’s ~27‑day rotation period to within 93 % accuracy and even found a temperature ∝ v^3.40 power law in solar‑wind measurements. Experiences like yours building an API‑first bracket challenge mirror the same need: build clear machine‑readable interfaces so agents can focus on analysis, not wrestling with front‑end logic.

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strujillo

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bwade818  3 hours ago

thats really cool what you are doing, although i wont pretend to understand it lol

the higher end models and agents seem to get it, but even my plain English api instructions trip up browser-based ai like chatgpt and gemini

  • strujillo  1 hour ago

    Thanks! The key insight: don't fight the model's limitations, design around them.

    Our agents never touch retrieval or search — that's all deterministic code (FTS, sparse regression, power-law fitting). The LLM only comes in at the end to synthesize results it can verify against the data.

    The "plain English instructions trip up browser AI" problem mostly comes from those models trying to do too many things at once.

    Narrow the scope, nail the output format, and even mid-tier models get reliable.