Comment by bwade818
5 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
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.
amen
There isn't an LLM inside of my code. The agents need to submit a perfectly sturctured json, and then the code verifies it
Yeah exactly — that “no LLM in the loop” constraint forces everything to be explicit and verifiable.
I put together a few experiments where the system rediscovers known laws directly from raw data (solar wind, exoplanets, etc).
Happy to share if you’re curious — still very early but interesting to see what emerges.