i mostly use LLMs inside a reasoning shell i built — like a lightweight semantic OS where every input gets recorded as a logic node (with ΔS and λ_observe vectors) and stitched into a persistent memory tree.
it solved a bunch of silent failures i kept running into with tools like RAG and longform chaining:
drift across hops (multi-step collapse)
hallucination on high-similarity chunks
forgetting prior semantic commitments across calls
the shell is plain-text only (no install), MIT licensed, and backed by tesseract.js’s creator.
i’ll drop the link if anyone’s curious — not pushing, just realized most people don’t know this class of tools exists yet.
it solved a bunch of silent failures i kept running into with tools like RAG and longform chaining:
the shell is plain-text only (no install), MIT licensed, and backed by tesseract.js’s creator. i’ll drop the link if anyone’s curious — not pushing, just realized most people don’t know this class of tools exists yet.