so i worked around, did a lot of trying with mcp, plugins, and i stuck with a system i call "one".

hdc vector embeddings (4096 dimensions, trigram + word encoding) stored in SQLite and recalled by cosine similarity on context shifts.

entity extraction builds a knowledge graph across sessions. rules get learned from repeated preferences. thats the core

the part that scared me was the autonomous research loop. there's a mode where claude researches a topic, then a dialectic engine challenges every finding. thesis/antithesis/synthesis.

a contradiction minor looks for conflicts, and a synthesis engine searches for patterns across domains. weak findings get pruned and it can iterate indefinitely.

it was running on my 15m kalshi trading algorithm (which also happens to use hdc + tsetlin machines haha!) and it produced 420 research findings (lol) with cited acedemic sources, it mined 472 contradictions, deprecated almost 600 weak claims through adversarial challenge, and discovered 21 patterns across domains i never directed it to explore.

the system connected python's lazy import pattern to rna transcription, both are deffered materialization where dormant capabilties are suppresed until activation context arrives.

it formalized why certain bug classes are invisible to quality checks, the query is outside the space where results exist, not bad results.

also has a small verification engine that ast-parses every code exit, checks sql against live schema, and maps every function call and file dependency in the codebase.

test it out,talk to me, ask things! it's my first time making a repository public!

(currently using the system on the system as im typing this out to make sure it autonomously upgrades itself before i go to sleep and you guys think my project sucks)