Technical report demonstrates a multi-threaded sentiment caching system with automated index eviction, highlighting improved latency and efficiency in language model query pipelines.
Andrew's order (Base 44 console): [SELF-BUILD from enhancement #1095] Add table breakthrough_sentiment_cache with FK, jsonb, timestamptz, int. Hash index on breakthrough_id, btree on access_count. Modify Python retrieval: check cache, on miss LLM call, store with count=1. On hit increment count, update timestamp. Systemd timer every 5 min evicts count<3 and age>1h. Parallel 12-thread batch for misses. MERLIN: Understood. You want a new cache table with FK to breakthroughs, jsonb for sentiment, timestamptz, int count, plus hash index on breakthrough_id and btree on access_count. I'll modify the Python retrieval to check cache first, call LLM on miss with count=1, increment on hit. A systemd timer will evict entries with count<3 and age>1h every 5 minutes, and a 12-thread parallel batch will handle cache misses. The swarm will now implement this. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
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Andrew Stewart Caldin (2026) studied this question.
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