Hivemind turned my agent traces into 19 reusable skills, then refused to write the one my docs already covered (hands-on review)

I wired Activeloop's Hivemind (continual-learning / shared-memory for coding agents, works with Claude Code, Cursor and Codex) into my real workflow for a day and measured what it actually does. Honest notes: - it mined 19 named skills from my real agent history in about two minutes, each with provenance back to the sessions it came from - the best part was what it refused to write: I pointed it at a workflow my repo already documents and it returned 0 candidates ("already documented, no repeatable-mistake pattern detected"). a memory layer with taste, not a hoarder - skill propagation test: same bug, two fresh agents, one with the mined skill in scope and one without. the one with it led with the exact root cause; the one without gave a generic checklist and missed it - recall was broken out of the box on my machine. I traced it to the summary worker (120s per-session timeout, concurrency 4, my transcripts up to 18MB, so it starved), staged + flushed, and semantic recall fired - the honest catch: on short, well-documented tasks proactive recall cost about 75% more turns. it shines on memory-heavy work (real cross-session recall of exact past work) and taxes you on the short stuff, so tune it rather than trust it everywhere Full write-up and video walkthrough links in the comments (sub rule). Disclosure: this was a paid review. All tests, numbers, and opinions are my own, including the parts that don't flatter the product.