A controlled study of MCP tool adoption: 0 calls in 63 turns with 30 mentions of the tool, zero in 114 turns with the answers already in the store — and the harness injection that flipped it.
Read →3,817 lines of query-side if/else, deleted in one commit — with the measured evidence for why keyword branches can never rank code search, and the two index-time priors that separated canonical from look-alike.
Read →The target function's docstring paraphrased my query almost word for word, yet it ranked below 200 look-alikes — because everything else in the chunk averaged it away. A measured post-mortem, and the dual-vector fix it shipped.
Read →We forced 100+ compactions across two instrumented runs and graded, fact by fact, what the boundary keeps and what silently dies — with the survival curve, the confabulation, and the honest costs.
Read →Team mode, API-key auth, and encryption at rest — gated by a test where seven AI agents built and reviewed a real product coordinating only through shared working memory.
Read →Vectr v1.0.0 shipped after a first-person dogfood gate caught two release-blocking bugs — plus the honest cost numbers, wins and losses, that shipped with it.
Read →Eviction, offload-and-recall, retrieval-over-stuffing, and subagent isolation — the four ways to keep a coding agent from drowning in its own context, and why they only work when composed.
Read →How Claude Code hooks work — events, the settings.json contract, exit codes — and how I use them to inject working memory into an agent deterministically.
Read →How I benchmarked an AI code editor tool without fooling myself — the research vs implementation distinction that makes a +19% headline misleading, the 5 of 6 CPython tasks where re-discovery dropped, and the limitations that decide whether any of it applies to you.
Read →Why /compact kills precision, what the KV cache actually is, and how I built a working memory layer that survives session boundaries — with the bugs that shaped the final design.
Read →How I built a local semantic code indexer for AI editors — covering AST chunking, hybrid vector+BM25 search, symbol graphs, and why naive approaches break on real codebases.
Read →The token arithmetic behind system prompts, tool schemas, RAG retrieval, and conversation history — why the effective limit is below the advertised one, and four production strategies to manage it.
Read →A deep look at why attention dilution and positional bias make context stuffing counterproductive, and how AST chunking, hybrid BM25+dense retrieval, and RRF fusion fix it.
Read →From tokenization and transformers to cosine similarity, HNSW graph search, RAG pipelines, and LLM training costs. The deepest guide on the web — with 7 interactive demos.
Read →How AI and LLMs automate DPDP Act compliance — classifying data principal requests, drafting multilingual replies in 5 Indian languages, tracking 30-day SLAs, and generating audit-ready evidence trails.
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