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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