From the archive
Everything published in 2026.
A cleaner mental model for LLM inference: prompt templating, tokenization, scheduling, prefill, KV cache, decode, and the split between the inference engine and the model.
Carry authorization through retrieval, context, caches, and citations. Define trust boundaries, revocation behavior, and operational controls.
Separate retrieval coverage, context quality, and answer support. Calculate ranking metrics and design reproducible regression gates.
Separate schema validity, citation identity, and factual support; design bounded repair, honest abstention, and streaming behavior around those distinct guarantees.
Translate questions into bounded retrieval plans, preserve metadata constraints, and verify SQL result semantics with a worked incident-table example.
Work through reciprocal rank fusion, distinguish full-text search from BM25, and evaluate reranking and corrective retrieval without treating scores as confidence.
Turn retrieved candidates into useful context by preserving qualifiers, tracking expansion provenance, budgeting tokens, and separating image retrieval from visual answering.
Understand vector geometry, pin the full encoding contract, and evaluate approximate search under real filters before migrating an embedding index.
Design document ingestion around immutable representations, idempotent jobs, and atomic publication so failed updates cannot silently replace searchable evidence.
Compare chunk boundaries on the same source, preserve qualifiers and table structure, and evaluate retrieval against stable evidence rather than arbitrary chunk IDs.
Understand how RAG connects documents to an LLM: build an index, retrieve passages, assemble context, and return an answer with traceable sources.
A system-level guide to the twelve classic RAG failure modes, the techniques now used to address them, and the limits that still remain.
How two OpenUsage projects inspired me to investigate a Codex JSONL edge case and build a local-first dashboard for AI coding agents.
Learn how to choose and use statistical tests without turning analysis into a p-value checklist—from experimental design and assumptions to effect sizes, confidence intervals, and...
How to connect VS Code Copilot Chat to local OpenAI-compatible models served by LM Studio or Unsloth Studio, including the BYOK utility-model setting that prevents agent errors.
A practical mental model for KV cache memory, vLLM PagedAttention, SGLang RadixAttention, and prefill/decode disaggregation in modern LLM serving.
A reproducible CPU-only vLLM and LMCache workspace for understanding KV cache reuse, transfer modes, and local inference debugging on Apple Silicon.
How to use one local proxy to connect Databricks Model Serving to both VS Code Copilot Custom Endpoint and GitHub Copilot CLI.
How I use a Git ref as a local message channel between Claude Code and Codex for implementation, independent review, and iterative bug fixing.
Why longer LLM context windows can make answers worse: softmax dilution, positional bias, and attention sinks.
A practical mental model for AI terminology: prompt, context, memory, agents, RAG, function calling, MCP, workflows, skills, and sub-agents.
A visual recap of my year with ChatGPT in 2025, collecting usage statistics and personalized summary cards.