How regex-constrained sampling and grammar-guided decoding actually work at the token level, why prompt-only JSON mode fails 5–20% of the time in production, and the full toolchain for guaranteed structured output: Outlines, XGrammar, vLLM guided decoding, and Instructor.
AI/ML
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Constrained Generation: Outlines, JSON Mode, and Structured Output That Works -
MCP Deep Dive: Servers, Resources, and Tools A thorough technical walkthrough of the Model Context Protocol: how JSON-RPC over stdio and HTTP works, building servers with FastMCP and the TypeScript SDK, implementing tools and resources, the sampling primitive, security threat model, and production deployment patterns.
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Quantization Deep Dive: GPTQ, AWQ, GGUF, AQLM, and MLX A rigorous look at every major LLM quantization format: how GPTQ's Hessian-guided rounding works, why AWQ's activation-aware scaling beats naive per-channel approaches, when GGUF k-quants are the right choice, where AQLM wins at sub-3-bit, and how MLX handles Apple Silicon. Includes conversion workflows, a perplexity shootout, and a decision guide.
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Self-Hosted Image Generation: Flux.1, SDXL, and ComfyUI Workflows The complete self-hosted image generation stack: Flux.1 vs SDXL architecture and quality tradeoffs, hardware requirements by model tier, ComfyUI node-based workflow construction, the HTTP and WebSocket API for automation, LoRA fine-tuning, and Docker deployment behind a reverse proxy.
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Semantic Caching for LLM Applications: Cutting Cost and Latency How embedding-based semantic caching works, why threshold tuning is the hardest part, cache invalidation patterns that prevent staleness, and practical implementations with GPTCache, LiteLLM, and a from-scratch Redis + FastAPI setup.
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Speculative Decoding: Draft Models, EAGLE, and How to Actually Use It How speculative decoding achieves 2–4x inference speedup without changing model outputs, covering the rejection-sampling proof, EAGLE and EAGLE2 draft strategies, ngram lookahead, and production configuration for vLLM and llama.cpp.