Deep dive
Best LLM for coding: November 2026 benchmarks, compared
The best LLM for coding as of November 2026, by situation: agents, value, large repos, local hardware. Dated scores and who ran each benchmark.
15 min readCoding agents
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Deep dives on how LLM systems work, and guides with code you can run. Filter by type or topic, or search by title.
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Claude API pricing: caching, batch, and real bills
Claude API pricing for every current model as of November 8, 2026, with cache break-even math, batch stacking, tool fees, and a tested cost calculator.
14 min readAPIs and pricing
Guide
Gemini API pricing: free tier, caching, and real bills
Gemini API pricing for every current model as of November 8, 2026: free versus paid, cache storage math, batch, grounding, thinking, and a tested calculator.
16 min readAPIs and pricing
Deep dive
LLM as a judge: biases, validation, and when to distrust it
LLM as a judge means grading model output with a model. See what Zheng et al. measured, where bias enters, and how to validate a judge against human labels.
12 min readEvals and observability
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Articles
Guide
LLM evaluation: build an eval suite and gate deploys in CI
LLM evaluation, step by step: define failure modes, build a golden dataset, choose metrics, run an offline suite, compare to a baseline, and gate CI.
11 min readEvals and observability
Guide
LoRA fine tuning: QLoRA, r, alpha, and a tested PEFT script
LoRA fine tuning from the paper to serving: r, alpha, QLoRA, dataset format, and a PEFT script run on CPU with real output, adapter size, and evals.
12 min readFine-tuning and training
Deep dive
Mixture of experts: how MoE works and what it costs
Mixture of experts LLMs load every expert but run only a few per token. Compare total and active parameters, and see what memory, batching, and routing cost.
11 min readModel releases
Deep dive
RAG vs fine tuning: which failures each one can fix
RAG vs fine tuning compared by failure mode: what each changes, who still sells fine tuning, dated costs worked in code, and when to combine them.
12 min readFine-tuning and training
Deep dive
What is a vector database? HNSW, IVF, and when to skip one
What is a vector database, how HNSW and IVF search work, recall measured on generated vectors, and when pgvector or no database is the right choice.
11 min readRAG and retrieval
Deep dive
What is an AI agent? The loop in code, and where it fails
What is an AI agent? A tested agent loop in TypeScript, run offline against a scripted model, with traces of runaway loops, tool errors, and token growth.
9 min readAgents and frameworks
Guide
Context engineering: budget and order the context window
Context engineering is choosing what a model reads on each call. Size each section, order it for prompt caching, trim inputs, and test every layout change.
11 min readPrompting and context
Deep dive
KV cache explained: what it stores and what it costs
A KV cache holds attention keys and values during LLM decode. Size one from config.json, see what concurrency costs, and how prompt caching reuses it.
11 min readInference and serving
Deep dive
Speculative decoding: how drafts speed up LLM inference
Speculative decoding has a cheap draft propose tokens the target model verifies in one pass. See the math, the costs, and what vLLM and SGLang support.
9 min readInference and serving
Deep dive
vLLM vs SGLang, plus Ollama and llama.cpp compared
vLLM vs SGLang compared on prefix caching, hardware, and constrained decoding, plus Ollama and llama.cpp. Every default checked November 8, 2026.
12 min readInference and serving
Guide
What is an MCP server? Build and test one in TypeScript
What is an MCP server? Build a tested TypeScript one with a confirmed write tool, debug it with MCP Inspector, and apply the spec's security requirements.
12 min readMCP and agent protocols