ModelsStop Reading Benchmarks. Build a 20-Task Eval Instead.
Public leaderboards tell you almost nothing about your codebase. Here is how to build a private eval set in an afternoon and actually know which model to use.
ReadNotes on models, agents, and what inference costs
Practical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
Models
GuidesStreaming LLM Responses: SSE, Buffering, and Why Output Stalls
Streaming is what makes an AI feature feel fast. It is also where proxies, buffers and framework defaults quietly break things. A practical guide.
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AI AgentsStructured Outputs: Getting JSON You Can Actually Parse
Asking a model for JSON and hoping is a bug waiting to happen. Schema-enforced outputs, and the validation you still need around them.
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AI AgentsSubagents and Delegation: When Splitting an Agent Helps
Subagents buy parallelism and a clean context window, and cost you shared understanding. Here is when that trade is worth making, and when it is not.
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FundamentalsSystem Prompts: What They Actually Do and How to Write One
A system prompt is not a control panel and not a security boundary. Here is what it really is, where it sits in the instruction hierarchy, and how to write one.
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FundamentalsTemperature, Top-p and Sampling: What the Knobs Really Do
Temperature is not a creativity dial and top-p is not a quality setting. Here is what each sampling parameter changes mathematically, and how to set them for real work.
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ModelsThe Best Model for Refactoring Is the One That Stays in Scope
Refactoring is a constraint problem, not a generation problem. What the task actually demands from a model, and how to measure it on your own repo.
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Cost & PricingThe Hidden Costs of AI Coding Tools
The model invoice is the smallest line item. Here is the full list of what agentic coding actually costs, and a method for pricing each part yourself.
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Cost & PricingToken Accounting: Explaining AI Costs to Finance
Finance teams need cost drivers, allocation and controls, not a lecture on transformers. Here is how to translate token usage into terms a budget owner can act on.
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FundamentalsTokenization Explained: Why Your Bill Is Not About Words
Tokenizers decide what your prompt costs, how long your context really is, and why models miscount letters. Here is how subword tokenization works in practice.
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AI AgentsTool Calling: The Feature That Turns a Model Into an Agent
Tool calling is how a model asks your code to do something. Understanding the handshake — and designing good tools — is most of what makes an agent reliable.
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GuidesWhat "OpenAI-Compatible" Actually Means (And Where It Breaks)
Most AI tools speak one API shape. Understanding what compatibility covers — and the four places it usually leaks — saves hours of debugging a base URL swap.
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