News & guides
Streaming 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.
· 8 min read
Structured 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.
· 7 min read
Subagents 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.
· 9 min read
System 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.
· 9 min read
Temperature, 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.
· 8 min read
The 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.
· 9 min read
The 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.
· 8 min read
Token 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.
· 8 min read
Tokenization 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.
· 8 min read
Tool 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.
· 9 min read
What "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.
· 8 min read
What AI Actually Costs Per Developer Per Month
There is no single number, but there is a reliable way to derive yours. Work through the token arithmetic behind light, typical and heavy agentic usage.
· 8 min readReady to try it?
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