GuidesBuilding a Postman Collection for OpenAI-Compatible APIs
A reusable Postman or Insomnia collection for any OpenAI-compatible endpoint — environments, streaming, tool calls, saved examples, and sharing it without leaking keys.
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.
Guides
GuidesBuilding a PR Summariser Reviewers Do Not Skip
Most PR summary bots restate the diff and get ignored within a fortnight. What reviewers actually need, how to select the diff, and how to keep cost per PR predictable.
ReadGuidesBuilding a Prompt Library People Actually Reuse
Most prompt libraries become a graveyard of untested snippets. What makes one worth maintaining: ownership, discoverability, contracts and a deletion policy.
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GuidesBuilding an Incident Assistant On-Call Engineers Trust
Automate the first ten minutes of context gathering, not the diagnosis. Read-only tools, evidence-linked output, a latency budget, and why a wrong answer at 3am is expensive.
ReadGuidesBuilding an LLM Code Review Bot People Do Not Mute
Wiring a model into pull request review: what to send it, how to post comments that land, and the signal-to-noise threshold that decides whether the bot survives.
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AI AgentsBuilding an MCP Server: A Working One in an Afternoon
A practical walkthrough of writing your first MCP server: choosing the tools, wiring the transport, handling errors, and testing it with a real agent.
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FundamentalsByte-Pair Encoding Explained: How Tokenizers Are Built
BPE is a compression algorithm that became the standard way to split text for language models. How it is trained, what it produces, and why it behaves oddly.
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GuidesCalling an LLM API from Jenkins Without Leaking the Key
A declarative pipeline stage that calls an OpenAI-compatible endpoint, handles credentials properly, fails loudly on errors and does not bankrupt a shared build server.
ReadFundamentalsChain of Thought: Why Thinking Out Loud Actually Helps
Asking a model to reason step by step measurably improves accuracy on some tasks and wastes tokens on others. The mechanism, and when it is worth the cost.
ReadCost & PricingChargeback Models for AI Spend That Teams Accept
Allocating LLM spend back to the teams that caused it: tagging, per-key versus per-service attribution, shared costs, and when showback is the better step.
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ModelsChoosing a Model for Non-English Codebases
Comments, identifiers and issues in another language change tokenisation, cost and accuracy. What to test and how to pick a model that handles it.
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GuidesCircuit Breakers for LLM APIs: Failing Fast on Purpose
How to build a breaker around an inference provider: what to count as a failure, where to set thresholds, how half-open probes work, and when to fall back instead of failing.
ReadShowing 397–408 of 731 articles