FundamentalsWhere Quantised Models Actually Lose Quality
Quantisation degrades capabilities unevenly rather than uniformly. Which ones erode first, why a benchmark delta hides it, and how to measure the loss on your own work.
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.
Fundamentals
ModelsWhich Open Model Fits Your GPU: A Memory-First Guide
Total parameters set the memory floor, active parameters set the speed. How to work out what your hardware can actually serve before reading a benchmark.
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ModelsWhy Benchmarks Disagree, and Which One to Believe
Two credible evaluations can rank the same models differently without either being wrong. How to read that disagreement instead of picking the flattering one.
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GuidesWindsurf Setup Guide: Custom Models and the BYOK Question
What Windsurf needs to talk to a model you chose, how to find the provider settings in your build, and the local proxy fallback when there is no slot for one.
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GuidesWriting Database Migration Scripts With an LLM
How to use a model for schema and data migrations safely: supply both schemas, demand reversibility, verify with dry runs and row counts, and know what not to delegate.
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GuidesZed Setup Guide: Wiring Up a Custom Model Endpoint
Point Zed's agent panel at any OpenAI-compatible endpoint — the JSON provider block, the model metadata you have to declare yourself, and how to verify it.
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AI AgentsAgent Cost Control: Patterns That Bound the Worst Case
Agent spend is not a per-call problem, it is a per-loop problem. Budgets, caching, tiering and circuit breakers that cap what a bad run can cost you.
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AI AgentsAgent Error Recovery: Patterns That Keep the Loop Alive
Errors are the normal case in an agent loop, not the exception. These are the recovery patterns that separate agents that finish from agents that spiral.
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AI AgentsAgent Memory: Managing Context Before It Manages You
Long agent sessions fail because context fills with noise. Here are the practical strategies for deciding what an agent should remember and what to drop.
ReadAI AgentsAgent Observability: Tracing a Loop You Cannot Reproduce
Agent failures are rarely reproducible, so logs are not enough. What to record per step, how to span a tool loop, and which metrics predict a bad run.
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AI AgentsAgent Planning Strategies: From ReAct to Explicit Plans
Reactive loops drift on long tasks and rigid plans break on contact with reality. Here are the planning strategies that work, and how to choose between them.
ReadAI AgentsAgent Sandboxing: Containers, gVisor and microVMs
An agent that runs code needs a blast radius, not trust. How containers, gVisor and microVMs differ, and the network and credential controls that matter.
ReadShowing 625–636 of 731 articles