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Practical guides on the tools developers use every day — JSON, regex, JWT, cron, and more.
15 posts matching · page 1 of 2
Learn the true hardware, power, and time costs of running AI models on your own machine, with commands to measure them.
Learn how to run a 30B parameter AI model on a consumer GPU with quantization, llama.cpp, and practical setup steps.
Learn why VRAM, not raw GPU speed, determines which local AI models you can run, and how to measure it with real commands.
Learn to calculate VRAM needs for local LLMs, estimate with formulas, and test real models on your GPU.
A hands-on guide to building a practical AI developer stack for 2026: models, agents, orchestration, and observability with copy-paste code.
Learn to build a cost-effective AI agent with a minimal loop, tool schema, and guardrails, using open-source models and practical code examples.
Learn AI model routing by building a real app that sends tasks to the best model based on cost and latency.
Learn why AI agents lose context between tasks and how to fix it with memory, state, and observability patterns.
Learn why LLM context windows are not memory and how to build real persistent memory for AI agents with code examples.
A hands-on guide to choosing and integrating the top 5 AI models for developer agents, with code and config examples.
Learn to choose cost-effective LLMs for AI agents, with a practical routing setup, cost benchmarks, and a working example.
Learn when to route to reasoning or fast LLMs, with a latency-cost decision framework and runnable API examples.