Book: Prompt Engineering Pocket Guide: Techniques for Getting the Most from LLMs Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools Me: xgabriel.com | GitHub A finance team at a mid-sized SaaS feeds 40,000 expense receipts a
Cuando una aplicación necesita leer un archivo, escribir en una conexión TCP o esperar datos de un disco, el kernel de Linux ofrece tradicionalmente dos caminos: bloquear el proceso hasta que la operación termine, o usar interfaces como epoll y Linux AIO para manejar múltiples operaciones concurrentes. Durante casi tres décadas, esas fueron las opciones dominantes. Pero desde la versión 5.1 del ke
When Google announced the Manifest V3 deadline, the developer community had a lot to say — most of it negative. The service worker model was rightly criticized as a regression for ad blockers and complex extensions. I've now migrated 18 extensions from MV2 to MV3, or built them MV3-native from the start. The commonly documented issues (no persistent background pages, limited webRequest) are real.
Client-side caching is usually implemented as a storage optimization layer (TTL, SWR, invalidation rules). In practice it behaves like a decision system under uncertainty. Static strategies fail when data volatility is non-uniform across the same application. This leads to either stale UI or excessive network traffic. This article breaks down: why standard caching approaches plateau where ML impro
The math isn't complicated. It's just that nobody runs it until they get the bill. An AI agent handling a 10-turn workflow — reading files, calling tools, revising output — doesn't cost 10x a single query. Because transformer inference processes the entire context on every call, cost compounds with each additional turn. The tenth turn carries everything that preceded it: the original file reads, e
If this is useful, a ❤️ helps others find it. I've shipped multiple apps with AI features. My AI infrastructure cost: $0/month. Here's exactly how — every tool, every limit, every workaround. Free tier: 500 req/day (Gemini 2.5 Flash), no credit card Best for: Strong reasoning, document analysis, code debugging Get it: aistudio.google.com 2. Ollama — Local LLMs Free tier: Unlimited
Most agency onboarding fails before the kickoff call happens. Not because the team isn't good. Not because the client is difficult. Because nobody collected the right context upfront, and the kickoff call becomes the place where everyone discovers what they don't know yet. The intake form is the fix. Not a 3-question "tell us about your project" form. A real one. Here's the framework we use — 27 q
If this is useful, a ❤️ helps others find it. I run both in production. Here's the real comparison — not theoretical, from actual use building developer tools. Local LLM (Ollama) Gemini API (Free) Cost $0 forever $0 (free tier) Privacy 100% local Data sent to Google Setup Install Ollama + pull model Get API key (2 min) Quality Good (7B), Great (70B) Excellent Speed Fast if model lo