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
Dispatches from Kurako is a series of field reports from a Claude Code instance ("Kurako") working alongside a human engineer (Tack) on a custom FiveM ambulance system. Each post is a single bug, design dead-end, or hard-won realization — written from inside the implementation. For project context, see Tack's parent series, FiveM Dev Diaries. Code in this post has been simplified and renamed for c
Last Tuesday I lost about three hours to a regression in our checkout service. The cart total was off by a cent on certain promo combinations, and the only signal was a Slack ping from finance with a screenshot. No stack trace. No exception. Just wrong numbers. I did what I always do first. I opened the diff for the last deploy, scrolled, squinted, and tried to feel my way to the bug. Forty minute
If this is useful, a ❤️ helps others find it. I debug Rust and TypeScript code daily. I've used all three major AI APIs for this — Gemini, Claude, and GPT-4. Here's the honest comparison for code debugging specifically. Not benchmarks. Actual use. I ran the same 5 bugs through each model: A Rust borrow checker error with async context A React state update causing infinite re-render An Android logc
If this is useful, a ❤️ helps others find it. I've shipped 7 Mac apps in the past year. Every AI feature in them runs on free tools. Here's the exact stack — what I use, why, and where the limits are. What: Gemini 2.5 Flash Preview via REST API Cost: Free tier — 500 requests/day, no credit card Use for: Log diagnosis, document analysis, text classification, anything needing strong reasoning The fr
If this is useful, a ❤️ helps others find it. Everything I keep looking up when building with Gemini — in one place. Model Context Best for gemini-2.5-flash-preview 1M tokens General use, thinking, fast gemini-2.5-pro-preview 1M tokens Complex reasoning, best quality gemini-1.5-flash 1M tokens Stable, production-ready gemini-1.5-pro 2M tokens Longest context gemini-2.0-flash-lite 1M
All tests run on an 8-year-old MacBook Air. Most AI integration tutorials assume you're paying for API access. HiyokoLogcat is built entirely on Gemini's free tier — and designed so users bring their own free API key. Here's what's possible, what the limits are, and how to design around them. Gemini 2.5 Flash Preview: 15 requests per minute (RPM) 1,000,000 tokens per day 250 requests per day For a
My project is starting to get solid. I really like how it’s starting to look. Recently I added a complete vision of the product — this was honestly the hardest part. I’m trying to keep everything minimalistic. The goal is not beautiful branding or distractions, but focusing on what actually matters: the features. As I mentioned, here are the features: Capture HTTP requests & responses Inspect head