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
Some time ago, I was building a chat application using AWS Websocket API gateway. Things were going smoothly. I created a WebSocket API Gateway, added $connect, $disconnect, and sendMessage/addGroup routes. From the frontend (React) side, everything was fire-and-forget. You send a message, and the onMessageHandler takes care of it 💪🏼 But then a new requirement of uploading files using S3 signed
As Large Language Model (LLM) agents increasingly integrate numerous external systems, they suffer from Tool Space Interference (TSI), a phenomenon causing context bloat, attention dilution, and degraded reasoning accuracy. In this paper, we introduce the Agent-as-a-Tool paradigm—an evolutionary, practical implementation of the recently proposed Self-Optimizing Tool Caching Network (SOTCN) and Fed