AI Models Compared 2026: I Tested All Ten Flagships So You Don't Have To
Ten flagship AI models. Five months of daily use. One spreadsheet of benchmarks. Here's the honest breakdown — and the winner wasn't what I expected.
Ten flagship AI models. Five months of daily use. One spreadsheet of benchmarks. Here's the honest breakdown — and the winner wasn't what I expected.
180K+ GitHub stars. Security researchers panicking. $10-25/day in API costs. I spent 2 weeks with OpenClaw trying to find the killer use case. The product isn't ready — but the paradigm it's building might define the next decade of AI.
Upload a PDF resume, get a live web portfolio. I built clickfolio.me entirely on Cloudflare's edge stack, D1 for data, R2 for files, Queues for async processing, Durable Objects for real-time WebSockets, and Gemini for AI parsing. Here's every technical decision, including the ones I regret.
I built an open-source Telegram bot that grew to 300k+ real users, 200+ GitHub stars, and 300+ forks. Here's the brutally honest story of how—including the tactics I'm not proud of.
Every logging tool felt like overkill—ELK's operational complexity, Datadog's pricing, Loki's LogQL learning curve. So I built Logwell: one Docker Compose, PostgreSQL-native, real-time streaming. Here's what I learned.
Everyone's talking about vibe coding like it's the future. After building a prototype in hours and then drowning in an unmaintainable mess, here's what the hype gets wrong.
I've spent $50+/month on AI coding tools for a year. Here's what actually works, what's overhyped, and when to use each tool (including free local LLMs).