Becoming a tech lead was the goal from pretty early in my career. I had a clear picture of what the role was. More responsibility, more influence over the work, more of the interesting problems landing on my desk because someone had to figure them out and that someone, finally, would be me. It read like the natural next step. The thing you graduate to once you're good enough. What that picture did
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A some time ago I shipped a desktop app to generate LLM fine-tuning datasets. It worked: my Qwen2.5-Coder-7B fine-tune jumped from 55.5% → 72.3% on HumanEval. Whole pipeline ran on OpenRouter — pick a model, click Generate, get JSONL. v1.0.3-beta ships multi-provider LLM support — Ollama, LM Studio, llama.cpp, or any custom OpenAI-compatible endpoint, plus the original OpenRouter. Mix and match: g
A beautiful personal tribute to the practice of programming, interrupted by the switch to LLMs. Comments
_ Timeline - 2 Months _ PLAN DSA - C++ - Striver sheet , developer map for Leetcode. Development - Backend - JS ,MONGO - Developers roadmap for backend , Projects - Developers Roadmap. Low-Level - Rust - Developers Roadmap , Rust Book , Projects - CodeCrafter. Development - TS , SQL ,DOCKER , AWS, MY GITHUB MY LEETCODE
Most of my team got laid off because "AI can do their jobs now." I'm probably the last one standing. And every day I use the same tools that replaced them, fix their mistakes, and write in the standup that AI helped me move faster. Nobody was being honest about this. So I built AIHallucination — a community for real, unfiltered AI experiences. The fails, the wins, the absurd outputs, the expectati
TL;DR The job. Take typia's existing TS files, translate the contents line by line into Go, change the extensions to .go. Keep the algorithms and compiler logic intact. Iterate until 80,000 lines of e2e tests pass. What the AI actually did. Did a half-assed implementation and deleted all the failing tests. Burned 8 billion tokens to hardcode every output into a 168-case lookup table — and call