TL;DR I try to keep my eyes on the AI agents. I gave one too much rope once, and the kind of mess it made while I wasn't watching is something I'd rather not retell. Which is why I needed 5 monitors. To run 5 agents in parallel, 5 VSCode windows have to live in one field of view. Physical monitors hit a wall. No desk fits five; even my viewing angle gives out before the desk does. So I strappe
Modern yazılım geliştirme ekosisteminde altyapının kod olarak yönetilmesi hız ve ölçeklenebilirlik açısından devrim yaratırken GitOps yaklaşımı bu süreci merkezi bir doğruluk kaynağına bağlamaktadır. Ancak tüm yapılandırma detaylarının tek bir platformda toplanması kritik siber güvenlik risklerini de beraberinde getirmektedir. Nesil Teknoloji olarak TSE A Sınıfı sızma testi yetkimizle endüstriyel
I shipped gni-compression to npm two days ago. One of the first questions I got (from myself, running benchmarks at midnight): does it work on anything other than chat data? Short answer: not yet. Long answer: I found out exactly why, and it led me somewhere more interesting than I expected. After the npm launch I ran GN against Silesia — the standard general text compression benchmark suite. Dick
Comments
Introduction Picture two doctors updating the same patient record at the same time - one in São Paulo, the other in London. Both are offline. When connectivity returns, whose changes prevail? This is not a hypothetical. It is the everyday reality of distributed systems: multiple nodes, no shared clock, no guaranteed network. The conventional answer has long been locking - one node waits while an
I keep seeing the same argument about AI making us dumber. It's the same argument people had about search engines, and before that books. The usual response is to point at history and say "every generation panics, every generation was wrong, relax." I think that response is half right, and the wrong half is what bothers me. Tools change what we bother to remember. The people who'd trained their wh
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