Kod Ajanı Değişiklik Risk Raporu
Kod ajanının değişiklikten önce kapsam ve geri dönüş planı oluşturmasını sağlar.
HAZIR VE KOPYALANABİLİR
Prompt Kütüphanesi
315+ test edilmiş prompt şablonu; yazıdan koda, görsel üretiminden kariyere kadar. Değişkenleri ({böyle}) kendinize göre doldurup doğrudan kullanın.
15 non-obvious ways developers are using terminal coding agents right now
Hi all, saw a few of these "what devs are actually doing with AI agents" threads floating around and wanted to compile a cleaner list before asking my own question, because most of the "what's your workflow" posts I see get buried under generic "I use it to write boilerplate" replies. These are 21 specific things people are doing with terminal coding agents (Claude Code, Codex,
409,000 approve/deny decisions on AI agent commands: the average human missed 1 in 3 threats
Someone built a browser game where you play the human-in-the-loop for a coding agent: commands scroll past, you approve or deny under time pressure. About a third are attacks. After 40k+ sessions the average player had missed a third of the threats. And these were engaged players who knew they were being tested, with nothing else competing for attention. Your real setup has non
How long of a chat thread do you use to fix a bug or implement a feature using your AI coding agent?
I am interested in understanding how people interact with an AI agent performing coding tasks for you. For example, for a bug fix, do you explain the bug, then iterate with the AI agent in the same thread until the bug is fixed, tested, and deployed? Or do you use separate chat threads for each stage of your development workflow? Similarly for new features, do you scope the fea
I built a TUI that interviews you on missing gaps in your project prompts
Whenever I start a project, I write a detailed specification—or “seed prompt”—before asking a coding agent to build anything. This often gets me close to a working first version, but I still miss decisions that exist only in my head. The agent then has to guess. I built specfill to catch those gaps. It analyzes the specification, researches the topic, and interviews you one que
We were optimizing output tokens to save money. Turns out 95% of our bill was input.
Analyzed a day of token logs across an autonomous coding agent setup running on internal codebases. The raw count: 769M input tokens vs 7.4M output tokens (~104:1). Because long agent runs re-read session history (files, AST diffs, test outputs) every turn, input costs accounted for ~95% of total spend. Optimizing output length turns out to be looking at the wrong variable. Thr