These are some topic i wanted to research on a little so that i learn a little more
TL;DR: We wanted PR reviews like the big commercial bots, but with control over cost and where our code goes. We tried Cursor cloud agents, then per-repo GitHub Actions, compared open tools, tried Mod
TL;DR: I wanted every new PR to get an automatic first-turn brief — summary, risks, security/performance smells — before I deep-dive. After comparing SaaS review bots, building a custom agent, and usi
TL;DR: Non-tech teammates can already build useful internal dashboards — often as a single HTML file with AI's help(claude export). The real problems are sharing, secrets, and control. We solved that
I got tired of reading app reviews manually every morning, so I built a command-line tool that pulls reviews from Google Play and the App Store, classifies them (bug, feature request, crash, praise),
TL;DR: I built an app review pipeline that classifies bugs and crashes. It was useful but stopped at "what's wrong." I extended it to answer "where in the code" and "what to change" — using a Pydantic
TL;DR: I maintain a couple of apps on both stores. I kept losing track of what users were saying, and every tool I found either cost too much or did too many things I didn't need. So I wrote a CLI tha
TL;DR: I needed a system that pulls my Gmail and Google Calendar every morning, uses AI to figure out what actually matters, and sends me a prioritized summary on Telegram/Discord/Whatsapp. After rese
TL;DR: Use the official Google Play Publisher API and App Store Connect API to pull your own app's reviews on a schedule, classify each review with a cheap LLM call (bug/feature request/crash/praise),
Securing Your Frontend: A Tactical Guide to Content Security Policy (CSP) Introduction: Silent Failures, Real Risks Imagine this: You deploy a new feature with a third-party widget. QA passes, CI is green, you feel good. Then, your frontend fails sil...