In the fast-paced world of continuous integration and deployment (CI/CD), managing sensitive information like API keys, tokens, and credentials—collectively known as secrets—is not just a best practice; it's a critical foundation for security and efficiency. GitHub Actions provides a robust framework for automating workflows, but a common friction point for many development teams, particularly tho
The Challenge of Scalable Secrets Management in GitHub Actions For development teams scaling beyond a handful of repositories, managing environment-specific variables and secrets in GitHub Actions can quickly become a significant bottleneck. The manual duplication of configurations across multiple repos, especially when dealing with distinct environments like development, staging, and production
Introduction Building a mobile application that handles sensitive financial data — crypto transactions, KYC verification, gift cards — means security is not an afterthought. It is a core deliverable. During the development of a cross-platform fintech application, one of the non-negotiables on the security checklist was runtime application self-protection (RASP). After evaluating our options, we
React Native's New Architecture — JSI, Fabric, and TurboModules — has been "coming soon" for long enough that some teams wrote it off as vaporware. It shipped. It is now default in new React Native projects. And it meaningfully changes how the framework works at the performance-critical boundaries between JavaScript and native code. This post is not a getting-started guide. It is an honest account
I got tired of the same three-step content publish loop: write draft → open CMS → paste, format, re-paste, fight the rich-text editor, click publish. Repeat for every environment — staging, then production. For one article, fine. For a team publishing 20+ pieces a month? That workflow is a quiet tax on everyone's time. So I wired up a pipeline that cuts the loop entirely. You commit a .md file to
Originally published on rohitraj.tech UPI fraud hit ₹805 cr in India last year. Cloud APIs leak data. So I built ScamRakshak — fully on-device scam detection. 3-tier inference engine: Gemma 4 LLM — context-aware classification LiteRT — fast pattern model Regex fallback — when battery low Full architecture write-up: https://rohitraj.tech/en/notes/build-on-device-ai-scam-detector-android-gemma Read
It's a one-line item on the roadmap. "Send a push notification when X happens." Estimate is two days, three if the backend doesn't have FCM credentials yet. There's a library for it. The library is the visible part. The other 90% is platform lifecycle, registration state machines, race conditions with navigation, payload archaeology, and a half-dozen iOS and Android quirks. Nobody writes them down
I have been building web apps for 12 years. In that time I never wrote a single line of mobile code. Not Swift, not Kotlin, not even a basic React Native hello world. That changed last month because of my wife. She has been using Synapse, the AI companion I built for her, every day from her phone browser. If you are new here, Synapse is a personal AI that uses a temporal knowledge graph instead of