Table of Contents Introduction Environment Requirements Core Features Core Design and Code Analysis Actual Execution Demo Architecture Overview How You Can Expand Future Plans & Conclusion What is this It is a basic debugger, running on Linux and implemented in C++, aiming to create a debugger that is easy to read and expand. In addition, Lavender's main function is to help users analyze the logic
I spent long hours debugging why Google couldn't index my React app. Lighthouse showed green scores. The app felt fast. But Search Console kept flagging LCP failures and CLS shifts I couldn't reproduce locally. The fix? Four lines of metadata and one misunderstood render strategy. If you've ever shipped a "fast" SPA and watched it flatline in search rankings, this Core Web Vitals SEO guide is for
If you are running production workloads, this is for you. Not side projects. Not early-stage experiments. Not a single-service app with low traffic. This is for teams shipping real systems. Systems with users, uptime expectations, and release pressure. Because at that stage, your deploy process is no longer a convenience. It is part of your product. And right now, for most teams, it is the weakest
Most teams treat cloud cost as a finance problem. But the root cause is usually engineering. Bills spike, dashboards grow, alerts fire — but the underlying issue rarely gets fixed. That idea stood out to me while reading about an approach where AWS cost was handled like an SRE problem — using the same mindset applied to reliability and performance. Instead of asking “why is the bill high?”, the fo
I just shipped v1.1.0 of oh-my-kimi — a multi-agent orchestration harness that wraps the Kimi Code CLI (K2.6) into parallel coding teams. One prompt → planned, parallelized, reviewed project: npm install -g @oh-my-kimi/cli omk chat — Interactive Kimi session with resumable context, tmux support omk cockpit — Real-time dashboard with parallel TODO/agent rendering omk hud — Full terminal dashboard
The more I use AI, the more convincing it feels. Clear answers. Whether it’s: strategy code writing decision support AI rarely hesitates. And over time, I noticed something subtle. I stopped questioning it as much. Breaking the Expectation We assume better tools reduce errors. Smarter systems. And in many cases, that’s true. But there’s a hidden shift happening: As AI improves, our skepticism decr
My first Cloudflare Worker deployed in 47 minutes. Three of those were spent staring at this exact error in a red GitHub Actions log: Authentication error [code: 10000]. I had the API token. I had the account ID. I had copy-pasted the workflow from the official docs. It still failed. The fix was one checkbox I never selected. That checkbox is the entire reason I'm writing this post, because every
Most candidates overthink "Tell me about a time you failed." They assume the safest move is to soften the story, pick a harmless mistake, or package a "failure" that is secretly a strength. That usually backfires. In software interviews, especially for experienced engineers, a real failure is often better than a polished non-answer. Hiring managers are trying to figure out whether you can own mist