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
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
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
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
Blueprint Felonies Software isn't a puzzle to solve; it is a liability to be managed. In high-stakes, cloud-native environments, the line between "sophisticated" and "unstable" is razor-thin. With over 17 years in the software trenches, I’ve seen architectural "thinking mistakes" destroy more careers than bad syntax ever could. We often build massive, intricate systems when a simple, focused sol
Discord rewrote their stack multiple times. Elixir for real-time messaging. Python for APIs. Go for microservices. MongoDB for storage. Electron for desktop. Standard startup choices. Ship fast, figure out the rest later. 5 million users. MongoDB couldn't keep up. Switched to Cassandra. 12 nodes. Worked fine. Until 2022, when those 12 nodes became 177. Maintenance got painful. Costs climbed. They
Updated May 2026: Now covers virtual desktop (Spaces) restoration and iCloud sync across multiple Macs, both shipped in ShiftPlus 1.3. TL;DR A complete macOS workspace includes apps, window layouts, browser profiles, virtual desktops, and terminal state. Native macOS saves almost none of it. Most third-party tools cover one slice: Stay and Spencer handle window layouts, Shift handles browser profi
Introduction The Generative AI tooling ecosystem has exploded over the past two years. What started as a handful of Python libraries has grown into a rich, opinionated landscape of frameworks spanning multiple languages, deployment targets, and philosophy bets. As a developer who has shipped production applications using all five of the frameworks covered in this article, Genkit, Vercel AI SDK,