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
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
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
If you've ever managed multiple GitHub accounts on the same machine — a personal account, a work account, maybe a freelance client account — you know the pain. You clone a repo, push some code, and then realize it went up under the wrong username. Or worse, you spend 20 minutes debugging why your SSH key isn't working, only to find out you're using the wrong identity file. I got tired of it. So I
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
I started where a lot of us do: a LangChain RAG walkthrough. You chunk some text, embed it, retrieve top‑k chunks, and wire an LLM to answer questions. It clicks quickly, which is exactly why it’s easy to walk away thinking you’ve “done RAG.” What bothered me was that the demo corpus is usually tiny and artificial. I write on DEV.to about things like NLP routing and CNN image classification. If I
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