Watch the lesson first, then come back here. Your job: 1. Write your one-sentence definition of an AI agent. Use the four verbs from the lesson: perceive, plan, act, observe. Do not copy mine. Build your own version. 2. Read two other definitions in the comments. Find one edge case their definition does not handle. Reply to them with it. 3. Use the template below to structure your answer. Copy and paste it into your reply. I will read every definition posted here and push back on the interesting ones. What is the hardest part of this definition to get right?

These members helped, posted, asked questions, or shared progress this week: 1. @Ewan Park 2. @Aakash Choudhary 3. @Hemant Kumar If you want to appear here next week: - Post your build log - Help someone in comments - Share one useful resource

Nobody talks about this: the 'thinking out loud' style of reasoning models is mostly noise for production. You don't ship a chatbot that goes 'Let me think... I should consider...'. Strip the chain-of-thought from outputs. Reasoning is useful internally, ugly externally.

Hi Everyone, This is my first project AIOS v1 -> https://github.com/attacker-codeninja/AIOS About -> Unified AI terminal for Claude, Gemini, and Ollama — session memory, custom commands Don't make users manage AI tools. Make one tool manage the AI tools. A manager shell for Claude Code, Gemini CLI, Kimi, and Ollama , built in Rust. Please check and provide your feedback and contribute if want and We can improve it for v2

Killed a 3-week project today. Built a "smart" meeting summarizer for a client. They didn't want it. They wanted just the action items in their slack which would've taken 1 day. I overbuilt because I fell in love with the architecture. Lesson: ask "what do you do with the output?" BEFORE designing the agent. Output drives architecture, not the other way around. Anyone else have a "wow that was an expensive lesson" moment this week?