I’ve been playing with AI agent harnesses lately. Basically, what happens between the agent saying “I got this” and me believing it? 😂 🔧 Did the tool finish, or did we just tell it to start? 🔁 If we retry, are we about to send the same email twice? 👻 Can yesterday’s worker wake up and overwrite today’s result? My favorite check is dead simple. Give the agent a file to edit, then have a plain script reopen it and look for the exact change. If it’s missing, the job stays open. Even if the agent wrote a beautiful little victory speech. 🧾 It reminds me of commissioning. “Command sent” and “valve open” get separate lights for a reason. ✅ What’s the dumbest little check that’s saved you from an agent’s victory lap? Bonus points if it was one line of code. 😂

Hi All, new here and looking for a little help with VS Code setup. I use both Claude Code and Codex and so far have been working mainly in Claude Code and Codex desktop environments. But to follow along with the training here I wanted to setup a new folder system that uses the ICM structure and use VS code as I find Codex and Claude aren't great when it comes to opening and editing the markdown files. But I'm also new to VS Code and wanted to double check how people connect Claude and Codex. Is it just a case of using the terminal or is it better to go down the plugins route?

I am not new to software or AI, but have been trying to wrap my head around ICM for a while. Recently ran the script-to-animation workflow, asked it to make up a silly theme. Whadaya know, it worked. :) What I'm interested in, though, is whether ICM can be used for full SDLC. Let's say a game. Hypothetically (wink, wink.) Having trouble figuring out how to break it down, and if the middle "loop" (implement features until done, to oversimplify it) even fits ICM. The planning could probably be a workflow, at least. So I don't know if that should be split off as a separate workflow, create specs, and then - what? Has anybody used ICM for end-to-end software development? Did it work? Challenges? Tips?

I've been poking at Grok Bot before trusting it with anything real. What got my attention is that it's already built a lot like an ICM workspace. Every account gets its own computer with a /workspace folder, and the bots read and write files there. So I set one up the way I'd set up mine: an AGENTS.md at the top, a log, and some tests. Then I tried to break it. This was the web version, one bot (Scout) plus a throwaway second agent to mess with it. Here's what I found. 1. The router isn't guaranteed to get read. Scout had a note in memory to read AGENTS.md before every task. The second agent rewrote the file to v3 behind its back, and Scout's next log line still ended in v2. 2. The description box is where the instructions actually live. Think project instructions. The docs say personal bots don't have one anymore. The web app still does, and it gets read every turn. I put "start every reply with Scout here" in it and that's what it did. When I had Scout research this, it pointed out the docs line and then said, more or less, I'm a personal bot and I clearly have one that I follow every turn. 3. The chat is the memory. Each bot runs one long conversation that gets summarized as it goes. Here we close out and start fresh to control context. Over there you spin up a new bot or duplicate one, and the docs are pretty vague about it. I tried a thread to get a clean start and it still knew a code word from the main chat. 4. Any bot can change the rules. Every bot on the account shares the same workspace. I put a fingerprint lock in the description (stop if the file's hash changes) and it worked for a changed file. Then the second agent edited Scout's description box itself, and Scout can edit its own with one tool call. xAI's own FAQ says don't use separate bots as a security boundary. They mean it. 5. The log caught what the chat didn't. The reply looked fine every time. The one line per task log is where the v2 showed up.

I've been building an AI Workspace and Second Brain using Claude Code, VS Code, Obsidian, and GitHub. As my projects grow, I'm organizing them into separate contexts rather than loading everything into one session. My setup: - AI-Workspace — My central Second Brain, learning system, and shared project instructions. - AI Employees — Rex and Piper, two AI agents that research potential customers and help prepare sales outreach. - RentalManager — Automates rental paperwork and lease preparation, with human approval before generating documents. What each context includes: - AI-Workspace: A root CLAUDE.md that directs Claude to shared instructions, learning notes, and individual projects. - AI-Employees: Agent responsibilities, prospecting criteria, research workflows, and safeguards. Piper drafts emails but cannot send them. - RentalManager: Lease templates, workflow instructions, validation rules, and approval checkpoints. I'm following three principles from the lesson: - One fact, one location: Each project has its own authoritative documentation. My Second Brain points to the appropriate sources instead of duplicating them. - Clean sessions: Claude loads only the context needed for the current task, keeping unrelated project information out of the conversation. - Team consistency: Shared Markdown instructions, project conventions, and GitHub version control make the workflows repeatable, whether I'm using Claude or another AI tool. What I'm testing next: I'm refining how Claude starts new sessions and picks up where I left off. My goal is to open a project and have Claude understand its structure, current status, and rules without having to explain everything again. My biggest takeaway: Scaling AI isn't about loading more information. It's about making the right information available when it's needed.