Hi everyone, I got my first AI workflow client: my wife! She's a food blogger who loves writing and content creation but isn't a fan of video editing. I figured this was a good excuse to build a fully custom workflow for her business instead. I've started on the folder structure, but one thing I haven't gone deep on yet is plugins and open-source components (specifically, making sure anything downloaded and use is safe and secure). Would love advice on: 1. Building a video editing workflow - tips or things to avoid 2. Handling raw footage intake - recommended tools and/or best practices for ingesting iPhone footage and reliably producing multiple platform-specific cuts (different aspect ratios, lengths, pacing) without manual re-editing for each platform 3. Vetting open-source plugins - what's your process for evaluating a plugin or library before it touches a client's raw media? (checking maintainers, license, recent commits, sandboxing, etc.) Thanks in advance!

You just saw how video production turns into code. Vote below, then tell us in the comments: what is one repetitive creative task in your work that could become a pipeline?

This one traces AI auditing instruments back to places most people don't expect. Vote below, then tell us in the comments: what surprised you most about where these tools came from?

Tell us three things: 1. What you do (job, industry, student, career-changer, whatever) 2. What brought you to Clief Notes 3. One thing you're trying to figure out right now related to computing or AI I'll respond to every single one. And read each other's intros too because the person who's stuck on the same problem as you might already be in this thread. I'll go first I am Jake, I have been working in tech for 15 Years, building with Generative AI for 3 Years straight now! Excited to teach and learn! That's it. Simple, scannable, gives you data on who's joining and what they need, and keeps the feed clear for content that retains people past week one.

The setup Yesterday I posted about rebuilding my workspace to be agent-agnostic. Plain markdown, plain YAML, env-var paths, no Claude-specific lock-in. The thesis: when the tooling layer churns, the workspace outlasts it. I did not expect to test that thesis the next day. What happened Claude went down mid-task. I opened Codex CLI in the same workspace. Same skills loaded. Same memory. Same briefs. Same manifests. Codex read the workspace exactly the way Claude reads it, because the workspace is just files. The task done. Faster, actually. Zero downtime. Zero re-plumbing. Zero "let me port my setup." Why it worked Three properties carried the swap. The orchestration layer is plain text. Briefs, manifests, memory, voice rules. All markdown and YAML. Any agent that reads files reads my workspace. The agent-specific bits are isolated. Hooks, slash commands, settings live behind one entry point. Codex doesn't need them. The rest of the system functions without them. The skills are portable. My skill definitions aren't Claude-shaped, they're task-shaped. Codex picked them up and ran. The lesson When you build your stack around one tool, an outage is a stop. When you build your stack around your workspace, an outage is a tool swap. The agent is a worker. The workspace is the contract. Workers are interchangeable. This is the whole point of decoupling. What I'd do differently Nothing. Yesterday's migration was the work. Today's outage was the dividend. If you're still running everything inside one agent's surface, that's a single point of failure dressed up as convenience. Pull your config, briefs, and memory into plain files. Put the agent-specific layer in a sidecar. Test the swap before you need it. You will need it. // A<3