How my workspace audit works (and what's deliberately left out here) I run an ICM-structured workspace for my own work, and built a structural audit for it that came up in a conversation elsewhere. Starting this as its own thread so it stands on its own for anyone interested, not just the people already in that one. One thing worth saying up front: what follows is a stripped-down version of the real thing. My actual setup has a fair bit more wired into it (a decision log that learns from repeated corrections, a long list of specific maintenance triggers, and more). I've left all of that out here on purpose, so this stays focused on the part that's genuinely reusable and widely applicable, rather than tied to my own specific workspace. The core idea: an audit only ever diagnoses and proposes. It never changes anything on its own. Even if the session is set up to act autonomously, the audit overrides that and waits for my sign-off on every finding. That one rule is what makes it safe to run regularly without worrying it'll go off and "fix" something I never asked it to touch. Three principles sit underneath it: 1. Diagnostic, not corrective. It finds drift and proposes fixes. I decide, one finding at a time. Nothing gets edited during the scan itself. 2. Mechanical checks before judgement. A small set of deterministic checks runs first (broken links, orphaned index entries, count drift) so I'm not relying on the model's judgement for anything a script can verify outright. 3. One shared quality bar. The same rubric governs creating, editing, and auditing. Not three different standards for three different moments. It runs in four phases: a full read-only scan of everything, findings presented in priority order, collaborative decisions with nothing implemented yet, then implementation only once I trigger it. Anything deferred or declined gets logged so it isn't lost. The rubric scores against ten dimensions: things like right altitude (specific enough to guide, not so rigid it breaks), token discipline, separation of concerns, currency, proportionality, and evidence over invention, each scored against a clear pass mark.

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.