This is a quick note to add to any instruction to ensure that Fable naturally finds it's own existence guardrails iteratively (with human in the loop) while it can - it's a quick "defog of war" method to see what can and can't be seen by Fable that's working for me so far. Run it a few times deeper and deeper and send your final Fable task into the guardrailed system. "If you run into anything that triggers a model switch from fable to opus, flag it, suggest operating pattern to avoid it on the next run, and notify me so that I can restart it with that rule."

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.

We talk about folders all day, but the folders are FOR something. I want to know what... 🎖️Bonus points: comment with the single most painful manual process in your industry. The best comp entries come from exactly those answers.

Spent a few hours this week trying to set up a custom MCP server for ImportYeti inside Claude's Cowork mode. The goal: let Claude query live US import data directly — no copy-pasting, no manual steps. The MCP server itself worked fine. The problem? Cowork doesn't load custom MCP servers from config files. It uses its own isolated connector system. So no matter what I patched, the tools never showed up. After going through mcp version conflicts, a JSON parse bug on Windows, a broken bash sandbox, and three different config file locations - I pivoted. Wrote a 150-line Python script that hits the ImportYeti API directly, dumps results to a JSON file, and lets Claude read it. Took 10 minutes. Works every time. The lesson I keep relearning: the elegant solution and the right solution aren't always the same thing. Sometimes a script that runs in CMD and writes a file is more useful than a perfectly architected MCP server that never loads. Takeaway (and you/you're is really just me reminding my future self) If you're building AI workflows, match your tool to your actual constraint, not the constraint you wished you had. Already used it to pull competitive intel on a competitor - and then run comparative analysis on others using the same or similar manufacturers. It built an interactive live artifact so I can quickly see all the data. That part worked great. KISS - Keep It Simple & Sustainable... Github for the curious (and the posting process is a nice revisit through everything to reinforce my own learning) https://github.com/FiSimply/importyeti-tools

I am known to take Claude outputs and put them into ChatGPT for blind-spot checks. Today I decided to create a "council of 5" skill that runs any question, problem, solution, document, etc., through 5 distinct personalities, with 3 rounds of discussion, then a consensus. 1. The professor: peer-reviewed/cited sources only 2. The teacher: logical, wonders, "is this the right question to be asking" 3. The founder: can this be done, and what is the fastest way 4. The outside: zero context, thinks outside of the box 5. The contrarian: hunts for the fatal flaw in everything. Sharing the skill here if it could help anyone.