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High Tea is happening in 7 days
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1983.
The past will tell you the future.
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Welcome to Clief Notes. Here's where to start.
1. Watch the intro video and introduce yourself in the intro post here 2. Start with The Foundation (free course). Concepts, folder architecture, prompting framework. Everything else builds on this. 3. Check in at the bottom of each lesson. Polls, discussion posts, other members working through the same stuff. Use them. 4. When you're ready to build real things, move to Implementation Playbooks (Level 2). When you're ready to build your own tools, Building Your Stack (Level 3). 5. Post your work. Ask questions. Help others when you can. What are you here to build?
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Companies want to hire from Clief Notes. So we're building this.
Been sitting on this for a few weeks and figured it's time to show you. 👀 Over the last month, three companies have reached out asking the same thing. How do we hire people from Clief Notes. They've seen what folks here are building with ICM and they want that on their teams. Not LinkedIn AI experts. Not Coursera grads. People who can actually ship. So we're building it. 🛠️ talent.eduba.io Heads up, that's a demo. No real backend, no signups, no live data. Click around and you'll see what the full thing is going to be. A private platform where you list yourself with a real portfolio, companies browse, and they request an intro through us. We make the intro. You take it from there. Few things worth knowing. 🔍 Every profile gets reviewed by the Eduba team before it goes live. The quality bar is the whole point. 🔒 Companies don't see your last name, your employer, or your contact info until we make a formal intro. You can block your current employer too, plus five more companies if you want. Nobody you don't want seeing you sees you. You can list as actively looking, open to offers, or not looking. Passive welcome. Honestly most of the strongest people we've trained are employed and plan to stay that way until the right thing shows up. That's fine. Sit on the platform, see what comes through. 💰 When a placement happens you get a $500 to $1,000 bonus after 90 days in the role. On top of whatever you negotiate. We pay you for staying. This is why the community matters. Companies aren't asking us for resumes. They're asking us for the people who already get it. ICM, agent architecture, knowing when not to use AI. That's not on a LinkedIn profile. Go click around. Tell me what's missing, what's confusing, what you want to see when the real thing ships. We're already building it. 🚀
Who's here? Drop your intro.
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.
I gave my Claude a soundtrack
Last week I gave my LLM a memory layer I call Cortex. This week I started feeding it something stranger: what I was listening to while I worked. A work session is not just the files you touched and the decisions you made. It has a texture. The track that was playing when something finally clicked is part of that memory, even if you would never think to write it down. So instead of throwing that signal away, Claude and I built a small observer to catch it. ———————————————————————— What it does, in three layers: 1. Passive. A tiny watcher checks the local music app once a minute and logs track, artist, and timestamp to a plain markdown file. No browser audio, no streaming history scraped, just what is actually playing on the machine. 2. Bookmarks. When a session opens or closes it drops a marker, so the log has boundaries instead of one flat stream of songs. 3. Flags. When a track lands on a moment that matters, I star it with one line of context. "This was playing when the gallery finally rendered." That markdown file is just another source the brain reads. Same rule as everything else: > Files own the truth. The brain owns the connections. ———————————————————————— Here is the part I do not know yet, and why it is interesting. The episodic layer now carries an ambient track. Does that change retrieval? When I come back to a problem, will the brain surface the session by its soundtrack the way a smell drags back one specific afternoon? Or is it just noise in the index? I genuinely cannot tell you. It has been running for two days. That is the honest bit. This is an experiment, not a feature. I built the observer in an afternoon because the cost of being wrong is a markdown file I can delete. The cost of being right is a brain that remembers the way humans actually do, by association and atmosphere, not just by fact. The try-me is attached if you want to point one at your own memory layer. It is about forty lines. Watch what your brain does with it before you decide whether it earned its place.
I gave my Claude a soundtrack
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Clief Notes
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Jake Van Clief, giving you the Cliff notes on the new AI age.
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