Jev is the shiny new thing taking over AI conversations right now. It is not another chatbot. It makes fast, structured decisions instead of writing paragraphs. That is why developers cannot stop talking about it. If you are vibe-coding (prompting your way through projects without really knowing what the code is doing), stop. Learn the fundamentals first. Do not drop Jev into your workflow until you understand loops, data, APIs, and how your own program actually decides things. If you already know how to code, Jev is extremely useful. It is fast, cheap, and built for software that needs quick yes/no, pick-one, or score answers it can act on immediately. Bottom line: Learn to code first. Then Jev becomes a powerful tool instead of a shiny distraction.

Remote Control solves a real problem for me. I run several long sessions at once, and my render factory builds take time. Right now, if one of them stops and needs a decision, it sits blocked until I am back at my desk. That is wasted time, sometimes hours, on a build that was one answer away from finishing. My workflow: I kick off a render-factory build and walk away. The factory is built to stop instead of guess, so when it hits something it cannot verify, like a color that is not confirmed in the library, it flags it and waits. With Remote Control, that question shows up on my phone. I answer it, the build keeps going, and I put the phone away. This is the quick-intervention case from the lesson, and it fits exactly how my factory already works. What matters most: everything my factory needs is local. The DWG drawings, the material library, the rules, the whole workspace. The cloud version of Claude Code would start fresh with none of that. Remote Control keeps it all on my machine and just lets me reach the session from my phone, so I get the mobility without giving up the setup that makes the factory work. No SSH tunnels, no custom front-end. The one thing I have to manage is keeping the computer running while I am away, since the session lives on my machine. The honest downside for me: I already think about this work constantly and rework it more than I should. A feature that lets me check a build from anywhere is also a feature that lets me never step away, and the people around me notice. So the discipline is not the access, it is deciding when to leave it alone. I want to use this to check in and put the phone down, not to keep working through dinner.

This is for people who are not technical. Someone with a normal job who saw something on YouTube and wants to be able to make something similar for practical use. Live with Danney Trieu · Four months ago I had never made a single AI video. I'm not a deeply technical person and never done any film in my life before, I'm just very passionate about this space. I figured out where the decisions live — which ones you hand to the machine, and which ones you take back. Then I found Jake & ICM just like many of you. Once I saw his method, everything got easier. More valuable than that, I found this awesome community that keep motivating me and supporting me. I like to give it back.. Every Saturday, I'm hosting a Skool webinar here: 🚩*UPDATE (09/22): Class cancel on 26th. I have to fly out to prepare for a visual staging concert in Vegas. I hope to learn a lot from this experience and bring back what some knowledge for you guys. Until then, keep on vibe filming ! Session records: https://drive.google.com/drive/folders/1TNk-42gZajrTWw-2tD3Fbn760UE-BeM_ Reelix explainer: https://danneytrieu.github.io/vibe-film-sessions/index.html Higgsfield affiliated link: https://higgsfield.ai/?fpr=nguyen61 My discord channel: https://discord.gg/EKab83ZGB Youtube: https://youtu.be/dryW_H54Rss ━━━━━━━━━━━━━━━━━━━━ Here's what I'm covering -> 🪜THE SIX LEVELS OF AI FILMMAKING 1 · PROMPT — you type it, the model decides everything else 2 · REFERENCE — you stop describing the look and start showing it 3 · MEMORY — your world lives in folders, so you stop re-explaining it 4 · BLOCKING — you decide where the camera goes, not the model 5 · DNA — your taste, written down, applied automatically 6 · AUTOPILOT — you point it at your work and it hands you the shot list

I’ve done a master’s thesis before: genetic algorithms, XGBoost, Monte Carlo simulations, graph optimization. Lots of ML, long before modern AI could meaningfully help me. Back then, the hard part wasn’t having ideas. It was turning those ideas into structured experiments, reliable evidence, useful visualizations and clear conclusions, without losing weeks to setup, documentation and context switching. ICM changed the game. In one week, I’ve done more research than I managed in six months of my master’s thesis. The difference is not just speed. It’s the number of ideas I can now explore, test and refine: - Turn a vague idea into a concrete research question. - Convert that question into an experiment or proof obligation. - Generate the code, tests and evaluation structure. - Produce visualizations and evidence automatically. - Inspect the results and use them to guide the next idea. - Keep the reasoning, context and decisions connected throughout. - Do all of the above concurrently. The ICM becomes a research operating system. It helps separate what the code proves, what the experiment observes, what the data supports and what still requires human interpretation. That distinction matters. A test can show that the implementation works. It cannot prove that the model is useful. A result can show that one approach performed better. It cannot automatically explain why. A paper can inspire an architecture. It cannot validate that architecture on your dataset. ICM gives each of these things a place—and connects them into a traceable research loop: question → hypothesis → experiment → evidence → interpretation → next question The most powerful part is that ICM does not replace the researcher. It amplifies the researcher’s ability to think. The human provides judgment, curiosity and scientific direction. The ICM provides structure, memory, continuity and execution. That is why it feels so transformative for ML research. It turns research from a sequence of disconnected tasks into a living system that can continuously generate, test and refine knowledge.

I don't want a progress percentage today. I want to know what's actually changing. If you've been working through Building Your Stack, what is one decision you've made because of it? - Maybe you added something. - Maybe you removed something. - Maybe you realized part of your current stack makes absolutely no sense. 😂 Whatever it is, post it below. What did Building Your Stack make you rethink? And if you've been flying through the lessons but skipping the quizzes or accountability posts... Go back. Those are there to make sure you can actually explain and apply what you're learning rather than just consuming it. Drop your biggest realization below. 👇 And keep going. New roadmap next Friday, September 25th.