Taking a sabbatical to contribute to OSS and learn in public is something. Hope you find a great Rust gig soon!
@whats_ai
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Perfect Weight Prediction as AGI Indicator
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If it can give ALL weights accurately we would definitely be past AGI already
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Prompting vs RAG vs Training: Understanding Model Adaptation Methods
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When you paste your company data into ChatGPT, you are not retraining the model. You are giving it temporary context for that conversation.
— Louis-François Bouchard 🎥🤖 (@Whats_AI) 1 avril 2026
Same with RAG and embeddings: you are not injecting knowledge into the model’s brain, you are giving it access to external memory it can… pic.twitter.com/KllVuKY7kTWhen you paste your company data into ChatGPT, you are not retraining the model. You are giving it temporary context for that conversation. Same with RAG and embeddings: you are not injecting knowledge into the model’s brain, you are giving it access to external memory it can search when needed. Training is the part that actually changes the model’s internal weights and reshapes how it behaves. That is why prompting is great for context, RAG is great for controllable memory, and training is best when you need the model to truly adapt to a new style, skill, or domain. Huge difference, and it matters a lot when you are deciding where to spend time and money. Curious where fine-tuning fits in this stack? I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
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LLMs Replace Mediocrity, Not Expertise Quality
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LLMs are not making expertise less valuable. They are making mediocre work easier to replace. Quantity is cheap now. Judgment, taste, and direction are not. We need fewer people. But we need better ones. To be clear, I am not saying beginners are doomed. I am saying the path changed. The people who learn fast, care about quality, and use LLMs to improve their judgment will do very well. The ones focused on shipping and quantity will be the ones replaced first.
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AI in Education: Tools Yes, But Learning Still Matters Most
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I sincerely hope the first take in this post is trolling. Yes, school should evolve. Of course it should. AI changes what we do and how we do it. But the point of education was never just memorizing facts or producing perfectly formatted essays. It is learning how to think, how to question, how to analyze, how to work with others, how to struggle through a hard problem, and how to grow. Calculators did not make math useless. Computers did not make learning obsolete. And AI will not make education irrelevant. What we teach will keep changing. It has to. But learning itself still matters. A lot. So do attention, discipline, curiosity, friendship, and the ability to build your own mind before outsourcing everything to a machine. Use AI in school, absolutely. But do not confuse using a tool with becoming educated. I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
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Evolutionary Prompt Optimization vs Direct Model Iteration
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Evolutionary prompt optimization is super interesting. How does it compare to just iterating with the model directly? 🙂
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Multi-model routing for deep research systems
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Multi-model for deep research makes sense. Curious how it picks which model for what, or if users control the routing.
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Agent Economics: Ownership and Reputation Rights
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Agents as economic actors is the inevitable next step. The question is who owns the agent's output and reputation.
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Open Source Vulnerabilities Threaten AI Application Supply Chain
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The axios attack is a reminder that our entire stack runs on trust in a handful of maintainers. Scary when you think about how many AI apps depend on these packages.
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Public perception versus practitioner reality in AI
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The gap between how the public sees AI and how practitioners experience it day to day is something.