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Leo

@leonard

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425 Followers


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balajis
@balajis.eth
We just launched the Network School Fellowship. Anyone from anywhere can apply for $100k in funding. Apply online at ns.com. https://x.com/balajis/status/1913280710531826057
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Leo
@leonard
@balajis.eth was reading this by Antirez https://antirez.com/news/150 this morning and felt aligned.
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Leo
@leonard
If you know you know. If not… start the LearnOS. Hic sunt dracones 🐉 https://deserted-ladybug-896.notion.site/NS-Learn-OS-1c4e55b8653780ca9c9bc23db470c352
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Leo
@leonard
The revolution won’t be tweeted. But maybe… it will be sung. By a child with pizza in hand, while a track nobody asked for changes everything.
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Vitalik Buterin
@vitalik.eth
My own current privacy roadmap (much lighter on L1 changes, but also more limited in its consequences): https://ethereum-magicians.org/t/a-maximally-simple-l1-privacy-roadmap/23459 Highly encourage people to read both! https://x.com/pcaversaccio/status/1909939119037313255
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Leo
@leonard
XP x zkSync — Learnathon 2 inside Network School 72h of crypto, proofs & XP quests. Not a class. A sovereign mind activation. Porter Adams from @zksync — live kickoff Sat 10am SGT / Fri 10pm EST / Fri 7pm PST Join via Luma: lu.ma/d2uc3czt
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Leo
@leonard
First we built. Then we remembered. Now we call the world in the open. 🌏 Learnathon Archive — live. https://deserted-ladybug-896.notion.site/Learnathon-Archive-1cfe55b86537808788f5fefad261b8c8
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Leo
@leonard
Curious how it all played out? Notebook, repo, personal log — it’s all there. GitHub: https://github.com/leomanfree/Creative_Fork_FastAI_LearnSprint/blob/main/README.md Full Learn Sprint: Day 1 → Day 3 Let’s build. And rethink what it means to learn.
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Leo
@leonard
Final outcome: – Model fine-tuned and tested – Repo cleaned and logged – 3-day Learn Sprint complete – Philosophical reflections on code and cognition Now I rest. But the loop continues.
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Leo
@leonard
So I logged a new block in my repo. Not technical — conceptual. From Brilliant Sophists → Human-AI Neuroengineering The future isn’t AI that becomes human. It’s humans who become hybrid, on purpose.
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Leo
@leonard
So I logged a new block in my repo. Not technical — conceptual. From Brilliant Sophists → Human-AI Neuroengineering The future isn’t AI that becomes human. It’s humans who become hybrid, on purpose.
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Leo
@leonard
That’s when I dropped the code and opened Aristotle, Hegel, Jung, and von Franz. "LLMs are brilliant sophists. They optimize likelihood, not meaning." Statistics ≠ cognition Probability ≠ insight Fluency ≠ understanding
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Leo
@leonard
This time, I started from my own repo. Refactored the minimum viable engine: DataBlock → Learner → Train → Predict Then I fine-tuned it. It worked. Prediction successful. But again — was that mine?
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Leo
@leonard
http://Fast.ai says: "train a model to classify pet images." So I did — minimal code, clean results. But I kept asking myself: “Is this just copying — or am I learning?” Today, I hit the edge of that question.
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Leo
@leonard
Day 3. Final lap. I trained, fine-tuned, and tested a model from scratch — again. But this time, I added something else. Philosophy. Neuroengineering. A reflection on what hybrid intelligence really means. 👇
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Leo
@leonard
If you’ve been learning in public, rebuilding from first principles, or just trying to own your tools — you’re not alone. Follow along. Tomorrow: Day 3. The Learn Sprint finale. (9/9)
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Leo
@leonard
I’m not here to master everything. I’m here to practice *thinking like a builder* - even when the AI isn’t there to help. Call it autonomy. Call it sovereignty. Call it monk mode. (8/9)
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Leo
@leonard
(Yesterday, it called a mixed-breed dog a shiba_inu…) My friend laughed: “That's not even close.” But honestly, I was just proud it *ran*. Today, I’m proud it’s accurate — and that I understood *why*. (7/9)
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Leo
@leonard
Today’s result? I trained the model. Fed it a test image. Got a clean prediction: > basset_hound (99.99%) > This time, it was actually right. (6/9)
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Leo
@leonard
But fixing that one stupid bug felt better than completing the entire model yesterday. I learned something deep: AI makes you fast — but debugging without it makes you *present*. Every character matters. Every layer sticks. (5/9)
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