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https://thenetworkstate.com
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Leo
@leonard
🧵 Learn Sprint — Day 1 I’m a philosopher. Not a developer. But today I started the fast.ai course — because I believe AI sovereignty starts with learning to think in code. This is Day 1 of my Learn Sprint. (1/10)
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Leo
@leonard
I didn’t follow the official path. Skipped the video. Opened @muellerzr’s notebook. Ran the code. The model trained — eventually. After 10 minutes of setup… and nearly an hour per epoch. On loop. (2/10)
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Leo
@leonard
That’s when I realized: Starting from theory feels fast, but it’s passive. Starting from practice feels slow — but it sticks. Watching teaches concepts. Waiting teaches patience. Building teaches truth. (3/10)
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Leo
@leonard
The model worked. Loss dropped. Predictions made sense. But I asked myself: Could I rebuild it tomorrow — from scratch? Without guidance? Without AI? Not yet. That’s where real learning begins. (4/10)
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Leo
@leonard
Socrates once said: “The craftsmen are wiser than the wise, because they act — but they don’t know why they know.” Today we could say: The builders are fast — because AI helps. But could they build alone? (5/10)
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Leo
@leonard
That tension shaped my framework: Build → Distill → Share → Repeat Start from motion. Backfill the meaning. Teach it forward. (6/10)
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Leo
@leonard
This isn’t about completing a course. It’s about building autonomy — not just velocity. And I’m testing how this could scale. (7/10)
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Leo
@leonard
You could ask: “Why worry about building without AI — if it’ll always be there?” Because it’s not about rejecting AI. It’s about understanding what it’s doing — so we can use it better. I don’t want to depend on AI. I want to be supercharged by it. And that starts with learning the fundamentals. (8/10)
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Leo
@leonard
I spent 11 hours on Day 1. Not just coding — waiting, reading, debugging, reflecting. My notebook is alive. So is my doubt. More tomorrow. (9/10)
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Leo
@leonard
Oh, and about that prediction? The model said: “Shiba Inu — 91.62% sure.” Reality said: “Nope.” Just a lovely mixed-breed dog on some steps. The model was confident. But it was confidently wrong. Just like us, sometimes. That’s why real learning matters — beyond the output. (10/10)
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