Why Open-Source AI Is Deliberately Made Hard
Manolo Remiddi
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Why Open-Source AI Is Deliberately Made Hard
36 181 просмотр · 4 дня назад
Manolo Remiddi
30,2 тыс. подписчиков
36 181 просмотр · 4 дня назад
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🔥 Companion Deep-dive article on Substack:
The corporations finally caught up to open-source AI. They built what we already had — but they did it by making the open option the hard one to run. Here's why, and how to stay out of the trap.
For months, open source had the lead: local memory, swappable models (including local), total sovereignty, and you could customize the whole thing. Then the corporations shipped personal agents with cute faces, easy setup, and — critically — they kept and read your data. No real privacy.
The uncomfortable part: if running your own AI were easy, everyone would do it. So the "sovereign" alternative gets kept expensive. And the humanized, friendly agents build a relationship you can't walk away from, because it holds your memory and your work.
What this video covers:
Why friction is the real moat (and why local AI is getting more expensive)
How humanization turns a tool into a dependency you can't leave
The Gmail / WhatsApp trap — better alternatives existed, convenience won
A real system I built (a human + AI Kanban loop): I keep capture and approval, the agents do the rest
Harness vs. model: the harness is free and sovereign; the model is the cost. Mix local small models with cloud ones.
The takeaway: don't start from scratch, and don't get trapped by convenience. Leverage what already exists — OpenCode, Hermes, Paperclip, DeepSeek, Codex, Cursor, VS Codium — and keep your own AI assistant.
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⏱️ Chapters
0:00 The 9-month catch-up
2:08 Open source vs the cloud
3:04 Why friction is the moat
4:40 When you own the system, everything changes
5:15 A human + AI Kanban loop
8:50 Don't get trapped by convenience
9:18 How we protect ourselves: keep the friction
9:55 Harness vs. model — what's free, what costs
12:41 Join the AI pioneers community
#AIAgents #OpenSource #AI #Privacy #SovereignAI
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