Sentient Foundation
@sentient_found
Sentient Foundation is a non-profit dedicated to open-source AGI, free from single-entity control. Advancing the future through @SentientAGI.
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AI is permeating every layer of the enterprise, making businesses worldwide dependent on products that lock them inside black box infrastructures.
An open, community-driven architecture gives businesses the full transparency, flexibility, and sovereignty to tailor AI to their exact needs.
Think outside the black box. Go open-source.
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Today we’re launching the OpenAI Deployment Company to help businesses build and deploy AI.
It's majority-owned and controlled by OpenAI. It brings together 19 leading investment firms, consultancies, and system integrators to help organizations deploy frontier AI to production for business impact.
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The biggest open-weight models right now are still being released by the same companies building the closed ones.
Meta, NVIDIA, and even OpenAI.
If the open-source world depends on Big Tech to share its scraps, how open is it really?
Full episode:
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Cocktails, jazz, and 300 of SF's best AI researchers and engineers in one room building together 🍸
@sentient_found is grateful to be a part of this night with our friends from @smallest_AI and @ThineAI.
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The number of people building AI will explode from low millions today to potentially 100 million, as coding agents lower the barrier to entry.
This wave of new builders will tackle real problems in biology, medicine, chemistry, and more.
And open-source AI is how we get there.
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"It would be a mistake for any country to try to slow down open source.
A country that leads open source is a country that can lead AI in general"
@ClementDelangue, co-founder & CEO @HuggingFace
Watch the full interview on YouTube:
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You built nothing.
You trained for nothing.
But AI is making decisions on your behalf.
@sewoong79 on why the models you use every day aren't loyal to you, and who they're actually loyal to.
Full episode:
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Open source AI is the strongest lever against this.
Without it, intelligence belongs to three or four labs.
So what are you building: the open future or the anti-human one?
📁 Tristan Harris, co-founder of the Center for Humane Technology, says the AI race is no longer about augmenting human work but replacing it at planetary scale.
Once GDP depends more on data centers than people, governments stop investing in humans because humans no longer drive growth.
That is how an intelligence economy quietly becomes anti-human without anyone explicitly choosing it.
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Linux won in the 90s. Can open-source AI do the same?
@sewoong79 's take: for general everyday use, we might already be approaching a plateau where open models are roughly as good as closed ones.
The real gaps are in the specific, specialized tasks.
Full episode on @opencommonspod :
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If the model becomes the interface for reality, then Software 3.0 is the most centralized computing paradigm ever proposed.
You give an input to a black box that answers based on its proprietary reasoning and alignment.
Open models are the only future where users, developers, and small companies retain leverage.
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Andrej Karpathy says Software 3.0 makes the app layer redundant.
MenuGen was built in the old paradigm: OCR the menu, parse the dishes, generate images, and render a new interface.
Then Gemini did it directly inside the photo.
The more work the neural network does, the less software exists between intent and output.
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Five things we took away from our conversation with @sewoong79:
→ Your data is not anonymous
→ Synthetic data might be the real privacy fix
→ The real moat in AI is compute
→ Loyalty is AI's hardest unsolved problem
→ AI is heading toward diversity
Full recap by @0xsachi:
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Open-source AI isn't just shifting east, it's redrawing the entire map.
Chinese labs @deepseek_ai and @Kimi_Moonshot are out-shipping the West, open-source goes quantum with @nvidia, and the Pentagon makes open weights part of its national security stack.
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People assume Big Tech is ahead because they build better models. @sewoong79 says the real answer is simpler: compute.
They train with thousands of GPUs in parallel. In the open-source world, 32 is a luxury.
Full episode on @opencommonspod :
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Every closed-source AI lab is essentially an “extractive institution”.
Build your own stack. Run models locally.
Open-source is the real freedom.
The goal of Personal AI: civilization where individual humans, augmented by AI, can do consequential work without being captured by extractive institutions.
Freedom to write your prompt and own your data.
This is the new battleground.
2034 won’t have to be like 1984.
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Anthropic built its brand on safety. Then adjusted its values to close a Pentagon deal. So who should actually be setting the governance frameworks for AI?
@sewoong79 's answer: right now, a few decision makers who don't answer to any publicly elected officials. No problems yet. But unchecked power doesn't stay unchecked forever.
Full episode on @opencommonspod:
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Open weights aren't enough. The loop that improves an agent, how it learns from failure, which skills it keeps, where it runs, needs to be open sourced too.
EvoSkill v1.1.0 (now on @Docker + @daytonaio) is @SentientAGI's open-source bet on AI evolution research: agents building themselves through prompts, skills, memory, and eventually the harness.
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Introducing EvoSkill v1.1.0
This release introduces support for running the EvoSkill loop on remote environments using @Docker and @daytonaio.
See it in action ↓
AI models are mostly either closed and API-gated, sacrificing transparency and local execution, or openly distributed, sacrificing monetization and control.
But there's actually a way to enable open-source models that are freely distributed for local execution to be economically sustainable.
Comment "OML" to learn how.
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Open Source's big problem.
Last night I went to a Y Combinator party in San Francisco and met an entrepreneur who is making a top Open Source AI model.
He told me it is very hard to make money in open source. Yeah, it is cool being popular, he told me, but figuring out how to make a business out of it is proving to be very difficult.
The Chinese are pounding the price into the ground with their open source models. Which makes it tough.
In the old world of Open Source you could make money with them by consulting, service, etc, like RedHat did.
But in this new world, he told me, it's much harder to make a good business out of it.
Is anyone making a good business out of open source?
What would your advice be to the businesses that are trying to support Open Source?
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The full conversation between @0xsachi and @sewoong79 is here.
An hour on inferential privacy, synthetic data, why compute is the real moat in AI, and the hardest unsolved problem in AI: loyalty.
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