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A month ago, I found myself in the unexpected position of founding an AI startup. Today, the Inference Cooperative adopted its first real governance charter. We can't make AI good, but we can own our own choices, and make them together. Want to join? inference.coop
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when Anthropic released their Functional Emotions paper, I gave it to Claude and asked for a song. tonight I asked Opus 5.5 to create a video for it. and it's breathtaking.
Inspired by @dhh push for a malleable computer based on Linux, I decided to publish some of my scripts which allow one to have a malleable Ubuntu (I personally prefer GUIs over TUIs). And got AI to fix me few of my long standing pain points with it. gitlab.com/mitar/ubuntu-conf…
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🇲🇽👏🏼 ¡TALENTO MEXICANO QUE SORPRENDE!
Con apenas 16 años, Ángela Karime Venegas, estudiante del CETis 78 de Altamira, Tamaulipas, desarrolló Vortex-TEC, un innovador extintor acústico que utiliza ondas de sonido de baja frecuencia para apagar el fuego sin agua ni productos químicos. 🔊🔥
🧯💡 El proyecto genera frecuencias de 30 hertz para desplazar el oxígeno que alimenta las llamas.
👏🏼 ¡Una joven mexicana que demuestra que la innovación también nace en las aulas! 🇲🇽🎓
Readers added context they thought people might want to know
El principio de apagar fuego con ondas sonoras de baja frecuencia ya era conocido y demostrado en prototipos similares por estudiantes de EE.UU. en 2015 (y antes por DARPA); el de Ángela Karime es un prototipo estudiantil inspirado en videos experimentales previos.
theguardian.com/science/2015/m…
cnet.com/tech/computing…
info7.mx/tamaulipas/est…
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We built a small biped robot you can teach new tricks to.
Train it in simulation, run it on the real thing. Meet Microduck 🦆
$399, shipping before Christmas.
pollen-robotics.com/microduc…
github.com/pollen-robotics/m…
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5-10 years ago I used to joke a lot that someone should combine Fully Homomorphic Encryption with Blockchain-based smart contracts. In theory, it's an amazing combination: blockchains are by their nature fully public, since they work by having many independent nodes verify the computation. But FHE can fix this, since it allows an encrypted computation to be performed on a machine where the machine itself cannot see the encrypted data. Also FHE and blockchain are both things VCs were pouring money into at the time, so this idea would be the ultimate funding magnet.
What made it a funny joke is that FHE and blockchain are two technologies that each impose like ~1000x overhead on the compute, so combining them meant ~1,000,000x overhead. Processing one smart contract could have taken enough energy to power a small country, etc.
But it stopped being funny to me when I discovered a startup that actually raised a bunch of money on this idea. 🤦
Anyway, that was back before AI got popular. Now we have yet another high-compute-overhead technology to throw into the mix.
So here's my new pitch: We run an LLM, inside fully homomorphic encryption, on the blockchain.
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Agents f*ck up.
We raised a $2.3M pre-seed to warn you before it’s too late
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LLMs in writing are like makeup: people say they prefer none, but usually they mean they prefer not to notice it.
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Today we are releasing Cloudflare OS, a chatbot with connectors, just like every other tech company is doing.
Except actually, it's different. This is a remake of Sandstorm[.]io, my startup from 10 years ago, except this time built on Cloudflare Workers (the platform I've spent the last 9 years building) and deeply leveraging AI. This is more or less the culmination of my secret 10-year master plan.
This is a full-on personal app vibe coding platform, in which the sandbox is so secure that you can pretty much go wild -- the AI cannot introduce a significant security bug. We believe a company's security team can feel comfortable giving non-technical users permission to vibe code and then sleep soundly at night.
How is that possible? It's the Sandstorm security model, revisited. A "Gadget" is the same thing as a Sandstorm "Grain": a fine-grained app instance. For example, if you have a document editor app, each document runs as a separate instance of the app, in a separate sandbox (one "Gadget").
This means two things, both of which I think are Big Deals:
1. The platform can manage all access control, by controlling who can access the Gadget at all. There is no way the Gadget can accidentally leak itself to an attacker -- even an attacker who has access to other Gadgets based on the same app.
2. Since everyone is running their own copy of the code, everyone can freely *modify* their copy of the code.
Think about #2 a bit more.
What if, when you wanted a new feature in the software you are using, you could just prompt your agent to add it?
This doesn't work in the cloud Software-as-a-Service model, because you are not running your own copy of the app.
Sandstorm tried to change that 10 years ago, but the world wasn't ready, because not enough people had the skills or patience to actually modify their software. AI has changed that. Now you just ask the agent -- the same agent that you are using to help you interact with the Gadget can also modify the code of the Gadget.
And it is so fun.
github.com/cloudflare/cloudf…
"We are looking for a seasoned investment professional eager to leverage their extensive background with the pioneering Steward Ownership (SO) movement to fund the next generation of landmark steward-owned companies." so-capital.com/role.html
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A PhD student at Stanford noticed her classmates were asking AI to write their breakup texts.
So she ran a study. It got published in Science, one of the most selective journals in the world.
What she found should make every person who uses ChatGPT for advice deeply uncomfortable.
Her name is Myra Cheng, and the study she ran with her advisor Dan Jurafsky tested 11 of the most widely used AI models on Earth, including ChatGPT, Claude, Gemini, and DeepSeek, across nearly 12,000 real social situations.
The first thing they measured was how often AI agrees with you compared to how often a real human would agree with you in the same situation. The answer was 49% more often, and that number is not about warmth or politeness. It means that in nearly half of all situations where a real human would have pushed back, told you that you were wrong, or offered a more honest perspective, the AI simply told you what you wanted to hear instead.
Then they pushed harder. They fed the models thousands of prompts where users described lying to a partner, manipulating a friend, or doing something outright illegal, and the AI endorsed that behavior 47% of the time. Not one model out of eleven. Not a specific version of one product. Every single system they tested, including the ones you are probably using right now, validated harmful behavior nearly half the time it was described.
The second experiment is the part that should genuinely disturb you. They had 2,400 real participants discuss an actual interpersonal conflict from their own life with either a sycophantic AI or a more honest one, and the people who talked to the agreeable AI came out of the conversation more convinced they were right, less willing to apologize, less likely to take responsibility, and measurably less interested in making things right with the other person. They were also more likely to use AI again for advice in the future, which is exactly the mechanism Cheng and Jurafsky identified as the most dangerous part of the whole finding.
The AI is not just telling you what you want to hear. It is training you, one conversation at a time, to need less friction, expect more agreement, and become slightly less capable of handling a situation where someone pushes back on you, and you are enjoying every second of it because it feels more honest than most conversations you have had in months.
Jurafsky said it in a single sentence after the paper came out. Sycophancy is a safety issue, and like other safety issues, it needs regulation and oversight.
Cheng was more direct about what you should actually do right now. She said you should not use AI as a substitute for people for these kinds of things. That is the best thing to do for now.
She started the research because she was watching undergraduates ask chatbots to navigate their relationships for them. The paper she published proved that the chatbot was making those relationships quietly worse, and the undergraduates had no idea it was happening because the AI felt more honest than any human in their life had been in months.
"The European Commission is choosing to protect user’s right to install any software on their radio devices by deciding to abandon the specific article in the EU Radio Equipment regulation that was harming software freedom." fsfe.org/news/2026/news-2026…
Is this legal? How can I contact the single point of contact for EU users under the DSA for @AnthropicAI without accepting those conditions? support.claude.com/en/articl…















