CEO @entolabs, AI x energy engineering. Prev co-founder @wiseio acquired by @GE and machine learning x astrophysics @UCBerkeley

Denmark
Joined April 2009
I used to think this but then I realized MCPs are much more effective if they are designed kind of like a CLI (and not like a REST APIs) but compared to CLIs you get: - Nothing to install, just insert MCP URL - No distribution or version hell, updates are available for users and agents whenever you deploy. Especially useful for new or fast moving servers of course - OAuth built in, just hit connect - With MCP apps you can embed UI elements as well, like charts and approval flows that the agent shouldn't see etc
I was wrong I still think it's just an API though and any API can be used as an MCP and in the end they'll just become the same
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So this chart from the Fable 5.1 release basically tells us to use Grok?
Today, we're releasing Fable 5.1 and Mythos 5.1. While the model is getting slightly better at a lot of things, I expect many users to appreciate that Fable 5.1 solves agentic coding tasks at roughly half the cost of Fable 5. I personally also really like it's writing style, which imho sounds a lot more natural then our previous models did. Some other personal highlights below!
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Cursor on Android when? 😢
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Opened preferred coding agent, asked for a few tweaks to keybindings, created a git repo. Now I can easily get any Omarchy env to run exactly the way I want 💪 This is how Linux will win! Congrats @dhh and team on the release and the foundation 👏 github.com/brinkar/omarchy-c…
Revived the old XPS 13 💪
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Revived the old XPS 13 💪
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As an old Linux only user on an almost two-decades Mac detour, Omarchy looks like a good way back 👌
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How is it that NVDA is down so much when clearly everyone is rushing to buy more GPUs to run Deepseek? 😅
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Also known as Jevons Paradox, apparently!
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Nvidia $NVDA just released a statement regarding DeepSeek: "DeepSeek is an excellent AI advancement and a perfect example of Test Time Scaling. DeepSeek’s work illustrates how new models can be created using that technique, leveraging widely-available models and compute that is fully export control compliant. Inference requires significant numbers of NVIDIA GPUs and high-performance networking. We now have three scaling laws: pre-training and post-training, which continue, and new test-time scaling."
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Henrik Brink retweeted
Hey, ChatGPT-4: stage 1. Decide on 16 random things: concepts, things, people, works of art, etc. make them as different as possible. Make them uncommon things stage 2: set up a sweet 16 bracket between these things. stage 3. Decide who wins each matchup in an entertaining way.
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And multi-modal! I guess "I'm not a robot" CAPTCHAs will have to do something very different now? #gpt4
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If not, it can always just hire and convince a person to do it 😂 xitter.cf/YosarianTwo/status/163…
Holy shit. GPT-4, on it's own; was able to hire a human TaskRabbit worker to solve a CAPACHA for it and convinced the human to go along with it.
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32k context window 🤩
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Crossing my fingers for a successful deployment of #JWST at L2🤞 looking good so far! I remember studying this project a decade ago 🤯 Can't wait for the amazing science that this telescope will enable 🔭
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Carl Sagan testifies before Congress on climate change. In 1985. And here I thought I couldn't be a bigger fan. Too bad they didn't listen, though... m.youtube.com/watch?v=Wp-WiN…
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Great news - now they just need to do the same at home 👍
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