Rish
20.8K posts

Rish
@rishistyping
Media Artist | Founding Principal AI Research @II_posts / New Media | NeuroAI @UCLA | X— Distinguished Research @StabilityAI | Generative Media @refikanadol
NYC — Paris— London Beigetreten Temmuz 2019
7.5K Folgt2.5K Follower
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@hla_michael @vishalmisra Love the direction! Great posts! Would love to collaborate on similar!
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@vishalmisra Nice, would be a great follow up! Would have to think of ways to test each rung
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I trained an LLM from scratch on pre-1900 text to see if it could come up with quantum mechanics and relativity.
While the model is too small to do meaningful reasoning, it has glimpses of intuition.
When given observations from past landmark experiments, the model can declare that “light is made up of definite quantities of energy” and even suggest that gravity and acceleration are locally equivalent.
I’m releasing the dataset + models and leave this as an open problem to the research community.
I also include what this project has taught me about intelligence in a mini essay linked below.
🧵(1/n)
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InkField | 墨域 is about to be released.
From the first version, both humans and bots will be able to create on it together.
In a way, the system reflects the time we live in.
Humans and machines are already living in a kind of symbiosis.
I’m generally an optimistic person.
But I have to admit, 2026 feels a little heavy right now.
Maybe the work will reflect that. Maybe it won’t.
Either way, wishing everyone well.
I still believe the dawn is coming.
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@stash_pomichter Cool to explore Creative AI usecases similar to Dr. Madeline Gannon’s work atonaton.com/quipt
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50 years of @Apple
From the early days of the #iPod to bringing the #iPhone into the world, some of the most formative years of my career were spent there. The products and teams stay with you. But more importantly so does how Apple thinks.
A few lessons that have held true for decades:
1) Start with the user, not the tech. The question isn’t “what can we build?” but “what problem actually matters?”
2) Focus is everything. Apple is defined as much by what it says no to as what it builds.
3) End-to-end matters. Hardware, software, services. It all has to work together.
4) Details are the product. What feels small is what users remember.
5) Debate hard. Commit fully.
6) Build for the long term.
We’re in another moment of massive technological change. The fundamentals haven’t changed.
The companies that win build things people actually use and can’t imagine living without.
Congrats to everyone who has been part of Apple’s first 50 years! 🙌

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Today Apple celebrates its 50th year as a company. In its honor, take a look back at one of the articles featured in our 2009 magazine issue covering Steve Jobs as CEO of the Decade—and what CEOs across industries had to say about him. bit.ly/41BQnga




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"Great teamwork is like a rock tumbler" — Steve Jobs
Ivan Zhao@ivanhzhao
The loudest story about AI is a lonely one. One person with an army of chatbots. Other humans are friction. That gets the future wrong. The best things aren’t built alone. In a moment of change, we want to remind the world (and ourselves) what Notion stands for: — Think Together
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Hindsight is a luxury, but the biggest innovations are rarely obvious at the start.
I sat down with former @ASMLcompany President Martin van den Brink to discuss how they bet the company on EUV technology long before the AI boom made it essential.
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Last year, Nothing OS 3.0 introduced the ability to swap fonts system-wide, adding another layer of customisation to our well-loved mobile software.
Today we’re expanding our font offering; Papyrus introduces a new dimension of legibility and style, marking a significant milestone in our software design journey.
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Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do?
That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100.
This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration.
Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.
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My team has been cooking! Turns out you can make LLMs creating great game levels… but only if you give them a whole bunch of existing metrics, generators, and parts of generators as tools. There’s lots more to do here. Agentic PCG for the win!
Zehua Jiang@jzh_000
New paradigm alert! 🎮 AgenticPCG We combine classic PCG (Procedural Content Generation) algorithms with large language models for generating game levels. LLMs on their own are not good at level generation, but when given the right tools from our PCG toolbox they're killing it!
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