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We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d...

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Liam Fedus

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I announced a Critical Raw Materials Act in my 2022 State of the Union address. And this year, we made it happen. It is crucial to our security of supply. And it helps us and our reliable partners to strengthen our industries and create good jobs. Join me on 13/09 for #SOTEU
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In our constant commitment to bring value to holders, we’re excited to announce the addition of image generation to our dApp on April 3rd Next week, as part of our journey towards decentralization, we are starting to integrate our open-source-based AI model into the Solana blockchain. This marks the beginning of our transition from relying on third-party APIs to utilizing fine-tuned open source AI Models integrated with Solana, enhancing our dApp’s security and autonomy.
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(1/n) 🚀 With FastVideo, you can now generate a 5-second video in 5 seconds on a single H200 GPU! Introducing FastWan series, a family of fast video generation models trained via a new recipe we term as “sparse distillation”, to speed up video denoising time by 70X! 🖥️ Live demo: (Thanks to @gmicloud for the support!) 🔗 Blog: 🔓 We fully open-source our models, code, and data with Apache-2.0 licenses

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Axis Robotics

28,096 Aufrufe • vor 8 Monaten

To replace animal testing with AI, we need MASSIVE human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!
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To replace animal testing with AI, we need MASSIVE human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!

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Stormwind was the first city we ever built. Our lead programmer had it load and play the song "We built this city" to our surprise when we first loaded in. The first version of Stormwind had no districts or canals. The framerate was below terrible. Then I thought of Venice. I grabbed a map of the city and slapped it down on the table the next morning. THIS, I said, is how we'll partition the city, drawing only the district you can see, separated by canals. It worked, and Stormwind was born.

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We get portrayed as vigilantes or racists or far right nazis we have heard It all but nothing will stop us from trying to make sure our woman and children are safe on our streets we will try and make sure everyone is aware of the real dangers our city is in 💯🙏🏻🏴󠁧󠁢󠁳󠁣󠁴󠁿
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We get portrayed as vigilantes or racists or far right nazis we have heard It all but nothing will stop us from trying to make sure our woman and children are safe on our streets we will try and make sure everyone is aware of the real dangers our city is in 💯🙏🏻🏴󠁧󠁢󠁳󠁣󠁴󠁿

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Over the last few months, our engineering teams have been heads down building a high throughput, low-latency blockchain coupled with an exchange replicable across different data centers. We’ve invested thousands of developer hours designing our data infrastructure stack from scratch. We designed our own streaming indexer to serve data to our frontend in real-time, and shipped optimized memory crates to push our general shared IPC to under 100 nanoseconds. This architecture enables us to colocate our sequencers with the key sources of price discovery across all asset classes: Tokyo for crypto, New Jersey for equities, Chicago for commodities. In turn, institutional market makers colocated with GTE bare metal racks will be able to post orders with minimal roundtrip latency, resulting in the tightest spreads and pristine liquidity. We call this GTE Turbo. By optimizing the entire stack from hardware, to the software, into the networking stack, we will build a world where anyone, anywhere, can trade anything, at anytime. lfGTE
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Over the last few months, our engineering teams have been heads down building a high throughput, low-latency blockchain coupled with an exchange replicable across different data centers. We’ve invested thousands of developer hours designing our data infrastructure stack from scratch. We designed our own streaming indexer to serve data to our frontend in real-time, and shipped optimized memory crates to push our general shared IPC to under 100 nanoseconds. This architecture enables us to colocate our sequencers with the key sources of price discovery across all asset classes: Tokyo for crypto, New Jersey for equities, Chicago for commodities. In turn, institutional market makers colocated with GTE bare metal racks will be able to post orders with minimal roundtrip latency, resulting in the tightest spreads and pristine liquidity. We call this GTE Turbo. By optimizing the entire stack from hardware, to the software, into the networking stack, we will build a world where anyone, anywhere, can trade anything, at anytime. lfGTE

Matteo

141,212 Aufrufe • vor 9 Monaten

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We have built a re-entry capsule. And we’re doing a mid-air drop test to demonstrate our dynamics and landing capabilities, a key test before we launch it into space within next 12 months. RSVP to watch us live from Mission Control. We’re creating the OpenAI moment for space, where anyone can develop solutions in space faster and cheaper.

Rifath Shaarook

42,012 Aufrufe • vor 1 Jahr

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We are releasing AutoResearchExam, a benchmark on open-ended machine learning and engineering tasks. Our benchmark covers seven research areas including model training, data curation, AI safety and interpretability. In each task, we give agents 24 hours with a CPU or GPU machine to develop and improve their solutions through experiments and feedback. We measure both speed and quality with a combined score. Our benchmark has a unique feature: testing if agents create improvements that hold up on data they never see. We find that AI research agents often overfit as they try to improve. We see an interesting head-to-head comparison at the frontier: Astra starts the strongest and holds the lead for up to 19 hours but Fable 5.1 catches up and gets the top performing spot in the final hours. Qwen3.8 Max, Gemini 3.8 Flash and Grok 4.6 all sit on the cost-performance Pareto frontier, giving strong options at lower API budgets. Anthropic's Opus and Fable retain nearly all their validation performance on hidden tests, with gaps of 1.1% and 2.9%. Astra's improvement over Sol extends to generalization too, with that gap falling from 6.9% to 1.7%. (1/n)

Alex Dimakis

1,952,608 Aufrufe • vor 6 Tagen

Sneak peek at some stuff 🫣 This is our local development environment where we can compare what the models generate by default to what they generate with our opinions layered in. So much more to do, but already obvious improvements from just focusing on little details like hanging punctuation, card content alignment, lighting/elevation cues, and edge highlighting of avatars.
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Sneak peek at some stuff 🫣 This is our local development environment where we can compare what the models generate by default to what they generate with our opinions layered in. So much more to do, but already obvious improvements from just focusing on little details like hanging punctuation, card content alignment, lighting/elevation cues, and edge highlighting of avatars.

Adam Wathan

57,471 Aufrufe • vor 7 Monaten

Help us make Pathfinder even better! The more we talk to our community, the more it validates our belief. Everyone’s favorite shapes are unique, even when they look similar, size makes all the difference! But we want to understand more and to further the science of shape. Our Discord data shows that Xtra Claw, Right Hand, and Wave shapes are among the most popular. Now we want to reach out to mroe people and hear from you! Like ❤️ / Retweet 🔁 this post and complete the survey📋. In one week, we’ll pick a winner 🎁 to be the very first to receive our secret gift box! 🎉
0:13

Help us make Pathfinder even better! The more we talk to our community, the more it validates our belief. Everyone’s favorite shapes are unique, even when they look similar, size makes all the difference! But we want to understand more and to further the science of shape. Our Discord data shows that Xtra Claw, Right Hand, and Wave shapes are among the most popular. Now we want to reach out to mroe people and hear from you! Like ❤️ / Retweet 🔁 this post and complete the survey📋. In one week, we’ll pick a winner 🎁 to be the very first to receive our secret gift box! 🎉

Orbital

11,658 Aufrufe • vor 11 Monaten