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Our bodies’ molecules aren’t static so why do many AI models treat them that way? Without considering the physical movement of molecules, many AI tools fail to robustly capture what makes drug candidates promising. AquaGen is a generative AI framework built to close that gap: an all-atom, explicit-solvent model... show more

Valence Labs

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10,296 просмотров • 1 месяц назад •via X (Twitter)

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In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

Sajith Wickramasekara

30,646 просмотров • 3 месяцев назад

AI-Native Service Companies Gustaf Alströmer The total spend on services is many times larger than the spend on software, and a lot of those services are already outsourced, which makes them easier to replace with an AI-native product. We're excited about companies that don't sell a tool to help you do the work: they just do the work.

AI-Native Service Companies Gustaf Alströmer The total spend on services is many times larger than the spend on software, and a lot of those services are already outsourced, which makes them easier to replace with an AI-native product. We're excited about companies that don't sell a tool to help you do the work: they just do the work.

Y Combinator

337,783 просмотров • 3 месяцев назад

🚨David Sinclair: “AI helped us do 160 years of biology work in two months.” ROGAN: “What?!” SINCLAIR: “We’re looking for molecules, a single molecule that can replace this cocktail... You need a molecule to do three or four activities in the cell. Finding one activity is what pharmaceutical companies do. It takes hundreds of millions, if not billions, to make a drug.” “My little lab of 20 people and students are trying to make one drug do three or four different things. That’s beyond the reach of pharmaceutical experience, at least five years ago.” “This funnel is full of a trillion molecules, and we have to come down to one. We don’t look for a trillion molecules at the bench. That would take 160 or thousands of years... so we do it inside the computer.” “We just use a lot of compute, and we dock each of these molecules against the enzymes that we now know how they work, thanks to Google DeepMind solving the problem of how all our proteins look, 20,000 of them.” 1 trillion candidate molecules. Filtered to 200. Two months. “Next week, we’re testing these 200 against human cells, and we’ve also got AI helping us tell if the human cells go from 92 years old back to 20. We can tell that just by looking at them, thanks to AI.” Dr. David Sinclair is a Harvard geneticist and one of the world's leading longevity researchers. Fascinating to hear how he's using AI to compress 160 years of biology work into two months. What other decades of work is about to become two months? Joe Rogan David Sinclair

🚨David Sinclair: “AI helped us do 160 years of biology work in two months.” ROGAN: “What?!” SINCLAIR: “We’re looking for molecules, a single molecule that can replace this cocktail... You need a molecule to do three or four activities in the cell. Finding one activity is what pharmaceutical companies do. It takes hundreds of millions, if not billions, to make a drug.” “My little lab of 20 people and students are trying to make one drug do three or four different things. That’s beyond the reach of pharmaceutical experience, at least five years ago.” “This funnel is full of a trillion molecules, and we have to come down to one. We don’t look for a trillion molecules at the bench. That would take 160 or thousands of years... so we do it inside the computer.” “We just use a lot of compute, and we dock each of these molecules against the enzymes that we now know how they work, thanks to Google DeepMind solving the problem of how all our proteins look, 20,000 of them.” 1 trillion candidate molecules. Filtered to 200. Two months. “Next week, we’re testing these 200 against human cells, and we’ve also got AI helping us tell if the human cells go from 92 years old back to 20. We can tell that just by looking at them, thanks to AI.” Dr. David Sinclair is a Harvard geneticist and one of the world's leading longevity researchers. Fascinating to hear how he's using AI to compress 160 years of biology work into two months. What other decades of work is about to become two months? Joe Rogan David Sinclair

KanekoaTheGreat

472,545 просмотров • 2 дней назад

This Studio Ghibli AI trend is an utter insult to the studio and anime/cinema in general. the real issue is that the AI is clearly trained on copyrighted material without permission in order to recreate like that. this is what the discussion should be about. It's especially disgusting considering Miyazaki is an artist whose profoundly against AI. Kind of reveals that people care far more about aesthetics than the artist...

This Studio Ghibli AI trend is an utter insult to the studio and anime/cinema in general. the real issue is that the AI is clearly trained on copyrighted material without permission in order to recreate like that. this is what the discussion should be about. It's especially disgusting considering Miyazaki is an artist whose profoundly against AI. Kind of reveals that people care far more about aesthetics than the artist...

3DxDEV

315,009 просмотров • 3 месяцев назад

This Studio Ghibli AI trend is an utter insult to the studio and anime/cinema in general. the real issue is that the AI is clearly trained on copyrighted material without permission in order to recreate like that. this is what the discussion should be about. It's especially disgusting considering Miyazaki is an artist whose profoundly against AI. Kind of reveals that people care far more about aesthetics than the artist...

This Studio Ghibli AI trend is an utter insult to the studio and anime/cinema in general. the real issue is that the AI is clearly trained on copyrighted material without permission in order to recreate like that. this is what the discussion should be about. It's especially disgusting considering Miyazaki is an artist whose profoundly against AI. Kind of reveals that people care far more about aesthetics than the artist...

3DxDEV

238,864 просмотров • 24 дней назад

Today we're excited to introduce Vy, our AI that sees and acts on your computer. At Vercept, our mission is to reinvent how humans use computers–enabling you to accomplish orders of magnitude more than what you can do today. Vy is a first glimpse at AI that sees and uses your computer just like you do. It runs natively on your computer, with access to all your applications and accounts that you’re already signed into. It is available for download starting today! As part of the release, we’ve built VyUI, an AI model bridging the gap between language and your screen. It sets a new state-of-the-art on UI grounding benchmarks like ScreenSpot v1, ScreenSpot v2, and GroundUI Web–beating leading models from OpenAI, Google, and Anthropic. Check out what Vy can do in the thread below: 🧵👇

Today we're excited to introduce Vy, our AI that sees and acts on your computer. At Vercept, our mission is to reinvent how humans use computers–enabling you to accomplish orders of magnitude more than what you can do today. Vy is a first glimpse at AI that sees and uses your computer just like you do. It runs natively on your computer, with access to all your applications and accounts that you’re already signed into. It is available for download starting today! As part of the release, we’ve built VyUI, an AI model bridging the gap between language and your screen. It sets a new state-of-the-art on UI grounding benchmarks like ScreenSpot v1, ScreenSpot v2, and GroundUI Web–beating leading models from OpenAI, Google, and Anthropic. Check out what Vy can do in the thread below: 🧵👇

Vercept

123,748 просмотров • 1 год назад

Chai Discovery cofounder Jack Dent says AI drug discovery hit a major inflection point in 2025. As a result, it's now being deployed at the biggest pharma companies as part of their core discovery engines. "Over the course of 2025, these models went from being research curiosities... to being able to design molecules without needing to fine-tune them on a lot of data." "You can really just prompt them in the way that you'd prompt an LLM with a target, maybe some disease you're going after. And they will design a number of candidates that would help do that, essentially." "A year, year and a half ago, basically none of this stuff worked." "In 2025 we really crossed the Rubicon. And now these technologies are actively being deployed in companies like Eli Lilly, Novartis, Pfizer. These are some of the most scientific, highest-tech companies in the world. And they're putting them into real drug programs and making them part of their core discovery engines."

Chai Discovery cofounder Jack Dent says AI drug discovery hit a major inflection point in 2025. As a result, it's now being deployed at the biggest pharma companies as part of their core discovery engines. "Over the course of 2025, these models went from being research curiosities... to being able to design molecules without needing to fine-tune them on a lot of data." "You can really just prompt them in the way that you'd prompt an LLM with a target, maybe some disease you're going after. And they will design a number of candidates that would help do that, essentially." "A year, year and a half ago, basically none of this stuff worked." "In 2025 we really crossed the Rubicon. And now these technologies are actively being deployed in companies like Eli Lilly, Novartis, Pfizer. These are some of the most scientific, highest-tech companies in the world. And they're putting them into real drug programs and making them part of their core discovery engines."

TBPN

29,183 просмотров • 26 дней назад

🚀 Excited to release AIBuildAI — an AI agent that automatically builds AI models 🏆 #1 on OpenAI MLE-Bench 💻 GitHub: Building AI models still requires a lot of manual work — designing models, writing code, training them, tuning hyperparameters, and iterating on results. We developed AIBuildAI to automate much of this process. The system runs an agent loop that automatically: • analyzes the task • designs models • writes code to implement them • trains the models • tunes hyperparameters • evaluates model performance • iteratively improves the models 🏆 On OpenAI’s MLE-Bench benchmark, AIBuildAI ranked #1:

🚀 Excited to release AIBuildAI — an AI agent that automatically builds AI models 🏆 #1 on OpenAI MLE-Bench 💻 GitHub: Building AI models still requires a lot of manual work — designing models, writing code, training them, tuning hyperparameters, and iterating on results. We developed AIBuildAI to automate much of this process. The system runs an agent loop that automatically: • analyzes the task • designs models • writes code to implement them • trains the models • tunes hyperparameters • evaluates model performance • iteratively improves the models 🏆 On OpenAI’s MLE-Bench benchmark, AIBuildAI ranked #1:

Pengtao Xie

151,764 просмотров • 4 месяцев назад

🚨 INSANE: Bill Gates predicts that AI-based drug discovery will enable the creation of an Alzheimer's vaccine "over the next five to ten years". "AI's ability to take large amounts of data and find meaning in that data means that this research is going to go a lot faster." "Understanding the biology of Alzheimer's and then seeing, okay, is there a drug? Is there a vaccine?" "Over the next five to ten years, we'll come up with tools so that this looming cost and tragedy can largely be avoided."

🚨 INSANE: Bill Gates predicts that AI-based drug discovery will enable the creation of an Alzheimer's vaccine "over the next five to ten years". "AI's ability to take large amounts of data and find meaning in that data means that this research is going to go a lot faster." "Understanding the biology of Alzheimer's and then seeing, okay, is there a drug? Is there a vaccine?" "Over the next five to ten years, we'll come up with tools so that this looming cost and tragedy can largely be avoided."

Died Suddenly

11,304 просмотров • 8 месяцев назад

One of the why questions we’re always asking is how can we help operations employees access inventory more efficiently? We’re taking a big leap forward by introducing DeepFleet, an exciting new AI model that makes our one million+ robot fleet work smarter. Think of DeepFleet as an intelligent traffic management system for our fulfillment centers. Just like smart traffic systems reduce congestion in cities, DeepFleet coordinates our robots’ movements to find optimal paths and reduce bottlenecks. By leveraging our deep expertise in robotics and combining it with AWS tools like SageMaker, we’ve built an AI model that learns from our extensive data on inventory movement to make real-time decisions that improve robot travel time by 10% (which at our scale is pretty significant). What makes this particularly exciting is how AI and robotics create value across the board. For customers, it means faster delivery times and lower costs. For employees, it opens up new opportunities – our robots handle physically demanding tasks and heavy lifting, making our fulfillment centers safer, while our people take on more technical roles. This combination of robotics and generative AI is just getting started, and am looking forward to what we’ll invent next.

One of the why questions we’re always asking is how can we help operations employees access inventory more efficiently? We’re taking a big leap forward by introducing DeepFleet, an exciting new AI model that makes our one million+ robot fleet work smarter. Think of DeepFleet as an intelligent traffic management system for our fulfillment centers. Just like smart traffic systems reduce congestion in cities, DeepFleet coordinates our robots’ movements to find optimal paths and reduce bottlenecks. By leveraging our deep expertise in robotics and combining it with AWS tools like SageMaker, we’ve built an AI model that learns from our extensive data on inventory movement to make real-time decisions that improve robot travel time by 10% (which at our scale is pretty significant). What makes this particularly exciting is how AI and robotics create value across the board. For customers, it means faster delivery times and lower costs. For employees, it opens up new opportunities – our robots handle physically demanding tasks and heavy lifting, making our fulfillment centers safer, while our people take on more technical roles. This combination of robotics and generative AI is just getting started, and am looking forward to what we’ll invent next.

Andy Jassy

42,327 просмотров • 1 год назад

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk

X Freeze

62,736 просмотров • 6 месяцев назад

LEAKED AUDIO: In an all-hands meeting on April 30, Mark Zuckerberg tells employees that he's training AI on them ahead of mass layoffs. "The AI models learn from watching really smart people do things... The average intelligence of the people who are at this company is significantly higher than the average set of people that you can get to do tasks. So if we're trying to teach the models coding, for example, then having people internally build tools or solve tasks that help teach the model how to code, we think is going to dramatically increase our model's coding ability faster than what others in the industry have the capability to do, who don't have thousands and thousands of extremely strong engineers at their company."

LEAKED AUDIO: In an all-hands meeting on April 30, Mark Zuckerberg tells employees that he's training AI on them ahead of mass layoffs. "The AI models learn from watching really smart people do things... The average intelligence of the people who are at this company is significantly higher than the average set of people that you can get to do tasks. So if we're trying to teach the models coding, for example, then having people internally build tools or solve tasks that help teach the model how to code, we think is going to dramatically increase our model's coding ability faster than what others in the industry have the capability to do, who don't have thousands and thousands of extremely strong engineers at their company."

More Perfect Union

4,562,109 просмотров • 2 месяцев назад

Hello, my dear friends, we have some breaking news today! Our rentosertib (novel target that has never been in clinical trials before we picked it using AI, novel molecule designed using true generative AI with the published broadly used AI platform, targeting fibrosis and biology of aging) entered Phase III trial in Idiopathic Pulmonary Fibrosis. This is a major milestone in the life of every drug hunter - the ultimate test. To my knowledge, in our AI drug discovery industry, no other company took the drug for a novel target with a novel molecule into Phase III before so it is a big milestone for the entire industry. Of course, at Insilico we are no longer dependent on this program - we have 13 INDs and many clinical trials are ongoing internally and run by partners. But this program is very special as we also demonstrated the senomorphic properties of this drug and built the entire pipeline of molecules around this target. Please Wish Me Luck! Luck is the most important superpower in everything we do and if we succeed with this program and also demonstrate the antiaging effect - all of us will benefit. Godspeed, my friends and thanks to everyone who helped us along the way!

Hello, my dear friends, we have some breaking news today! Our rentosertib (novel target that has never been in clinical trials before we picked it using AI, novel molecule designed using true generative AI with the published broadly used AI platform, targeting fibrosis and biology of aging) entered Phase III trial in Idiopathic Pulmonary Fibrosis. This is a major milestone in the life of every drug hunter - the ultimate test. To my knowledge, in our AI drug discovery industry, no other company took the drug for a novel target with a novel molecule into Phase III before so it is a big milestone for the entire industry. Of course, at Insilico we are no longer dependent on this program - we have 13 INDs and many clinical trials are ongoing internally and run by partners. But this program is very special as we also demonstrated the senomorphic properties of this drug and built the entire pipeline of molecules around this target. Please Wish Me Luck! Luck is the most important superpower in everything we do and if we succeed with this program and also demonstrate the antiaging effect - all of us will benefit. Godspeed, my friends and thanks to everyone who helped us along the way!

Alex Zhavoronkov, PhD (aka Aleksandrs Zavoronkovs)

49,498 просмотров • 1 месяц назад

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: *Decode proteins to fight disease *Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: Decode proteins to fight disease Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

Konstantine Buhler

13,014 просмотров • 9 месяцев назад

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Karl Mehta

16,619 просмотров • 1 месяц назад

I had to do this of course - test out the new Google Omni Flash model with my favorite 3d art sandbox - Dreams. Just a quick test for now.. I'll be sharing more examples and techniques soon but wanted to show you this one. There's something very fitting about combining "handmade" 3d models and animations sculpted from scratch in software that runs on 2013 hardware, with some of the most advanced AI technology in the world. As part of my work for Google, I've used Google V2V models on a few different projects now - Ancestra, Dear Upstairs Neighbors and most recently one of the IO films (I'll share more about that project soon) 🥰 - and video continues to be my favorite form of input for Generative Ai workflows. If an image says more than a thousand words, a video says more than a million. And knowing that these capabilities are now in the hands of all of you, makes me very excited to see the new workflows and projects that will come out of it. What do you think of Omni so far?

I had to do this of course - test out the new Google Omni Flash model with my favorite 3d art sandbox - Dreams. Just a quick test for now.. I'll be sharing more examples and techniques soon but wanted to show you this one. There's something very fitting about combining "handmade" 3d models and animations sculpted from scratch in software that runs on 2013 hardware, with some of the most advanced AI technology in the world. As part of my work for Google, I've used Google V2V models on a few different projects now - Ancestra, Dear Upstairs Neighbors and most recently one of the IO films (I'll share more about that project soon) 🥰 - and video continues to be my favorite form of input for Generative Ai workflows. If an image says more than a thousand words, a video says more than a million. And knowing that these capabilities are now in the hands of all of you, makes me very excited to see the new workflows and projects that will come out of it. What do you think of Omni so far?

Martin Nebelong

38,411 просмотров • 2 месяцев назад

NEW EPISODE of HARD DRUGS! AlphaFold, ProteinMPNN and other AI tools are transforming biology and drug design. But how do they work? What can’t they do (yet)? And can we use them to make a vaccine against Strep A for the very first time? In this episode, Jacob and I talk about hacking proteins with AI. 03:21 What makes proteins cool 05:29 Can we make a vaccine against Strep A? 10:12 Proteins in nature aren’t enough, and we can improve them with AI 21:12 ProteinMPNN and AlphaFold are transforming drug design 29:52 A short history of protein structure prediction from physical models to deep learning 34:48 Using AI to design a Strep A vaccine 39:27 Validating an AI-designed Strep A vaccine 43:09 Recent AI protein breakthroughs 49:30 Why we don’t already have a Strep A vaccine and the future of optimizing protein drugs

NEW EPISODE of HARD DRUGS! AlphaFold, ProteinMPNN and other AI tools are transforming biology and drug design. But how do they work? What can’t they do (yet)? And can we use them to make a vaccine against Strep A for the very first time? In this episode, Jacob and I talk about hacking proteins with AI. 03:21 What makes proteins cool 05:29 Can we make a vaccine against Strep A? 10:12 Proteins in nature aren’t enough, and we can improve them with AI 21:12 ProteinMPNN and AlphaFold are transforming drug design 29:52 A short history of protein structure prediction from physical models to deep learning 34:48 Using AI to design a Strep A vaccine 39:27 Validating an AI-designed Strep A vaccine 43:09 Recent AI protein breakthroughs 49:30 Why we don’t already have a Strep A vaccine and the future of optimizing protein drugs

Saloni

72,565 просмотров • 10 месяцев назад

Been experimenting with Apple's new Core AI framework over the past few days Built a mobile app that runs local AI models directly on device, exploring Core AI model deployment (.aimodel), model specialization, caching, and inference across Apple Silicon Also spent some time integrating with Apple's Foundation Models framework and understanding how custom models can coexist with Apple's on-device AI stack Still early, but the combination of Core AI, Foundation Models, and the Apple Neural Engine feels like a pretty compelling foundation for local first AI applications Still in alpha and working towards a more stable version, but it's been a fun way to explore where Apple's on device AI ecosystem is headed !!

Been experimenting with Apple's new Core AI framework over the past few days Built a mobile app that runs local AI models directly on device, exploring Core AI model deployment (.aimodel), model specialization, caching, and inference across Apple Silicon Also spent some time integrating with Apple's Foundation Models framework and understanding how custom models can coexist with Apple's on-device AI stack Still early, but the combination of Core AI, Foundation Models, and the Apple Neural Engine feels like a pretty compelling foundation for local first AI applications Still in alpha and working towards a more stable version, but it's been a fun way to explore where Apple's on device AI ecosystem is headed !!

Sagar

12,828 просмотров • 1 месяц назад