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The possibilities for AI-powered protein design are endless! In this case study we show how we used BindCraft to engineer a programmable maltose biosensor from scratch, including wet lab validation at Adaptyv of course. We envision a future where every biologist can make their own bespoke protein tools to... show more

Adaptyv Bio

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Can Claude Code design proteins? We're just about to release our wet lab API + SDK so we made a quick demo to test how agents can interact with protein design models and submit the proteins to our lab for testing. This closes the loop between the computational design and the experiment validation We queued up a bunch of agent-designed proteins for wet lab validation, will release the results in a couple weeks on Proteinbase

Can Claude Code design proteins? We're just about to release our wet lab API + SDK so we made a quick demo to test how agents can interact with protein design models and submit the proteins to our lab for testing. This closes the loop between the computational design and the experiment validation We queued up a bunch of agent-designed proteins for wet lab validation, will release the results in a couple weeks on Proteinbase

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“Proteins sticking to other proteins is what makes life exist.” 🧬 We explain the science behind AlphaProteo: our AI system for designing protein binders that holds huge potential for biology. ▶️ Hear from Jue Wang, co-lead of the protein design team, David La, research scientist, and Harshnira Patani, wet lab lead here at Google DeepMind. ↓

“Proteins sticking to other proteins is what makes life exist.” 🧬 We explain the science behind AlphaProteo: our AI system for designing protein binders that holds huge potential for biology. ▶️ Hear from Jue Wang, co-lead of the protein design team, David La, research scientist, and Harshnira Patani, wet lab lead here at Google DeepMind. ↓

Google DeepMind

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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

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It's like ChatGPT, but for proteins 😍 See RFdiffusion in action. Instead of writing essays, this AI tool builds new protein structures from scratch, or based on your prompts (like a desired shape or motif). It's powered by a deep learning technique called diffusion models, which are also behind tools like DALL·E and Stable Diffusion. I just tried it in Google Colab — and it’s mind-blowing to watch a protein form step-by-step. If you're into protein design, synthetic biology — this is worth exploring. 🧪 Try it here:

It's like ChatGPT, but for proteins 😍 See RFdiffusion in action. Instead of writing essays, this AI tool builds new protein structures from scratch, or based on your prompts (like a desired shape or motif). It's powered by a deep learning technique called diffusion models, which are also behind tools like DALL·E and Stable Diffusion. I just tried it in Google Colab — and it’s mind-blowing to watch a protein form step-by-step. If you're into protein design, synthetic biology — this is worth exploring. 🧪 Try it here:

Rafeeque

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What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

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How do we test how the human body reacts to pressure changes in space? 👀 We engineer low pressures on Earth! At JSC, volunteers can live & work in a specialized low-pressure chamber while scientists track their health. This helps NASA design protocols for future missions.

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Jensen Huang says, In the future, we will be able to talk to a protein We'll ask it: "what are you? are you soluble? how do you react to heat or chemicals?" This is how AI can talk to an image today, and how we've moved from only processing data to actually understanding it

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Haider.

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Today, we expand zero-shot drug design beyond binding to the design of multifunctional medicines, the intracellular proteome, and state-of-the-art atomic precision with our model, JAM-2. In a new report (below), we show: 1. The first drug-grade, fully computationally designed multispecific antibodies against five peptide-MHCs: Routine picomolar T-cell activation/cell-killing EC50s, >100-fold selectivity, and drug-like developability 2. The first fully generatively designed, drug-grade dual-variant KRAS G12 multispecifics: They recruit primary T-cells from human donors to kill G12V and G12C presenting cells at pM to single-digit-nM potency, completely sparing wild-type. 3. Atomic accuracy, from sequence alone: Angstrom-level agreement between Cryo-EM and JAM-2 de novo designs, requiring only target sequences (not structure) as input. 4. Unrivaled speed with an AI-native in-house wet lab: Designed, built, and tested five programs in one parallelized campaign, end-to-end in-house in ~6 weeks. 5. A higher validation bar for AI-generated drug candidates: In a field increasingly rife with hype and uneven standards of proof, we provide the highest quality public wet-lab validation of AI-designed antibodies to date. We share experimental methods in full, and invite folks to adopt and build on these standards. Truly individualized therapies will be the most important contribution of AI in drug design. These advances help accelerate this future.

Today, we expand zero-shot drug design beyond binding to the design of multifunctional medicines, the intracellular proteome, and state-of-the-art atomic precision with our model, JAM-2. In a new report (below), we show: 1. The first drug-grade, fully computationally designed multispecific antibodies against five peptide-MHCs: Routine picomolar T-cell activation/cell-killing EC50s, >100-fold selectivity, and drug-like developability 2. The first fully generatively designed, drug-grade dual-variant KRAS G12 multispecifics: They recruit primary T-cells from human donors to kill G12V and G12C presenting cells at pM to single-digit-nM potency, completely sparing wild-type. 3. Atomic accuracy, from sequence alone: Angstrom-level agreement between Cryo-EM and JAM-2 de novo designs, requiring only target sequences (not structure) as input. 4. Unrivaled speed with an AI-native in-house wet lab: Designed, built, and tested five programs in one parallelized campaign, end-to-end in-house in ~6 weeks. 5. A higher validation bar for AI-generated drug candidates: In a field increasingly rife with hype and uneven standards of proof, we provide the highest quality public wet-lab validation of AI-designed antibodies to date. We share experimental methods in full, and invite folks to adopt and build on these standards. Truly individualized therapies will be the most important contribution of AI in drug design. These advances help accelerate this future.

Nabla Bio

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We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.

We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.

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Automating the lab bench is the best thing we can do for AI in biology. Most experiments are still run by hand. Every biologist's handiwork is unique and every lab is a little different. So, biology faces widespread reproducibility issues. But, AI will demand more reproducible data than we can produce, and generate more ideas than we can test. Experiments must be communicated and executed in a standardized way to generate reproducible data. So, Tetsuwan is building a lab where users specify experiments in an exact syntax. These experiments are executed by an automated platform to generate transparent, reproducible output. The user never needs physical access to a lab. This is a biology lab you can use like a computer. Our platform, built and tested with pilot labs over the past two years, lets users configure automated workflows rapidly & precisely. Later this year, we will bring our first services online, focusing on functional screens for protein design. Alex and I met at Caltech, where she was one year my senior. We're building this company because we were little kids who wanted to be biologists that grew up into adults who resented the lab bench. We want biology to be about asking questions, not the painful and frustrating manual process of asking them. For those looking to receive updates on our pilot services, looking for a meaningful job, or just to learn more about our work, see the links in the comments!

Automating the lab bench is the best thing we can do for AI in biology. Most experiments are still run by hand. Every biologist's handiwork is unique and every lab is a little different. So, biology faces widespread reproducibility issues. But, AI will demand more reproducible data than we can produce, and generate more ideas than we can test. Experiments must be communicated and executed in a standardized way to generate reproducible data. So, Tetsuwan is building a lab where users specify experiments in an exact syntax. These experiments are executed by an automated platform to generate transparent, reproducible output. The user never needs physical access to a lab. This is a biology lab you can use like a computer. Our platform, built and tested with pilot labs over the past two years, lets users configure automated workflows rapidly & precisely. Later this year, we will bring our first services online, focusing on functional screens for protein design. Alex and I met at Caltech, where she was one year my senior. We're building this company because we were little kids who wanted to be biologists that grew up into adults who resented the lab bench. We want biology to be about asking questions, not the painful and frustrating manual process of asking them. For those looking to receive updates on our pilot services, looking for a meaningful job, or just to learn more about our work, see the links in the comments!

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High-quality experimental data fuels biological discovery. Could an AI-enabled "autonomous lab" fundamentally increase the rate at which we generate it, and accelerate our progress toward cures for disease? That's the question I sat down to explore with Jason Kelly, CEO of Ginkgo Bioworks, for a deep-dive and tour of their 52-RAC lab in Boston's Seaport District. In our conversation we covered: → Ginkgo's recent collaboration with OpenAI on cell-free protein synthesis → U.S. Department of Energy's POTUS-backed Genesis Mission to accelerate AI for science → The evolution from cloud labs to truly autonomous labs → An on-the-floor look at Ginkgo's lab, including early challenges and creative workarounds If you're curious about frontier AI models meet the lab bench, check out the latest episode of the Discovery Engines Podcast. Watch or listen: 👀 YouTube: 🎧 Spotify: 🎧 Apple:

High-quality experimental data fuels biological discovery. Could an AI-enabled "autonomous lab" fundamentally increase the rate at which we generate it, and accelerate our progress toward cures for disease? That's the question I sat down to explore with Jason Kelly, CEO of Ginkgo Bioworks, for a deep-dive and tour of their 52-RAC lab in Boston's Seaport District. In our conversation we covered: → Ginkgo's recent collaboration with OpenAI on cell-free protein synthesis → U.S. Department of Energy's POTUS-backed Genesis Mission to accelerate AI for science → The evolution from cloud labs to truly autonomous labs → An on-the-floor look at Ginkgo's lab, including early challenges and creative workarounds If you're curious about frontier AI models meet the lab bench, check out the latest episode of the Discovery Engines Podcast. Watch or listen: 👀 YouTube: 🎧 Spotify: 🎧 Apple:

Nabil Laoudji

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Sony is committed to advancing inclusive accessibility initiatives. We collaborate with a variety of users, including users with disabilities, during product development to ensure inclusive design. "Through the power of innovation, we create a future where everyone can share their excitement." Sony will continue to strive towards this goal by enhancing accessibility in our products.

Sony is committed to advancing inclusive accessibility initiatives. We collaborate with a variety of users, including users with disabilities, during product development to ensure inclusive design. "Through the power of innovation, we create a future where everyone can share their excitement." Sony will continue to strive towards this goal by enhancing accessibility in our products.

Sony

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Our first AI in Design 2026 case study is live, featuring Sierra! Sierra’s design team is only handful of people (though growing quickly) supporting an engineering org of 100+! They’ve had to be extremely thoughtful about how they work with an eng org of that size... At first with AI, it was easy to fix every design nit themselves. Now they focus on creating the systems and components for the whole org while having a clear POV on where to sweat the details by hand (e.g. spending two hours getting a 16-pixel icon right... watch to see why) “If we can touch production code and the systems more easily with stuff like Claude or Cursor, what is a designer’s role in trying to empower at scale? Instead of doing one little pixel at a time, we should build the infrastructure that engineers use to create new features.” We talked to Wayne Fan and Nick about: - The advantages of a small design team - Compressing "cook, prototype, be wrong, go back" cycle into a few hours - The "51st engineer" trap: why fixing every design nit yourself doesn't scale - Bringing back the joy of designing by doing some things by hand

Our first AI in Design 2026 case study is live, featuring Sierra! Sierra’s design team is only handful of people (though growing quickly) supporting an engineering org of 100+! They’ve had to be extremely thoughtful about how they work with an eng org of that size... At first with AI, it was easy to fix every design nit themselves. Now they focus on creating the systems and components for the whole org while having a clear POV on where to sweat the details by hand (e.g. spending two hours getting a 16-pixel icon right... watch to see why) “If we can touch production code and the systems more easily with stuff like Claude or Cursor, what is a designer’s role in trying to empower at scale? Instead of doing one little pixel at a time, we should build the infrastructure that engineers use to create new features.” We talked to Wayne Fan and Nick about: - The advantages of a small design team - Compressing "cook, prototype, be wrong, go back" cycle into a few hours - The "51st engineer" trap: why fixing every design nit yourself doesn't scale - Bringing back the joy of designing by doing some things by hand

Ben Blumenrose

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IN OUR HANDS: Palantir CTO Shyam Sankar says that despite the narratives, the future of AI is still being decided "every single day by the actions that we take." "Are we going to use it to build trinkets of marginal value — or are we going to use it to actually reindustrialize the country, to drive American prosperity, to create jobs, to have a normative view of the productivity that we can get from this to make our country great?"

IN OUR HANDS: Palantir CTO Shyam Sankar says that despite the narratives, the future of AI is still being decided "every single day by the actions that we take." "Are we going to use it to build trinkets of marginal value — or are we going to use it to actually reindustrialize the country, to drive American prosperity, to create jobs, to have a normative view of the productivity that we can get from this to make our country great?"

Fox News

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This is the future of web design. Gamma 3.0 has just been released, and I used it to create a complete website from a URL in seconds. No prompts. No code. No input. Their new AI agent will design, review, fix, and iterate on your content. The best part: you can watch it in real-time as it builds your website! This is pretty amazing! There are many AI design agents out there, but this one is one of the most hands-off tools I've seen. This is incredible.

This is the future of web design. Gamma 3.0 has just been released, and I used it to create a complete website from a URL in seconds. No prompts. No code. No input. Their new AI agent will design, review, fix, and iterate on your content. The best part: you can watch it in real-time as it builds your website! This is pretty amazing! There are many AI design agents out there, but this one is one of the most hands-off tools I've seen. This is incredible.

Santiago

39,140 görüntüleme • 11 ay önce

AI instead of UI The last few years radically changed how we think about digital products & product design process in general. What we are witnessing right now is a transition where AI is no longer just a feature; it is becoming the infrastructure of interaction. For decades, UI design was rooted in the concept of “Happy Path,” a series of static, linear screens (routes from A to B) designed to funnel users toward a goal. This “one-size-fits-all” approach assumes that all users have the same mental model, which is far from the truth. The rise of AI tools like ChatGPT and Gemini is showing that we are moving into an era of Generative Interfaces. Instead of a designer pre-determining every button and menu, the interface is synthesized in real-time based on user actions. Back in 2019, Gleb Kuznetsov and I were working on the concept of morphing Interface, the idea of a UI that adapts its structure based on user intent. The great thing about this UI is that it was highly dynamic: different users saw different content/contextual actions based on their behavior. This concept was crazy in 2019, but it is highly relevant to the modern state of the product design process. And I strongly believe that it represents the future of UI design. We will move beyond the “AI chatbox" crutch Yes, many products today treat AI as a sidecar (think of Copilots or chatbots pinned to the side of a traditional dashboard), but this is a transitional phase. Why? Because adding a chat window to a 20-year-old software layout is like putting a jet engine on a horse-drawn carriage. That's why the future isn't "AI as an add-on"; it is AI as the Operating System. AI will power generative interfaces that are rooted in anticipatory design: UI won’t wait for a command; it will surface tools based on the user's current environmental context and historical behavior. This evolution changes the very nature of product design, and we will move away from designing pages/screens and toward designing systems of logic.

AI instead of UI The last few years radically changed how we think about digital products & product design process in general. What we are witnessing right now is a transition where AI is no longer just a feature; it is becoming the infrastructure of interaction. For decades, UI design was rooted in the concept of “Happy Path,” a series of static, linear screens (routes from A to B) designed to funnel users toward a goal. This “one-size-fits-all” approach assumes that all users have the same mental model, which is far from the truth. The rise of AI tools like ChatGPT and Gemini is showing that we are moving into an era of Generative Interfaces. Instead of a designer pre-determining every button and menu, the interface is synthesized in real-time based on user actions. Back in 2019, Gleb Kuznetsov and I were working on the concept of morphing Interface, the idea of a UI that adapts its structure based on user intent. The great thing about this UI is that it was highly dynamic: different users saw different content/contextual actions based on their behavior. This concept was crazy in 2019, but it is highly relevant to the modern state of the product design process. And I strongly believe that it represents the future of UI design. We will move beyond the “AI chatbox" crutch Yes, many products today treat AI as a sidecar (think of Copilots or chatbots pinned to the side of a traditional dashboard), but this is a transitional phase. Why? Because adding a chat window to a 20-year-old software layout is like putting a jet engine on a horse-drawn carriage. That's why the future isn't "AI as an add-on"; it is AI as the Operating System. AI will power generative interfaces that are rooted in anticipatory design: UI won’t wait for a command; it will surface tools based on the user's current environmental context and historical behavior. This evolution changes the very nature of product design, and we will move away from designing pages/screens and toward designing systems of logic.

Nick Babich

53,581 görüntüleme • 6 ay önce

Yesterday, we announced that our autonomous lab, connected with OpenAI's GPT-5, beat the state-of-the-art in Cell-Free Protein Synthesis by 40%. This is how we did it. Watch OpenAI's Joy Jiao and Edmund Wong alongside our own Reshma Shetty as they explain how techniques like the use of a pydantic model set the stage for this breakthrough. The preprint is now out on bioRxiv: And if you’re in Boston for SLAS, come see Joy talk about it ( ) or come Monday-Wednesday from 9AM-5PM to 25 Drydock Ave to see the lab where we ran the experiments! You can just stop by or book ahead via And swing by Booth #1823 beforehand to pick up an Uber voucher for the 5-min ride!

Yesterday, we announced that our autonomous lab, connected with OpenAI's GPT-5, beat the state-of-the-art in Cell-Free Protein Synthesis by 40%. This is how we did it. Watch OpenAI's Joy Jiao and Edmund Wong alongside our own Reshma Shetty as they explain how techniques like the use of a pydantic model set the stage for this breakthrough. The preprint is now out on bioRxiv: And if you’re in Boston for SLAS, come see Joy talk about it ( ) or come Monday-Wednesday from 9AM-5PM to 25 Drydock Ave to see the lab where we ran the experiments! You can just stop by or book ahead via And swing by Booth #1823 beforehand to pick up an Uber voucher for the 5-min ride!

Ginkgo Bioworks

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Reality capture and generative AI make a powerful combination that is yet to be fully explored. Once you can perceive the world, genAI gives you the ability to reskin & augment it -- whether it's for VFX or interior design. Explore the possibilities in this compilation of my recent AI experiments. What would you create with this tech?

Reality capture and generative AI make a powerful combination that is yet to be fully explored. Once you can perceive the world, genAI gives you the ability to reskin & augment it -- whether it's for VFX or interior design. Explore the possibilities in this compilation of my recent AI experiments. What would you create with this tech?

Bilawal Sidhu

186,827 görüntüleme • 3 yıl önce