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Learn more about Boltz-2, the new open source AI model from MIT and Recursion capable of predicting protein binding affinity with unprecedented speed, scale and accuracy. ▪️ Boltz-2 is the first model to combine structure and binding affinity prediction, approaching the accuracy of physics-based free energy perturbation (FEP) calculations...

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Recursion

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15,593 views • 1 year ago •via X (Twitter)

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Recursion1 year ago

Learn more and explore Boltz-2 here:

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UserInterface2 years ago

Unveiling the Future of Prompt Engineering for Better AI Interactions #tech

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121,948 views • 4 months ago

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3:54

MIT announces the Initiative for New Manufacturing | Peter Dizikes, MIT News The Institute-wide effort aims to bolster industry and create jobs by driving innovation across vital manufacturing sectors. MIT today launched its Initiative for New Manufacturing (INM), an Institute-wide effort to reinfuse U.S. industrial production with leading-edge technologies, bolster crucial U.S. economic sectors, and ignite job creation. The initiative will encompass advanced research, innovative education programs, and partnership with companies across many sectors, in a bid to help transform manufacturing and elevate its impact. “We want to work with firms big and small, in cities, small towns and everywhere in between, to help them adopt new approaches for increased productivity,” MIT President Sally A. Kornbluth wrote in a letter to the Institute community this morning. “We want to deliberately design high-quality, human-centered manufacturing jobs that bring new life to communities across the country.” Kornbluth added: “Helping America build a future of new manufacturing is a perfect job for MIT — and I’m convinced that there is no more important work we can do to meet the moment and serve the nation now.” The Initiative for New Manufacturing also announced its first six founding industry consortium members: Amgen, Flex, GE Vernova, PTC, Sanofi, and Siemens. Participants in the INM Industry Consortium will support seed projects proposed by MIT researchers, initially in the area of artificial intelligence for manufacturing. INM joins the ranks of MIT’s other presidential initiatives — including The Climate Project at MIT; MITHIC, which supports the human-centered disciplines; MIT HEALS, centered on the life sciences and health; and MGAIC, the MIT Generative AI Impact Consortium. “There is tremendous opportunity to bring together a vibrant community working across every scale — from nanotechnology to large-scale manufacturing — and across a wide-range of applications including semiconductors, medical devices, automotive, energy systems, and biotechnology,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer and dean of engineering, who is part of the initiative’s leadership team. “MIT is uniquely positioned to harness the transformative power of digital tools and AI to shape future of manufacturing. I’m truly excited about what we can build together and the synergies this creates with other cross-cutting initiatives across the Institute.” The initiative is just the latest MIT-centered effort in recent decades aiming to expand American manufacturing. A faculty research group wrote the 1989 bestseller “Made in America: Regaining the Productive Edge,” advocating for a renewal of manufacturing; another MIT project, called Production in the Innovation Economy, called for expanded manufacturing in the early 2010s. In 2016, MIT also founded The Engine, a venture fund investing in hardware-based “tough tech” start-ups including many with potential to became substantial manufacturing firms. As developed, the MIT Initiative for New Manufacturing is based around four major themes: - Reimagining manufacturing technologies and systems: realizing breakthrough technologies and system-level approaches to advance energy production, health care, computing, transportation, consumer products, and more; - Elevating the productivity and experience of manufacturing: developing and deploying new digitally driven methods and tools to amplify productivity and improve the human experience of manufacturing; - Scaling new manufacturing: accelerating the scaling of manufacturing companies and transforming supply chains to maximize efficiency and resilience, fostering product innovation and business growth; and - Transforming the manufacturing base: driving the deployment of a sustainable global manufacturing ecosystem that provides compelling opportunities to workers, with major efforts focused on the U.S. The initiative has mapped out many concrete activities and programs, which will include an Institute-wide research program on emerging technologies and other major topics; workforce and education programs; and industry engagement and participation. INM also aims to establish new labs for developing manufacturing tools and techniques; a “factory observatory” program which immerses students in manufacturing through visits to production sites; and key “pillars” focusing on areas from semiconductors and biomanufacturing to defense and aviation. The workforce and education element of INM will include TechAMP, an MIT-created program that works with community colleges to bridge the gap between technicians and engineers; AI-driven teaching tools; professional education; and an effort to expand manufacturing education on campus in collaboration with MIT departments and degree programs. INM’s leadership team has three faculty co-directors: John Hart, the Class of 1922 Professor and head of the Department of Mechanical Engineering; Suzanne Berger, Institute Professor at MIT and a political scientist who has conducted influential empirical studies of manufacturing; and Chris Love, the Raymond A. and Helen E. St. Laurent Professor of Chemical Engineering. The initiative’s executive director is Julie Diop. The initiative is in the process of forming a faculty steering committee with representation from across the Institute, as well as an external advisory board. INM stems partly from the work of the Manufacturing@MIT working group, formed in 2022 to assess many of these issues. The launch of the new initiative was previewed at a daylong MIT symposium on May 7, titled “A Vision for New Manufacturing.” The event, held before a capacity audience in MIT’s Wong Auditorium, featured over 30 speakers from a wide range of manufacturing sectors. “The rationale for growing and transforming U.S. manufacturing has never been more urgent than it is today,” Berger said at the event. “What we are trying to build at MIT now is not just another research project. … Together, with people in this room and outside this room, we’re trying to change what’s happening in our country.” “We need to think about the importance of manufacturing again, because it is what brings product ideas to people,” Love told MIT News. “For instance, in biotechnology, new life-saving medicines can’t reach patients without manufacturing. There is a real urgency about this issue for both economic prosperity and creating jobs. We have seen the impact for our country when we have lost our lead in manufacturing in some sectors. Biotechnology, where the U.S. has been the global leader for more than 40 years, offers the potential to promote new robust economies here, but we need to advance our capabilities in biomanufacturing to maintain our advantage in this area.” Hart adds: “While manufacturing feels very timely today, it is of enduring importance. Manufactured products enable our daily lives and manufacturing is critical to advancing the frontiers of technology and society. Our efforts leading up to launch of the initiative revealed great excitement about manufacturing across MIT, especially from students. Working with industry — from small to large companies, and from young startups to industrial giants — will be instrumental to creating impact and realizing the vision for new manufacturing.” In her letter to the MIT community today, Kornbluth stressed that the initiative’s goal is to drive transformation by making manufacturing more productive, resilient, and sustainable. “We want to reimagine manufacturing technologies and systems to advance fields like energy production, health care, computing, transportation, consumer products, and more,” she wrote. “And we want to reach well beyond the shop floor to tackle challenges like how to make supply chains more resilient, and how to inform public policy to foster a broad, healthy manufacturing ecosystem that can drive decades of innovation and growth.”

Owen Gregorian

77,197 views • 1 year ago

Revolutionizing Bladder Cancer Diagnosis with ZayaAI We heard you Zayans, From our last videos we understood you all want a much easier to understand explanation of our AI models. So let's dive into the latest breakthrough in AI-powered healthcare, our Bladder Cancer Detection Model. This isn't just another AI tool, it's a game-changer for pathologists. Precision, Speed and Seamless Integration: Traditional diagnosis of urothelial carcinoma (bladder cancer) is often slow and prone to errors. Our AI model automatically detects low and high-grade tumors, along with lymphovascular invasion, with unmatched accuracy and speed. That means faster results, fewer mistakes and a more efficient workflow for medical professionals. Why This Matters: 1.Faster Diagnoses → AI speeds up the process, allowing pathologists to focus on critical cases. 2.Higher Accuracy → Reduces human error and reliance on costly, time-consuming tests. 3.Seamless Integration → No need for complex system overhauls. ZayaAI fits right into existing workflows. The Future of AI in Healthcare: Embracing our AI models isn’t just about using new technology, it’s an investment in a future where healthcare is more precise, efficient and patient-focused. Pathologists get to make faster, more informed decisions and ultimately, patient outcomes improve. This is how AI is redefining medical diagnostics and can help save lives. DeSci + AI = $ZAI ! 🔬⚕️
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Revolutionizing Bladder Cancer Diagnosis with ZayaAI We heard you Zayans, From our last videos we understood you all want a much easier to understand explanation of our AI models. So let's dive into the latest breakthrough in AI-powered healthcare, our Bladder Cancer Detection Model. This isn't just another AI tool, it's a game-changer for pathologists. Precision, Speed and Seamless Integration: Traditional diagnosis of urothelial carcinoma (bladder cancer) is often slow and prone to errors. Our AI model automatically detects low and high-grade tumors, along with lymphovascular invasion, with unmatched accuracy and speed. That means faster results, fewer mistakes and a more efficient workflow for medical professionals. Why This Matters: 1.Faster Diagnoses → AI speeds up the process, allowing pathologists to focus on critical cases. 2.Higher Accuracy → Reduces human error and reliance on costly, time-consuming tests. 3.Seamless Integration → No need for complex system overhauls. ZayaAI fits right into existing workflows. The Future of AI in Healthcare: Embracing our AI models isn’t just about using new technology, it’s an investment in a future where healthcare is more precise, efficient and patient-focused. Pathologists get to make faster, more informed decisions and ultimately, patient outcomes improve. This is how AI is redefining medical diagnostics and can help save lives. DeSci + AI = $ZAI ! 🔬⚕️

ZayaAI

18,015 views • 1 year ago

Introducing LifeGPT, showing that LLMs can simulate complex, Turing-complete systems like Conway's Game of Life with near-perfect accuracy—no prior topology needed.🌐This unlocks new potential for AI in modeling self-organizing systems in biology, materials science, & beyond.🔬🤖 #AI #LifeGPT. Cellular Automata (CA), like Conway's Game of Life ("Life"), are computationally irreducible, meaning their evolution is difficult to predict without an a-priori understanding of the rules of the game, including the topology on which it is played. LifeGPT is a topology-agnostic generative model that learns the rules of Life without prior knowledge of its grid structure or boundary conditions, from only a tiny number of game states. The success in simulating Life suggests promising avenues for scientific discovery, particularly in bridging the gap between AI, artificial life, and real-world biological systems, for both forward and inverse problems. The potential for universal computation within generative AI, including LLMs, through approaches like LifeGPT, represents an exciting area for future research, especially when combined with reinforcement learning. Model Convergence: LifeGPT exhibits rapid convergence during training, achieving high accuracy in predicting next-game-states. We attribute the non-zero cross-entropy loss to the lack of causal relationships within randomly generated ICs. Accuracy & Temperature: LifeGPT achieves near-perfect accuracy, particularly at lower sampling temperatures, but can be continually tuned towards higher creativity to discover patterns that the original ruleset would not be able to produce. This finding highlights the trade-off between model creativity (higher temperature) and accuracy in deterministic predictions, with high relevance to model real-world dynamical systems for which no closed-form rulesets exist. Zero/Few-Shot Learning: Trained on a small fraction of possible initial conditions, LifeGPT demonstrates strong zero/few-shot learning, accurately simulating Life for unseen initial conditions. However, rare prediction errors highlight that LifeGPT approximates rather than perfectly replicates the Life algorithm. Autoregressive Autoregressor: A recursive implementation of LifeGPT demonstrates the model's ability to simulate Life over multiple timesteps. LifeGPT is topology-agnostic with respect to its training data and our results show that a GPT model is capable of capturing the deterministic rules of a Turing-complete system with near-perfect accuracy, given sufficiently diverse training data. The work showcases the possibility for future models to synthesize stochastic generative capabilities with deterministic computational capabilities. Link to code, paper, etc. below. Podcast generated using #NotebookLM. LAMM@MIT DMSE at MIT
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Introducing LifeGPT, showing that LLMs can simulate complex, Turing-complete systems like Conway's Game of Life with near-perfect accuracy—no prior topology needed.🌐This unlocks new potential for AI in modeling self-organizing systems in biology, materials science, & beyond.🔬🤖 #AI #LifeGPT. Cellular Automata (CA), like Conway's Game of Life ("Life"), are computationally irreducible, meaning their evolution is difficult to predict without an a-priori understanding of the rules of the game, including the topology on which it is played. LifeGPT is a topology-agnostic generative model that learns the rules of Life without prior knowledge of its grid structure or boundary conditions, from only a tiny number of game states. The success in simulating Life suggests promising avenues for scientific discovery, particularly in bridging the gap between AI, artificial life, and real-world biological systems, for both forward and inverse problems. The potential for universal computation within generative AI, including LLMs, through approaches like LifeGPT, represents an exciting area for future research, especially when combined with reinforcement learning. Model Convergence: LifeGPT exhibits rapid convergence during training, achieving high accuracy in predicting next-game-states. We attribute the non-zero cross-entropy loss to the lack of causal relationships within randomly generated ICs. Accuracy & Temperature: LifeGPT achieves near-perfect accuracy, particularly at lower sampling temperatures, but can be continually tuned towards higher creativity to discover patterns that the original ruleset would not be able to produce. This finding highlights the trade-off between model creativity (higher temperature) and accuracy in deterministic predictions, with high relevance to model real-world dynamical systems for which no closed-form rulesets exist. Zero/Few-Shot Learning: Trained on a small fraction of possible initial conditions, LifeGPT demonstrates strong zero/few-shot learning, accurately simulating Life for unseen initial conditions. However, rare prediction errors highlight that LifeGPT approximates rather than perfectly replicates the Life algorithm. Autoregressive Autoregressor: A recursive implementation of LifeGPT demonstrates the model's ability to simulate Life over multiple timesteps. LifeGPT is topology-agnostic with respect to its training data and our results show that a GPT model is capable of capturing the deterministic rules of a Turing-complete system with near-perfect accuracy, given sufficiently diverse training data. The work showcases the possibility for future models to synthesize stochastic generative capabilities with deterministic computational capabilities. Link to code, paper, etc. below. Podcast generated using #NotebookLM. LAMM@MIT DMSE at MIT

Markus J. Buehler

114,217 views • 1 year ago

Today, we’re excited to launch Attention Labs. For almost 400,000 years, humanity has progressed through a simple but powerful cycle: we learn, we build, and we pass knowledge forward. Every major leap in civilization, from fire to medicine to computing, has expanded what humans are capable of achieving. We believe AI is the next great tool in that journey. While much of the world is focused on how AI can replace human work, we are focused on a different question: How can AI amplify human potential? At Attention Labs, we are building AI to discover brain-enhancing therapeutics, solve neurocognitive diseases, and accelerate the discovery of medicines for the human mind. We believe the greatest opportunity for AI is not creating a world with fewer humans in the loop, but a world where every human can think more clearly, learn faster, create more boldly, and live more fully. The biggest source of alpha in the age of AI will not come from replacing people. It will come from empowering them. Our mission is simple: Make humans the most intelligent entity on Earth. The future belongs to those who amplify human intelligence, not those who seek to replace it. We're just getting started. Attention Labs
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Today, we’re excited to launch Attention Labs. For almost 400,000 years, humanity has progressed through a simple but powerful cycle: we learn, we build, and we pass knowledge forward. Every major leap in civilization, from fire to medicine to computing, has expanded what humans are capable of achieving. We believe AI is the next great tool in that journey. While much of the world is focused on how AI can replace human work, we are focused on a different question: How can AI amplify human potential? At Attention Labs, we are building AI to discover brain-enhancing therapeutics, solve neurocognitive diseases, and accelerate the discovery of medicines for the human mind. We believe the greatest opportunity for AI is not creating a world with fewer humans in the loop, but a world where every human can think more clearly, learn faster, create more boldly, and live more fully. The biggest source of alpha in the age of AI will not come from replacing people. It will come from empowering them. Our mission is simple: Make humans the most intelligent entity on Earth. The future belongs to those who amplify human intelligence, not those who seek to replace it. We're just getting started. Attention Labs

Temi

37,046 views • 2 months ago

The Kimi AI Model And What If This Carnegie Mellon, PhD student was incentivized to open source AI in the US? The answer is Kimi would have been an open source US model. Yang Zhilin Moonshot’s founder and maker of the Kimi series turned his deep research expertise into one of the world’s most capable open AI systems. After earning his PhD at Carnegie Mellon under leading researchers and interning at Google Brain and Meta, he returned to China and co-founded Moonshot AI in March 2023 with Tsinghua classmates Zhou Xinyu and Wu Yuxin. Why? The US VC world and tax structure did not favor Zhilin’s proposal to open source the AI models as a strategy. So he left. He named the company after his favorite Pink Floyd album, reflecting his ambitious vision for scalable. Yang assembled a core technical team of inventors behind breakthroughs like Transformer-XL and RoPE. Together they focused on turning massive compute into efficient intelligence through innovative architectures. The journey began with the Kimi chatbot in October 2023, rapidly scaling context from 200,000 to millions of characters. This evolved into the Kimi series: K1.5 matched top reasoning models, K2 introduced a 1-trillion-parameter Mixture-of-Experts design trained on 15.5 trillion tokens and released openly, and K2 Thinking added advanced agentic capabilities. Kimi K3 represents the pinnacle, this 2.8-trillion-parameter model uses a sparse MoE architecture with 896 experts (only 16 active per token), new Kimi Delta Attention and attention residuals for efficiency, and a 1-million-token context window. It delivers frontier performance in long-horizon coding, reasoning, and multimodal tasks at competitive cost, with weights set for open release. Yang’s approach emphasizes openness, efficiency, and continuous self-improvement — enabling solo developers and teams to achieve what once required massive resources. By sharing technical insights in public talks, he has accelerated global progress toward more accessible, powerful AI. Imagine if we held open source higher than the fear theater games of Anthropic? We are chasing out some of the best minds. This is how you lose…
17:29

The Kimi AI Model And What If This Carnegie Mellon, PhD student was incentivized to open source AI in the US? The answer is Kimi would have been an open source US model. Yang Zhilin Moonshot’s founder and maker of the Kimi series turned his deep research expertise into one of the world’s most capable open AI systems. After earning his PhD at Carnegie Mellon under leading researchers and interning at Google Brain and Meta, he returned to China and co-founded Moonshot AI in March 2023 with Tsinghua classmates Zhou Xinyu and Wu Yuxin. Why? The US VC world and tax structure did not favor Zhilin’s proposal to open source the AI models as a strategy. So he left. He named the company after his favorite Pink Floyd album, reflecting his ambitious vision for scalable. Yang assembled a core technical team of inventors behind breakthroughs like Transformer-XL and RoPE. Together they focused on turning massive compute into efficient intelligence through innovative architectures. The journey began with the Kimi chatbot in October 2023, rapidly scaling context from 200,000 to millions of characters. This evolved into the Kimi series: K1.5 matched top reasoning models, K2 introduced a 1-trillion-parameter Mixture-of-Experts design trained on 15.5 trillion tokens and released openly, and K2 Thinking added advanced agentic capabilities. Kimi K3 represents the pinnacle, this 2.8-trillion-parameter model uses a sparse MoE architecture with 896 experts (only 16 active per token), new Kimi Delta Attention and attention residuals for efficiency, and a 1-million-token context window. It delivers frontier performance in long-horizon coding, reasoning, and multimodal tasks at competitive cost, with weights set for open release. Yang’s approach emphasizes openness, efficiency, and continuous self-improvement — enabling solo developers and teams to achieve what once required massive resources. By sharing technical insights in public talks, he has accelerated global progress toward more accessible, powerful AI. Imagine if we held open source higher than the fear theater games of Anthropic? We are chasing out some of the best minds. This is how you lose…

Brian Roemmele

37,185 views • 14 days ago

Today we are launching Openwork is now Accomplish, an open-source (MIT-licensed) computer-use agent that’s fast, cheap, and more secure. Openwork is now Accomplish is the result of a short two-day hackathon our team decided to hack, which brings together some of our favorite open source AI modules into one powerful agent, to allow you to: 1. Bring your own model/API key (any provider and model supported by OpenCode is supported by Openwork) 2. ~4x faster than Claude for Chrome/Cowork, and much more token-efficient, powered by dev-browser by Sawyer Hood (legend) 3. More secure - contrary to Claude for Chrom/Cowork, does not leverage the main browser instance where you are logged into all services already. You login only to the services you need. This significantly reduces the risk of data loss in case of prompt injections, to which computer-use agents are highly exposed. 4. Free and 100% open-source! You can download the DMG (macOS only for now) or fork the github repo via the link in bio (Openwork is now Accomplish). Let us know what you think (or better, send a pull request)!
1:23

Today we are launching Openwork is now Accomplish, an open-source (MIT-licensed) computer-use agent that’s fast, cheap, and more secure. Openwork is now Accomplish is the result of a short two-day hackathon our team decided to hack, which brings together some of our favorite open source AI modules into one powerful agent, to allow you to: 1. Bring your own model/API key (any provider and model supported by OpenCode is supported by Openwork) 2. ~4x faster than Claude for Chrome/Cowork, and much more token-efficient, powered by dev-browser by Sawyer Hood (legend) 3. More secure - contrary to Claude for Chrom/Cowork, does not leverage the main browser instance where you are logged into all services already. You login only to the services you need. This significantly reduces the risk of data loss in case of prompt injections, to which computer-use agents are highly exposed. 4. Free and 100% open-source! You can download the DMG (macOS only for now) or fork the github repo via the link in bio (Openwork is now Accomplish). Let us know what you think (or better, send a pull request)!

Or Hiltch

1,002,753 views • 6 months ago

Today, Recursion reported our 4Q/FY25 business updates and financial results. As CEO and President Najat Khan said, “Recursion has reached an inflection point: moving from proving that AI can participate in drug discovery to demonstrating that an AI-native operating system can generate clinical proof and durable value.” This includes: 🚀 A 5th milestone with Sanofi for a first-in-class Sanofi-partnered oncology program against a historically difficult and novel biological space. This brings the total upfront and progress-based milestones to $134 million to date. Five Recursion discovery program packages have been accepted to date, establishing a growing joint portfolio of novel AI-driven small molecules for immunology and oncology. 🚀 The first AI-enabled clinical validation of the Recursion OS platform in our FAP program, demonstrating translation from AI-driven biological insight to meaningful patient outcomes. We also have multiple clinical and preclinical programs advancing with defined milestones. 🚀 New preclinical efficacy data for REC-7735, a potential best-in-class PI3K⍺ H1047R inhibitor, precision designed with 242 compounds synthesized from first novel hit to REC-7735 in 10 months using the Recursion OS platform. REC-7735 demonstrates >100-fold selectivity for the H1074R mutation over WT PI3K⍺ suggesting potential improved tolerability over current inhibitors and is currently in IND-enabling studies. 🚀 $754 million of cash and cash equivalents. We exceeded our original cost savings guidance and now expect runway into early 2028, without additional financing. 👉 Read the full business updates here: 🔹 Tune in to our Earnings Call today, February 25, 2026 at 8:00 am ET / 6:00 am MT / 1:00 pm GMT, here on X, or on: ▪️ YouTube: ▪️ LinkedIn:
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Today, Recursion reported our 4Q/FY25 business updates and financial results. As CEO and President Najat Khan said, “Recursion has reached an inflection point: moving from proving that AI can participate in drug discovery to demonstrating that an AI-native operating system can generate clinical proof and durable value.” This includes: 🚀 A 5th milestone with Sanofi for a first-in-class Sanofi-partnered oncology program against a historically difficult and novel biological space. This brings the total upfront and progress-based milestones to $134 million to date. Five Recursion discovery program packages have been accepted to date, establishing a growing joint portfolio of novel AI-driven small molecules for immunology and oncology. 🚀 The first AI-enabled clinical validation of the Recursion OS platform in our FAP program, demonstrating translation from AI-driven biological insight to meaningful patient outcomes. We also have multiple clinical and preclinical programs advancing with defined milestones. 🚀 New preclinical efficacy data for REC-7735, a potential best-in-class PI3K⍺ H1047R inhibitor, precision designed with 242 compounds synthesized from first novel hit to REC-7735 in 10 months using the Recursion OS platform. REC-7735 demonstrates >100-fold selectivity for the H1074R mutation over WT PI3K⍺ suggesting potential improved tolerability over current inhibitors and is currently in IND-enabling studies. 🚀 $754 million of cash and cash equivalents. We exceeded our original cost savings guidance and now expect runway into early 2028, without additional financing. 👉 Read the full business updates here: 🔹 Tune in to our Earnings Call today, February 25, 2026 at 8:00 am ET / 6:00 am MT / 1:00 pm GMT, here on X, or on: ▪️ YouTube: ▪️ LinkedIn:

Recursion

11,878 views • 5 months ago

Connecting all intelligence with ORA ⏺️ So, how does it work? Let's dive in 👇 At the heart of ORA is the Onchain AI Oracle (OAO), designed to empower dApp developers by seamlessly integrating verifiable AI inference into their applications without sacrificing decentralization. At the center of ORA is Optimistic Machine Learning (opML). This technology ensures that AI inferences originate from the correct models and that results remain untampered. A robust network of nodes independently verifies each AI inference and can raise challenges if tampering is suspected. Want to be part of this decentralized movement? You can easily run your own node using Tora, our user-friendly node software. Built on Optimistic Machine Learning (opML), our Onchain AI Oracle connects the best open-source AI models to the blockchain. This means any dApp can easily become an AI dApp! With our Initial Model Offering (IMO), developers can tokenize open-source models, effectively overcoming the cold start problem by securing essential funding for their projects. ORA is integrated with several L2s, including Mantle Network, Linea.eth, and Arbitrum. This allows you to build on low-cost chains that best suit your needs. ORA is committed to bringing decentralized, verifiable AI to the blockchain. Are you ready to join us? Use OAO: Read more about OAO: Learn about IMO: Use Tora:
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Connecting all intelligence with ORA ⏺️ So, how does it work? Let's dive in 👇 At the heart of ORA is the Onchain AI Oracle (OAO), designed to empower dApp developers by seamlessly integrating verifiable AI inference into their applications without sacrificing decentralization. At the center of ORA is Optimistic Machine Learning (opML). This technology ensures that AI inferences originate from the correct models and that results remain untampered. A robust network of nodes independently verifies each AI inference and can raise challenges if tampering is suspected. Want to be part of this decentralized movement? You can easily run your own node using Tora, our user-friendly node software. Built on Optimistic Machine Learning (opML), our Onchain AI Oracle connects the best open-source AI models to the blockchain. This means any dApp can easily become an AI dApp! With our Initial Model Offering (IMO), developers can tokenize open-source models, effectively overcoming the cold start problem by securing essential funding for their projects. ORA is integrated with several L2s, including Mantle Network, Linea.eth, and Arbitrum. This allows you to build on low-cost chains that best suit your needs. ORA is committed to bringing decentralized, verifiable AI to the blockchain. Are you ready to join us? Use OAO: Read more about OAO: Learn about IMO: Use Tora:

ORA

453,392 views • 1 year ago