OpenAI’s first bio LLM shows one foundation model isn’t... enough—specialist models are the future. Transparency, Verifiable + data attribution = the key to next-gen innovation. #DeSciAIshow more

Syntropy becomes Synternet 🍾 Our rebrand to Synternet is... the natural evolution to becoming the primary gateway for real-time, on-chain data for everyone, everywhere. Synternet isn’t just a new name, it’s the foundation for the next gen of interoperable data infrastructure 👾show more

Yesterday, we announced the launch of DeepProve: verifiable AI... for the future of humanity. Already, we’re lining up with partners across web3 to build a future where we can prove that AI models are operating as expected. Inference Labs - already pioneers of trustless AI - is collaborating with Lagrange to build the next gen of DeAI. 🧵show more

The next wave of AI won’t come from corporations.... It’ll come from people. Building, training, and powering intelligence together. Because transparency isn’t just a feature, its the foundation on which progress is built. That’s the future DeepNode is building.show more

The internet is evolving - are you ready? 🌐⚡... At SUI DePIN, your data isn’t just collected - it’s owned by you. Monetize it. Secure it. Power the next generation of AI on the Sui! ✅ Own your data ✅ Earn real rewards ✅ Fuel AI innovation No middlemen. No limits. Just you and your data. Step into the future today 👉show more

We are releasing the fastest TTS model as open... source! One of the biggest problems we encountered while optimizing TTS models is optimization itself. Since it is LLM-based, even optimizing with VLLM and SgLang libraries is not enough. Additionally, I made the outputs much better with special optimizations for training. The voices you are listening to are results obtained from only 20% of the model training. We trained these outputs on 8xB200 in 24 hours. Even though the dataset includes some low-quality data, the model's output is much better. We will release the code as open source tomorrow. Data: Emilia-3m Model: EchoDit (custom) Codec: DacVae Opt: Flash-attn + CacheDitshow more

Crypto decisions are often driven by noise and emotion.... But the future belongs to clarity, data, and transparency. CrypGPT is built with an AI-first mindset — because crypto should be smarter, not louder. AI • Data • Blockchain #CrypGPT #CryptoAI #Web3Community #CryptoInnovation #DataDriven #CryptoCommunity #DigitalAssetsshow more

Data centers today were not built for the technological... innovation shaping tomorrow. Located on the largest energy grid in the US, Bitfarms is developing performance-driven, next-generation facilities engineered to meet the future of AI. That’s how we are Positioned to Power. #AI #HPC #Energy #PositionedToPower #Bitfarmsshow more

A Letter to Our Community: The Road Ahead for... Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷show more

We are excited to launch our two models Pharia-1-LLM-7B-control... and Pharia-1-LLM-7B-control-aligned. Both models and the code used to train them are now publicly available and open-sourced for non-commercial research and educational use. Read our model blog post here: Learn more about our open-source codebase Scaling: #writtenbyalephalphashow more

💥 Justice AI isn’t here to fix the system.... It’s here to build a new one. One that doesn’t bow to wealth or influence. Just truth, transparency, and trustless governance. Join the future of justice ✌🏻 👉 🔗 CA: bKphoUfpTd5AVqbfmv3wWUyneWL8ZdVAHthw388pumpshow more

Open weight models are now available in Kiro. Three... open weight models are available in the IDE and CLI, so you don’t have to default to the largest model for every task. Choose the right model based on speed, cost, or long-running agentic workflows. What model would you like to see supported next? #Kiro #BuildwithKiro #AICodingshow more

Running multiple accounts on one device was never supposed... to work at scale. Yet professionals do it every day without bans, flags, or leaks. Octo Browser is the reason. A next-gen anti-detect infrastructure built to replicate real devices, secure data, and manage hundreds of accounts seamlessly. Multi-accounting isn’t the risk anymore; weak tools are. 👇show more

We’re proud to share that Tamarind Bio has been... selected to build, host, and operate the inference infrastructure layer for TuneLab2.0, the next evolution of the platform. Eli Lilly and Company TuneLab is a first-of-its-kind, collaborative AI/ML drug discovery platform, bringing models trained on over $1B worth of Lilly proprietary data to the biotech ecosystem. Tamarind will power TuneLab’s scalable drug discovery workflows and model inference.show more

Robotics is obsessed with foundation models and humanoids. It’s... missing the most critical piece. One founder just raised $3M to build the “AWS for robots.” Fixing the silent bottleneck for most robotics startup: Data Infrastructure: 🧵show more

The future of bio is powered by faster data... Introducing the Medra AI Experimentalist: an agent that turns goals into experimental designs, learns from every result, and develops the next assay Excited to collaborate with DARPA and NVIDIA Healthcare on the future of scienceshow more

Humanoid Behavior Foundation Models are entering the perception era.... Perceptive BFM shows how vision can adapt the same human motion prior to different terrains instead of assuming the environment is already compatible. Really impressive work!show more

🚀 We introduce Neural Theorizer (NEO) — a new... type of world model that learns to theorize the world from observation, without language or LLM supervision. Selected as an ICML 2026 oral presentation — 0.7% of submitted papers. The paper asks: "What does it mean to understand the world and build a world model?" Today’s world models are often trained to predict the future: the next frame, next latent state, or next observation. But is prediction enough? We argue that a world model should be a theory-building system: one that discovers reusable primitives, composes them into executable explanations, and transfers those explanations to novel phenomena. NEO is our first step toward this vision — a World Theory Model that learns explicit, compositional theories from raw observation. This work was led by my wonderful students: Doojin Baek*(Doojin Baek), Gyubin Lee* (GyuBin Lee), Junyeob Baek (Junyeob Baek), and Hosung Lee (Hosung Lee). For more details, take a look at the paper — and if you’re attending ICML, let’s talk there! 📄 arXiv: 🌐 Project page:show more

Besties, one festival isn’t enough! #AllThingsGo is launching in... NEW YORK CITY on September 28-29, 2024 at the world-famous Forest Hills Stadium. Lineup drops on Tuesday ⛅️🗽 Sign up to get the lineup first at - link in bioshow more