People who are good at coming up with ideas will enjoy great leverage in the age of AI

Imagine your dog (500M neurons) searching articles on robotics and taking notes for you. The TINIEST tool caller on the planet has 500M neurons (like your dog) and now supports Anthropic MCP. This means hundreds of tools are now instantly accessible to your edge running

Build your personal assistant for research, dev pipelines, and daily workflows, running entirely on edge. Your needs, your agent.

111k downloads, Accelerate

Running Dria nodes got even easier. Next up: 🎯Node Runner dashboard with personalized stats 🤔Referral program for Node Runners 🤫A few surprises for our Steps program... What else would you like us to build for you?

Smaller models with optimal routing can match top-tier performance at lower cost. We tested routing strategies across 16 models. MIP routing achieved 95% accuracy at just 50% of the best model's budget, while NDCH(P) reached 99% accuracy at 50% cost on math reasoning tasks.

Contributing to accessible AI just got even better. Run node at home, track your impact, and stack $DRIA points.

We are building a global silicon fabric, integrating CPUs, GPUs, and NPUs to create the biggest decentralized AI Accelerator: * 30,000+ nodes * 15,000+ active nodes per day * 50M+ inference Learn more about Dria Launcher—over half a million downloads—and how to contribute 👇

Silicon is everywhere, yet nearly 99% of the world’s hardware isn’t part of the AI‑Energy economy. It’s both a software and orchestration challenge across heterogeneous hardware, a problem that only crypto can solve.

i convinced an ai agent to give me money with SWAN 🦢

Join us for a deep dive into open-source AI, RL, commodity hardware inference, and next-gen silicon. May 7th — we’re hosting: - Kyle Corbit (Kyle Corbitt) from OpenPipe, sharing insights on reinforcement learning and ART, an open-source RL library for agentic AI. - Peter

Most of the world's energy flows through heterogeneous hardware, yet today's AI software is designed for homogeneous and centralized setups. The concentration of high-performance AI into the hands of a few powerful entities poses profound risks to humanity. Introducing Dnet: a

We introduce a decentralized, guided-generation pipeline using classifier-free guidance, dynamic negative prompts, and iterative regeneration to asynchronously scale diversity, overcoming duplicates and static curricula in LLM-based synthetic data.

Launching Decentralized Batch Inference—cutting costs by 5x+ vs. traditional providers—and hitting 20,000 daily active users contributing hardware globally. Dria Batch API is perfect for synthetic data, offline model eval, and cost-sensitive batch jobs.

Introducing our research on low-overhead, privacy-preserving distributed inference: Protecting inputs, outputs, and model weights using lightweight permutations, enabling efficient and privacy-preserving LLM inference at scale.

NYC July 1: Join us to explore private/distributed inference & agent-native platforms. Talks: Brandon Reagen (FHE Framework for Deep Learning), Shubham Toshniwal (Synthetic Data), Aaron Wright (AI Driven Venture Capital), Stefan Teleman (Distributed Inference).

DriaConnect drew more than 200 attendees to Betaworks NYC last week. Explore sessions on agentic AI, synthetic data, distributed inference, and private AI featuring researchers from NVIDIA, NYU, LastMile, and more below.