Semantic search improves our agent's accuracy across all frontier... models, especially in large codebases where grep alone falls short. Learn more about our results and how we trained an embedding model for retrieving code.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

AI has huge potential to change how we work... and live. That was clear at Montreal’s ALL IN Conference. But we also need to make sure our AI ecosystem can develop responsibly – and that’s what our new code of conduct does. Learn more about the code here:show more

Today, we announced our earnings results for Q3 2024.... This quarter, we delivered $1.37B in revenue and our community grew to 443M DAU! Learn more atshow more

Search just got an upgrade! Our new Advanced Search... lets you set your location, apply updated filters, and customize your search between tournaments, leagues, and hubs much faster than before. Learn about this and more in our new Balance Patch article below!show more

A 35B open-weight model trained to search a precomputed... index answers repo search questions at 100x lower cost than a frontier model. We partnered with turbopuffer to train Qwen3.6-35B-A3B to find code across ~9,000 repositories. It tops the needle-in-a-haystack task outright at 2-10x lower latency.show more

Taking a short break from LLM research to go... back to my roots. Introducing... Concept Lenses! A training-free way to do similarity search along whatever concept you care about. Typically, embedding-based similarity search is constrained by whatever notion of similarity the model learned in training. This means that if the model is primed for semantic similarity, but you actually care more about visual appearance, you'd need to switch models. Under a Concept Lens projection, though, distance between embeddings means distance only along the concept you care about. Blog post: Demo:show more

New Generation Model! 🚨 We're introducing the Mistral model... to our expanding lineup of generation models. Mistral brings efficient performance and strong language understanding capabilities to our platform. Initial testing shows promising results in code comprehension and generation tasks, making it a valuable addition to development workflows. While we continue to optimize its implementation, early benchmarks demonstrate consistent and reliable outputs across various programming tasks.show more

Meet Scout 1.0, our first AI model built for... investigating production incidents. Scout matches frontier-model investigation quality at a fraction of the cost, which makes it practical to investigate more issues, earlier. We trained Scout on realistic simulations built from real-world incidents and production telemetry, using reinforcement learning to reward accurate root causes backed by evidence. Learn more and sign up for the preview:show more

Announcing our $320M Series A at a $2.3B valuation,... led by Khosla Ventures, with General Catalyst, Eric Schmidt and Jeff Bezos. General Intuition is the frontier lab for acting in space and time. We build large action foundation models trained on billions of ground truth action-labeled gameplay clips from 17M monthly active users on Medal, and push the frontier of world models to generate infinite training environments.show more

Many of you asked for code & weights for... π₀, we are happy to announce that we are releasing π₀ and pre-trained checkpoints in our new openpi repository! We tested the model on a few public robots, and we include code for you to fine-tune it yourself.show more

We're excited to introduce Arcee.ai's Trinity Large model. An... open 400B parameter Mixture of Experts model, delivering frontier-level performance with only 13B active parameters. Trained in collaboration between Arcee, Datology and Prime Intellect.show more

Introducing exa-code, a big step towards eliminating LLM code... hallucination. We indexed 1B+ docs pages, Github repos, StackOverflow posts, and more. Given a query, exa-code initiates a hybrid search over this data, chunks it, and returns a concatenated, token-efficient string. In our code hallucination eval, exa-code outperforms every popular web search tool (including Exa!). Available for free: How we built it:show more

We trained our Large Sensor Model (LSM) on over... 40 million hours of de-identified multimodal sensor data from 165K users to demonstrate how it could improve performance in wearable tasks like exercise and activity recognition. Here’s what we found →show more

Accidentally ran an accuracy test on our MicroFactory robotic... arm. I had to disassemble the base for some improvements. To keep testing consistent for our already-trained models, I quickly built an improvised setup to recreate the previous calibration. After removing and reinstalling the motors, I was able to restore their position within 0.002°. Positioning repeatability was at least 0.01 mm at 300 mm reach. This isn’t a full accuracy test — a proper one would require more planes and indicators with resolution at least an order of magnitude better than the measured values. But anyway, this accuracy is sufficient for all of our assembly tasks.show more

September is an important month for blood and platelet... donation: ❤️ #BloodCancerAwarenessMonth 💛 #ChildhoodCancerAwarenessMonth ❤️ #SickleCellAwarenessMonth Keep your eyes on our page to learn more about each of these and how you can support our mission to save more lives.show more

We built high-throughput materials labs in Menlo Park to... create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon. This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials. Read our blog posts below.show more

1/ Happy to share VADER: Video Diffusion Alignment via... Reward Gradients. We adapt foundational video diffusion models using pre-trained reward models to generate high-quality, aligned videos for various end-applications. Below we generated a short movie using VADER 😀, we used ChatGPT to write a script and an off-the-shelf AI music generator to generate the sound. Our code & weights are open-sourced:show more

Video understanding is the next frontier, but not all... videos are alike. Models now reason over youtube clips and feature films, but what about the everyday spaces we—and our future AI assistants—navigate and experience? Introducing Thinking in Space, our latest study exploring how multimodal LLMs see, remember and recall spaces. 🧵[1/n]show more