Introducing Microsoft-Decision-1, our new model for fast decision-making. It... delivers top performance on structured decision tasks, outperforming both LLMs and other decision models in latency and quality. We’re already testing it across Microsoft for everything from incident response and quality control to scientific discovery.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

Microsoft Asia just dropped Mage-Flow on Hugging Face a... smol 4B model for image generation and editing that matches much larger models in quality and it's fast! 4 steps in < 1s at 1024x1024 (model goes up to 4K) try out on spacesshow more

Introducing Span-02: anytime reasoning for decisions. Reasoning models make... you pick an effort level and wait. Span-02 takes a deadline and always returns its best decision so far. It gets better the longer it thinks. With reasoning off, it beats Jev at 1/4 of the price, and runs at 1/3 the latency of decision models at its level. It can: - Answer with numbers and scores, not just probabilities and choices - See images and screens (multimodal) - Take a configurable reasoning time And it’s fully open weight!show more

Introducing Amos 1, four open-weight decision models from 0.5B... to 27B. > 94.9% on Decision Bench, ahead of Perplexity, Gemini and Jev > beats Jev by 10 points on JevBench hard > reads photos and answers with boxes > decisions in 27 to 122 ms, free API, Apache 2.0show more

$0.04 per 1M input tokens 128K context #1 on... the Decision Index sub-second decisions Drex is looking less like “another small model” and more like an actual decision layer for agents and LLMs especially if you don’t want to burn a huge model’s tokens just deciding what should happen nextshow more

Jev + SERV is actually insane. We already showed... you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.show more

Nace shows Drex playing a strategy game. i gave... it a harder opponent: my timeline Spoiler Guard sends each post to the new decision model and blurs the spoilers, 352 post checked in one session with sub-second calls → 128K context window, per Nace the first and only decision model at that size → #1 on the Decision Index, a Jev rival for $0.04 per 1M input tokens → no text generated, just a probability per post try it with 250M free tokens from Nace AI:show more

instead of generating text, jev from TypeSafe AI generates... structured output this makes it great for classification tasks like model routing, tool selection/search, and guardrails of many forms! it's also ridiculously fast and cheap compared to LLMs doing the same tasksshow more

"You can't beat it here." 👍 Clayton Freshcorn on... his decision to return to Aggieland and wear Texas A&M across his chest for one more season:show more

Our upgraded database on discharges of radionuclides from nuclear... installations is now available! It's a valuable resource for understanding environmental releases and supporting decision making in nuclear safety. 📊show more

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft paper... page: Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective for creating instruction-following sequential decision-making agents. STEVE-1 is trained in two steps: adapting the pretrained VPT model to follow commands in MineCLIP's latent space, then training a prior to predict latent codes from text. This allows us to finetune VPT through self-supervised behavioral cloning and hindsight relabeling, bypassing the need for costly human text annotations. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. We provide experimental evidence highlighting key factors for downstream performance, including pretraining, classifier-free guidance, and data scaling. All resources, including our model weights, training scripts, and evaluation tools are made available for further research.show more

Diners can now connect to Resy in Claude! AI-powered... discovery is becoming a way many people decide where to dine, so we’re meeting them in those decision-making moments – and driving more demand to Resy restaurants. Read more about how it works:show more

Microsoft is testing a new feature in Windows 11... called Low Latency Profile that gives a short boost to your CPU for 1 to 3 seconds when you open apps or menus. Early tests show Microsoft apps like Edge and Outlook open up to 40% faster, while the Start menu and other menus can load up to 70% faster, with CPU use jumping high for a few seconds then dropping right back down. Microsoft says this is normal because Apple does it in macOS and Linux, plus phones use the same type of quick CPU boosts for better speed. The feature is only in early Insider testing now, so it is not out for everyone yet.show more

You don’t have to use one model (or one... provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.show more

Still zero lane change hesitation with Tesla FSD v14.2.2.2... for me 6 drives in. As expected, really awesome speed control and lane decision too, noticing it moving over earlier for exits on this build which is great.show more

Master's and Ph.D. students at Islamic Azad University in... Tehran gathered outside the university's central headquarters in Tehran on Tuesday to protest a decision to hold their exams in person, a video sent to Iran International showed. Students are objecting to a last-minute decision to hold exams in person after a semester of remote learning necessitated by January's protests and the war, arguing the switch is both inconsistent and unfair given the financial and logistical burden it places on graduate students who have already returned home.show more

Iran thanked Pakistan at the UN Security Council, praising... Pakistan’s decision to abstain on the draft resolution in view of the serious implications it could have had for regional peace and stability.show more