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Mistral AI has released Timestral, its first family of time series foundation models. Timestral is a set of 4 zero-shot forecasting models from Mistral AI, sized from 5M to 209M parameters. The largest checkpoint ranks first among pretrained, zero-shot models on GIFT-Eval, TIME and fev-bench. It does this without a GPU at inference. For developers,
An AI agent that reads the web pays 2 kinds of cost. The first is the retrieval charge: what a search or fetch API bills per request. The second is model processing: content passed from retrieved pages into an AI model consumes tokens, while reasoning and generated responses add further costs. TinyFish, a web infrastructure
TinyFish Makes Search Free: What It Really Costs an AI Agent to Read the Web Read More »
Google Cloud AI Research has open-sourced Harness². It is a recursive agent harnessing framework that improves the code and files around a frozen model. For developers, the pitch is simple. Your agent may get better without a better model. TL;DR Size: A framework, not a model. 0 parameters trained; 8 editable harness components. Version 0.1.0.
Google Open-Sources Harness2, a Recursive Agent Harnessing Framework Read More »
Voxel51 has released Voxel51 3.0, a very important update to its data platform. Voxel51 3.0 is an end-to-end multimodal data platform that puts search, curation, annotation and evaluation in one system. It targets teams training models on sensor data from robots, vehicles and factories. The release adds Voxel51 Agent, a data provenance layer and free
Meet Voxel51 3.0: A Unified Multimodal Data Platform for Physical AI Read More »
Kyutai, with Inria and École des Ponts, has released REKI, a 2.4B vision-language-action model (VLA) for robot manipulation. The REKI vision-language-action model is pretrained only on public data. It also carries a cheap memory of its recent past. On three out-of-distribution benchmarks, only models about twice its size beat it. All weights are live on
Meet Kyutai REKI: The 2.4B VLA Model Bringing Efficient Memory to Robot Manipulation Read More »
DeepSeek has just released DeepSeek-V4.1-Flash-1017, a new checkpoint of its V4.1-Flash model. It is live on the DeepSeek website, app and API. DeepSeek claims stronger scores, faster task completion and shorter outputs than the September V4.1-Flash release. The company calls it a Pareto improvement in performance and cost. Alongside it, DeepSeek opened its Search and
DeepSeek Just Released V4.1-Flash-1017, Scores 77.0 on DeepSWE, Opens Search API Read More »
TypeSafe launched Jev on September 15, 2026. It is a decision model, not a chatbot. You define the allowed answers, and Jev returns a label, a score or a yes/no probability. In about 4 weeks, at least 20 Jev alternatives shipped. They come from Cloudflare, OpenAI, Perplexity, Liquid AI, AWS’s Strands Labs and independent developers.
20 Jev Alternatives: The Decision Models Taking On TypeSafe in 2026 Read More »
StepFun has released Step 5 Preview as open weights. FP8 and BF16 checkpoints are live on Hugging Face and ModelScope. Step 5 Preview is a sparse Mixture-of-Experts model from StepFun built for long-horizon agentic work. It has 600B total parameters, 27B active per token and up to 1M tokens of context. For developers, this is
StepFun Releases Step 5 Preview Open Weights: 600B MoE, 27B Active, 1M Context Read More »
Perplexity has open-sourced SPACE, the sandbox platform behind every task in Perplexity Computer. The Perplexity SPACE sandbox gives each agent task its own Firecracker microVM. Credentials and outbound traffic are controlled from outside that VM. The code ships under Apache 2.0 on GitHub. For teams building coding or browsing agents, this is a production-tested isolation
Perplexity Open-Sources SPACE, a Firecracker MicroVM Sandbox for AI Agents Read More »
Liquid AI has released d1-ultra, its most capable decision model to date. The Liquid AI d1-ultra decision model reads text, images, or both. It answers typed questions with a probability for each possible answer. It does this in one forward pass and generates no output tokens. For developers, that means classification, routing and scoring calls
