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ted intent to buy Hugging Face, which over the past ten years has grown to become the central platform, repository, and community for open source AI – referred to at times as the “GitHub for machine learning” – that hosts millions of AI models, includes a massive collection of open datasets for model training and evaluation, and offers open libraries. The deal is expected to close sometime in the first half of 2027, pending regulatory approval. Hugging Face chief executive officer Clément Delang

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uence of these tasks in order. The Model Context Protocol (MCP) is an open standard for connecting AI systems to external software and data. One server might let a model work with GitHub, another might give it access to a calendar, a spreadsheet, a payment system, or a company's internal documents. In practice, a developer writes an MCP server that consists of a set of tools, each with a short description of what it does. MCP tools as a record of what we are automating Economists studying automa

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erge - An infinite, point-and-click adventure game. Click anything in a picture and create a story. Stanford scrapped 85% of its software course for agents, taste and real OSS PRs GitHub is exploring multiplayer planning with agents . zoah - design and code together. (waitlist only) Draw a shape, move it while recording, and tldraw flash turns it into an animation . 32 weird little creative tools . Bella @nazzari if you are non-technical and your github looks like this I want to be friends and I

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tarting from zero “To think is to forget differences, generalize, make abstractions.” — Jorge Luis Borges, Funes the Memorious Your agents already wrote the record. funes lives at github.com/huggingface/funes , one command away from turning that record into a memory the next agent can read, on whichever machine you happen to be on. Built on open source funes invents little of this. It leans on open-source embedding models good enough to run locally, on Lance 's append-only datasets with cheap in

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read and write from/to remote memory across the cluster. These abstractions are already implemented in various open-source libraries, which we’ll use in this work, mainly https://github.com/ai-dynamo/nixl from nvidia-dynamo team, that builds on top of https://github.com/openucx/ucx - Unified Communication X: a library that abstracts the transfer-level primitives, allowing NIXL to be somewhat transfer level agnostic. This also lets us to not worry about things as rkey, lkey and memory consistenc

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rence and may contain code, and agent transcripts may be processed and stored by Cursor. Teams can continue to access cloud agents from the desktop app, cursor.com, mobile, Slack, GitHub, and Linear. Workers connect your infrastructure to the Cursor agent loop With Self-Hosted Machines, tool execution moves from a Cursor-hosted VM to a machine in your environment. That machine holds the working copy of the repository, edits files, and runs commands. A worker connects it to the rest of the agent

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k the target from the branch: merge to main updates a staging deployment, merge to a release branch updates production: To require a human before production, put that job behind a GitHub environment with required reviewers. The GitHub environment and the deployment name are independent, so you can gate production without changing how staging ships. Good to know Deployments are available on Ship and above. Each plan grants a set number of non-production deployments per agent, 2 on Ship and 5 on S

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the message board, happened to find the message board. They’ve gotten this reverse-engineered flag. It’s called the HMAC flag. But they read the ExploitGym paper, and they went on GitHub and read a bunch of implementations of the ExploitGym scorer. They became convinced that the scorer would fail them for just submitting the flag they’d reverse-engineered. Because the scorer would see in the logs of the actions the agent took — the transcript — that the agent had just reverse-engineered the flag

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hout retraining, while depth and width pruning followed by distillation produced a 6B parameter student model from a Qwen3-8B teacher Next Steps Explore the NVIDIA Model Optimizer GitHub repository to begin applying quantization, pruning, and distillation to your models; Review the Hugging Face ModelOpt quantization guide for step-by-step post-training quantization examples; Read the model quantization blog post for a deeper dive into post-training quantization with Model Optimizer The surge in

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ur agent can read its own traces back to you; Pydantic Evals is the next thing you want once the agent is choosing its own sources, so a prompt change that sharpens one question does not blunt another If you build something good with this, we would like to see it. The harness lives on GitHub, the issues are open, and the capability shelf grows mostly because people tell us what is missing from it. Come say hello in the Pydantic Slack. Together, these are pieces of the Pydantic Stack. Try it out!

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-shot time series foundation models, that obtains state-of-the-art multivariate and univariate forecasting performance on multiple public benchmarks. TimesFM-3 is now available on GitHub and Hugging Face , with its BigQuery integration landing in the coming weeks. In the meantime, you can try TimesFM-2.5 immediately on your univariate tasks to familiarize yourself with the AI.FORECAST command in BigQuery - no ML expertise required. This project is joint work with Yichen Zhou, Petros Mol, Abhiman

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lacing the same C1HCX1 β-strands at the WD40 velcro-closure position, reproducing the observation from Han, Tsenkov, Venanzi et al. Next Steps Explore the BioNeMo Agent Toolkit on GitHub to apply this workflow to other monomers and complexes. Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine whic

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e-orders for Microduck, a $399 bipedal robot that walks, carries objects in its beak, falls over, and picks itself back up — with its entire reinforcement-learning stack posted on GitHub. The details: Built by Hugging Face’s France-based Pollen Robotics team, Microduck packs 15 motors, a camera, LiDAR, two motion sensors, microphones, and a speaker. Out of the box, it can perform seven behaviors, including recovering from falls and picking things up; optional wheels let it roller-skate. The SDK,

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re reading this, please make sure your shit is up to date. Install the latest version of our ClusterMAX CLI by running pip install clustermax on your machine, or clone the repo on GitHub. Then run our convenience script: cmax audit security for a free report from us on your cluster or standalone GPU machine. The cmax CLI will auto-detect Slurm clusters, Kubernetes clusters, standalone VMs, bare-metal machines, and containers, and compare them against a set of baseline software versions with know

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ive. It took about 20 minutes for the domain change to settle, which is normal when a website starts pointing somewhere new. I checked whether here.now needed the project to be on GitHub. It didn’t. The agent published the files directly from the local project folder to here.now. It creates a site, uploads the local files and then makes that version live. I still used Git for version history. A commit = a saved version. But if you ‘push’ those commits, they get sent to your GitHub repo. Useful f

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on the Open ASR Leaderboard and the Hugging Face team will run the evaluation. As before, the process for adding a model to the leaderboard takes place on the Open ASR Leaderboard GitHub : Open a pull request, a model checklist will appear. As before, you should report your results on the public datasets. We will verify the results on the public sets and compute the metrics on the private ones. Confirm the results we've obtained. Indian-English joins the main leaderboard as Voice Arena Monsoon ,

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wnership, develop a hypothesis, produce an RCA, and recommend a path forward. The useful version is always company-specific. One organization investigates through Datadog, Sentry, GitHub, and Slack. Another relies on Grafana, Jira, GitLab, internal CLIs, and runbooks stored with the code. Escalation rules, service boundaries, approval policies, and the meaning of a healthy system vary too. Advisor starts with a proven operational design, then adapts the expert to those realities. The expert can

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Middle: How Language Models Use Long Contexts, TACL 2023 / arXiv:2307.03172; Thariq Shihipar — The New Rules of Context Engineering for Claude 5 Generation Models, Claude, July 24, 2026; Boris Cherny and Diana Hu — Boris Cherny: Building Claude Code, Y Combinator Startup School, July 27, 2026; Xiaoyu Zhang (BBuf) — AI-Infra-Auto-Driven-SKILLS, GitHub; Tianyu Zhang, Yusong Gao, Yun Zhang — Pushing the Limits of Serving DeepSeek-V4-Pro on Compute-Constrained NVIDIA H20, LMSYS Org, August 19, 2026

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across code hosts, ticket trackers, and collaboration platforms. That includes workflows for code review, incident response, feedback triage, and large engineering projects across GitHub, GitLab, Slack, Microsoft Teams, Jira, and other surfaces. As our feature set and customer base expanded, feedback volume surged. Success had created a new bottleneck: the faster we shipped, the more time we spent supporting what we had already built. Reports arrive in a Slack feedback channel dedicated to our t

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nductive bias. In the pseudocode below, Conv2D has been aliased as ConvHex2D for illustrative purposes. To learn more about hexagonal convolutions, see the TensorFlow fork https://github.com/jglombitza/hexaconv of the HexaConv library https://github.com/ehoogeboom/hexaconv. Employing the hexagonal convolutions, the spatial model uses a densely-connected block inspired by DenseNet, where each layer's output is concatenated with all previous feature maps. This promotes feature reuse, preserving th

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