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Facebook; Twitter; LinkedIn; Mail Today, we're thrilled to announce the 1.0 general availability release of the Agent Development Kit (ADK) for Kotlin! Check out the GitHub repository to dive into the code and build your first agent today, and explore the documentation. When we introduced ADK for Kotlin 0.1.0, our mission was to bring idiomatic, lightweight, and composable AI agent development to Kotlin, Java, and Android developers. Over the past months, we've worked to evolve the framework int

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ery little publicly available information. In one particularly tricky case, it exploited a security flaw that enabled it to extract the email address associated with that person’s GitHub account. As Roose interviewed people and disappeared down research rabbit holes, he threw transcripts, peer-reviewed papers, articles, sections of books, and more into a set of queryable notebooks that served as his “outboard brain.” Later, he used those notebooks and his working draft to prepare for interviews.

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enWebText. This is not surprising since the Pile’s main improvement was data corpus diversity over filtering. It has a curated 22-source mixture including PubMed and arXiv papers, GitHub code, legal opinions, patents, and parliamentary proceedings. The amount of cross-domain transfer to OLMES (which is English web-prose MCQ) might be minimal for many of these tokens, thus resulting in lower compute efficiency. We note that by virtue of its larger size, we expect that the Pile should eventually b

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pic’s two largest customers represented $1.2 billion of annualized run rate (30% of its $4 billion run rate at the time), with investors believing they were Cursor and Microsoft’s GitHub Copilot, the latter of which moved to token-based billing in June 2026. The problem is both that Anthropic and OpenAI’s largest customers cannot afford to pay them and that they desperately need them to keep paying them more every quarter, which means that every single AI startup will need to raise more and more

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your chosen design and open it in its browser, then add an event, reload, and switch users to test whether it saves and shares correctly Pro tip: Ask Astra to find five skills on GitHub that could improve your app’s design, then choose which ones to install and use. The Rundown: Somewhere in your team, a spreadsheet is doing the job of software. A request is stuck three replies deep in an inbox. None of it is technically broken, but it works because you remember how it works. That's not a proce

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y collude during training runs to remember resources on the internet which might come in handy in the future? I should clarify that I have done experiments like this before. Typically they do not run this long and the agent leaves behind a maybe imperfect but still digestible piece of software.↩ Content licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. Contact me via mail, bluesky, x, or github. You can sponsor me on github. Color scheme: auto, light, dark.

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Was it because we shared ideas on Twitter and other communities that inspired us? Or is it all unrelated? There is something powerful and strange about how LLMs diffuse knowledge and capabilities, while perhaps also nudging us all simultaniously and independently toward building the same things. Content licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. Contact me via mail, bluesky, x, or github. You can sponsor me on github. Color scheme: auto, light, dark.

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ions. Next Steps Review the Memory-Driven Chief of Staff recipe to adapt the memory design for your own NemoClaw example. Read the design proposal in the NVIDIA/nemoclaw-community GitHub repo for deeper architectural details. Learn more about NVIDIA NemoClaw and NVIDIA OpenShell to explore the underlying frameworks. Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more Enterprise work spans messages, decisions, projects,

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ns from hundreds of mathematicians, many working with the Lean FRO . We thank Kevin Buzzard for reviewing the proof and for his comments. Learn more The full proof is available on GitHub along with a written walk-through of the proof. Recommended expository reading The Proof in the Code is a recent book about the history of the Lean theorem prover and the formalization of mathematics. The 1996 “Fermat’s Last Theorem” BBC documentary has interviews with Wiles and other mathematicians involved in

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duced those numbers. toki is also clear about scope, noting that MCP drove the finishing pass, not the original shot generation. Watch → youtube.com/watch?v=Rv5HOgCac-w Workflow → github.com/tokimwc/every-sound-leaves-a-mark Follow → @toki mwc Prize: RTX 5060 Ti SonderSaid didn’t just build a workflow, they built the tooling around it. The entry runs on custom nodes of their own design, wired into a reference-driven H3 pipeline that scored a clean 15/15 on novelty, workflow quality, and communit

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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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