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te: does the code run and do the tests pass? Two widely used coding agent benchmarks, SWE-bench Verified and Terminal-Bench , follow this approach. SWE-bench Verified gives agents GitHub issues from popular Python repositories and grades solutions by running the test suite; a solution passes only if it fixes the failing tests without breaking existing ones. LLMs have progressed from 40% to 80% on this eval in just one year. Terminal-Bench takes a different track: it tests end-to-end technical ta

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athering data for Deep Research tasks by walking open knowledge graphs to create complex questions and verifiable answers, then obfuscating the questions via LLM re-formulation. A GitHub repo and a HuggingFace dataset exist. The version of DeepDive used for these experiments; deepdive-rlm on the Environments Hub Some examples: In 2011, a study emerged from a Sicilian university department focused on pharmaceutical sciences, delving into the physical form of crystals. This work specifically exami

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e it works really well for people who want to get into LLMs. And for readers interested in more advanced material, as a follow-up, I added substantial bonus material to the book’s GitHub repository over the course of the year. I plan to continue expanding these materials over time. In addition, as you may know, I am currently working on a sequel, Build A Reasoning Model (From Scratch). The first book, Build A Large Language Model (From Scratch), focuses on the core large language model architect

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design, it must be clear where the final decision-making authority lies. When AI suggestions function as the “default,” human critical review and active judgment may be weakened. GitHub's code autocompletion, which provides UX cues such as “continue,” “keep,” or “discard” that signal users to “pause and choose,” is a prime example of explicitly creating decision points for human judgment intervention Beyond a tool, to a colleague who extends our judgment AI should be designed not to solve every

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ime in the hospital and then needed quite a bit of time to recover, which meant I didn't have the energy outside of family stuff to do anything really. That explains the gap in my GitHub contributions. Anyways, with all of that out of the way, here we go. Let's start with the one project I'm most proud of. Boxie Back in summer of 2024 I decided to learn electronics so I can build gadgets for my little boy. This culminated in Boxie, an always offline audio player for our then three year old. I've

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k July 26, 2026 Zig creator calls Bun's Claude Rust rewrite 'unreviewed slop' July 15, 2026 HTTP gets a QUERY method so complex searches can stop pretending to be POST July 14, 2026 Speedier type checks in TypeScript 7.0 as first stable Go release ships July 10, 2026 GitHub cuts short offer to burn repos on CD after mockery ensues A purported jab of Sony's physical media phase-out blows up on GitHub itself July 7, 2026 .NET's long term support is not long term enough, dev complains June 30, 2026

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d summarizes them with a local LLaMA model (via Ollama), so you can see what each paper is about without guessing from the filename. You can set it to auto-keep, auto-skip, or auto-archive papers based on your interests, and it emails you a digest every day or week. Everything runs locally, so your library stays private. If your "Read Later" folder has become a PDF graveyard, this might help. Check it out on GitHub. Applying to PhD programs, or thinking about it? I mentor a cohort each cycle.$/$

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k July 26, 2026 Zig creator calls Bun's Claude Rust rewrite 'unreviewed slop' July 15, 2026 HTTP gets a QUERY method so complex searches can stop pretending to be POST July 14, 2026 Speedier type checks in TypeScript 7.0 as first stable Go release ships July 10, 2026 GitHub cuts short offer to burn repos on CD after mockery ensues A purported jab of Sony's physical media phase-out blows up on GitHub itself July 7, 2026 .NET's long term support is not long term enough, dev complains June 30, 2026

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editor, head over to the ACP documentation for detailed setup instructions for your editor of choice. And if you're interested in how OpenHands works under the hood, check out our GitHub repository or join our Slack community to chat with the team and other developers using OpenHands in their daily workflows. What Is An AI Dark Factory? What Lights-Out Manufacturing Teaches Us About Software Insights and updates from the OpenHands team Thank you for your submission! OpenHands is the foundation f

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following the best practices defined for Mistral Vibe CLI . We’re excited to see what you will build with Devstral 2, Devstral Small 2, and Vibe CLI! Share your projects, questions, or discoveries with us on X/Twitter , Discord , or GitHub . If you’re interested in shaping open-source research and building world-class interfaces that bring truly open, frontier AI to users, we welcome you to apply to join our team . Devstral 2 Open-weights agentic coding model for autonomous software engineering.

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in models) exploit systems. The same cultural drift enables exploitation! And we already see agents make mistakes in day to day usage, like formatting hard drives, creating random GitHub issues, or wiping a production database. So, the signs are there. And it is inherently dangerous, not only because of attacks like indirect prompt injection, but also because these systems are trained on enormous, untrustworthy data sets from the Internet. Anthropic research recently showed that it takes only a

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Need a tailored solution? Contact our team to explore fine-tuning or enterprise-grade training. Share your projects, questions, or breakthroughs with us: Twitter/X , Discord , or GitHub . Alongside this launch, you can explore the full details of the Ministral 3 series architecture in our latest research paper here . We believe that the future of AI should be built on transparency, accessibility, and collective progress. With this release, we invite the world to explore, build, and innovate wit

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g to write his own coding agent harness and give it a name that's entirely un-Google-able, so there will never be any users. Which means there will also never be any issues on the GitHub issue tracker. How hard can it be? To make this work, I needed to build: pi-ai: A unified LLM API with multi-provider support (Anthropic, OpenAI, Google, xAI, Groq, Cerebras, OpenRouter, and any OpenAI-compatible endpoint), streaming, tool calling with TypeBox schemas, thinking/reasoning support, seamless cross-

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g, and evaluation. Technical Report: storage.googleapis.com/intellect-3-paper/INTELLECT 3 Technical Report.pdf; Model Weights: huggingface.co/PrimeIntellect/INTELLECT-3; PRIME-RL: github.com/PrimeIntellect-ai/prime-rl; Verifiers: github.com/PrimeIntellect-ai/verifiers; Environments: hub.primeintellect.ai Future Work We’re looking forward to extending this work to: Scaling Agentic RL: For the INTELLECT-3 checkpoint we release, rewards and evaluations continue to rise, and training remains stable.

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s the best frontier task planning and tool calling we’ve seen yet.Jeff Wang CEO Claude Opus 4.5 delivers high-quality code and excels at powering heavy-duty agentic workflows with GitHub Copilot. Early testing shows it surpasses internal coding benchmarks while cutting token usage in half, and is especially well-suited for tasks like code migration and code refactoring.Mario Rodriguez Chief Product Officer Claude Opus 4.5 beats Sonnet 4.5 and competition on our internal benchmarks, using fewer t

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An IDE assistant that integrates git operations, file manipulation, package managers, testing frameworks, and deployment pipelines. An operations coordinator that connects Slack, GitHub, Google Drive, Jira, company databases, and dozens of MCP servers simultaneously. To build effective agents , they need to work with unlimited tool libraries without stuffing every definition into context upfront. Our blog article on using code execution with MCP discussed how tool results and definitions can so

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, and specialized partner models into Azure AI Foundry to extend model choice in every direction. Integrating Nvidia libraries into Microsoft platforms (Foundry, Fabric, Discover, GitHub, Azure, and others) empowers AI customers with the best-in-class offerings and capabilities to build and customize agentic AI apps and workloads like never before. Accelerating AI Innovation The availability of Nvidia libraries through Microsoft isn’t only about choice – it’s about speed. Customers can quickly p

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language interface enables anyone to define the goals and guardrails of your customer experience with sufficient nuance to build a highly effective agent. Workspaces brings safe, GitHub-style collaboration across CX, ops, and engineering. And Integrations let you connect systems in minutes — so you can build and deploy with confidence. Read here for more. Insights 2.0 Insights is your hub for optimizing agent performance. The new Explorer feature (think deep research for customer conversations)

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clusion Building these tools is ridiculously easy, gives you all the freedom you need, and makes you, your agent, and your token usage efficient. You can find the browser tools on GitHub. This general principle can apply to any kind of harness that has some kind of code execution environment. Think outside the MCP box and you'll find that this is much more powerful than the more rigid structure you have to follow with MCP. With great power comes great responsibility though. You will have to come

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ing without the hallucination or instability of larger LLMs, and does so at a fraction of the cost. Marker, along with Datalab’s other open-source tools, have earned 48k+ stars on GitHub and are trusted by researchers, startups, and enterprise teams alike. Modal already powers Datalab’s hosted platform, enabling them to deliver reliable, scalable model serving and roll out new releases quickly: Now, any builder or team can use Modal to instantly deploy Datalab’s state-of-the-art Marker pipeline

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