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or,” in support of which it is making more of the AI features open source, including Copilot Chat (client only). A side-effect is that VS Code has become a promotional vehicle for GitHub Copilot. Some might prefer the team to work on decoupling the IDE from the AI features, removing bloat for those not using Copilot. When you just need to build a tiny desktop app: TinyJS October 13, 2026 Valen creator drives 'Golden Spike' to connect new languages with Rust September 26, 2026 Microsoft agentical

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ct”: top tier researchers join the adventure, bringing credibility and momentum to the project. It’s already working with recent high-profile hires including: Nat Friedman, former GitHub CEO; Alex Wang, former Scale AI CEO; Daniel Gross, who was the CEO and co-founder of SSI, Ilya Sustkever’s startup The recruiting pitch is powerful: unrivaled compute per researcher, a shot at building the best open-source model family, and access to over 2 billion Daily Active Users. The offers that generally r

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un in parallel workspaces to replicate a similar effect.. These agents can appear in many forms, including web apps, mobile apps, and integrations within popular tools like Slack, GitHub, Linear, and Jira. While a human paired with an AI assistant can achieve more than any AI alone, an autonomous agent's ability to handle tasks end to end allows for a new level of multi-tasking, turning every engineer into an engineering manager. Adapting to working effectively alongside these new AI colleagues

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JSON configuration files Dependency management : Users must resolve package conflicts and version mismatches No discovery mechanism : Finding useful MCP servers requires searching GitHub Update complexity : Keeping servers current means manual reinstallation These friction points meant that MCP servers, despite their power, remained largely inaccessible to non-technical users. Introducing Desktop Extensions Desktop Extensions ( .mcpb files) solve these problems by bundling an entire MCP server—i

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how this threat applies to your use case and apply a patch as needed. Anthropic’s Slack MCP Server When Anthropic introduced MCP they published reference server implementations on Github. One of the reference servers is a Slack MCP Server that is vulnerable to “link unfurling”. We previously described this vulnerability generically for AI agents that post to messaging apps. At a glance it allows an AI agent that posts to Slack or other messaging applications to leak data to third-party servers.

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6891 (2025); [6] Akhiad Bercovich et al. "Llama-Nemotron: Efficient Reasoning Models." arXiv preprint arXiv:2505.00949 (2025). https://arxiv.org/abs/2505.00949; [7] Hugging Face. "GitHub - huggingface/open-r1: Fully open reproduction of DeepSeek-R1." 2025, https://github.com/huggingface/open-r1; [8] Etash Guha et al. "OpenThoughts: Data Recipes for Reasoning Models." arXiv preprint arXiv:2506.04178 (2025); [9] Dakuan Lu et al. "SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized

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es, document styles, and content formats. For this evaluation, we used a curated dataset of articles covering a range of real-world formats and content types. The dataset included GitHub Markdown files, scanned PDFs that were digitized into Markdown using Vision-Language Models (VLMs), documents containing mathematical equations written in LaTeX, and chemistry documents featuring complex chemical notations. It also incorporated code files with embedded comments and documentation, as well as web

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both the Minion and MinionS protocols. To start it, run: A browser window will open. Select your Remote Provider to be “Secure”. Set the Secure Endpoint URL to http://20.57.33.122:5056. Set your local client to Ollama and select the model you would like to use. Example code To run the secure protocol programmatically in Python, the minions package can be used. First create a file named example.py and add the following contents: Run the example: Minions GitHub repository; Hazy Research blog post

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s truncated for brevity. Python library Please update to the latest Ollama Python library. Example of enabling thinking Please visit the Ollama Python library for more information about its usage. More examples are available. JavaScript library Please update to the latest Ollama JavaScript library. Example of enabling thinking Example of streaming responses with thinking Please visit the Ollama JavaScript library for more information about its usage. More examples are available. Reference GitHub

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tegories. The details of the benchmarks corresponding to each category can be found in the table in the “Benchmarks details” section. SWE-Bench is based on a dataset of real-world GitHub issues and corresponding fixes, and is especially relevant for retrieval-augmented generation for coding agents. Text2Code (GitHub) contains benchmarks relevant for giving context for code completion or edition. We believe that these two categories are especially relevant to code assistants. Codestral Embed is o

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impactful ways. Explore the diverse applications of Mistral’s Agents API across various sectors: An agentic workflow built with Mistral's agents API where an agent interacts with Github and oversees a developer agent, powered by DevStral to write code. The agent is granted full authority over Github, showcasing automated software development task management. Read our cookbook An intelligent task coordination assistant powered by our Agents API, using multi-server MCP architecture to transform c

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improve voice processing while keeping things efficient. Start by ensuring you have a C compiler and the necessary audio libraries on your system. Clone the LPCNet repository from GitHub, compile the library and tools using standard build commands, and use the lpcnet demo tool to encode and decode audio files. For integration, include the necessary header files and link against the LPCNet library. The process involves initializing LPCNet, processing audio frames through encoding functions, and p

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ying relationships between disparate components, and identifying subtle bugs in intricate functions. Devstral is designed to tackle this problem. Devstral is trained to solve real GitHub issues; it runs over code agent scaffolds such as OpenHands or SWE-Agent, which define the interface between the model and the test cases. Here, we show Devstral’s performance on the popular SWE-Bench Verified benchmark, a dataset of 500 real-world GitHub issues which have been manually screened for correctness.

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tion function or worry about breaking other models—they can focus solely on their own model and its training. Examples of how some models are implemented are available on Ollama’s GitHub repository. Accuracy Large images produce large number of tokens which may exceed the batch size. Processing this correctly with the right positional information is challenging, specifically when a single image crosses boundaries. Ollama adds metadata as it processes images to help improve accuracy. Some example

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uild & test steps, conventions, and avoiding common mistakes. Manual » Amp will offer to generate this file by reading your project and other agents' files (.cursorrules, .cursor/rules, .windsurfrules, .clinerules, CLAUDE.md, and .github/copilot-instructions.md). We chose AGENT.md as a naming standard to avoid the proliferation of agent-specific files in your repositories. We hope other agents will follow this convention. Multiple AGENT.md Files Use multiple AGENT.md files across directory trees

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control. I provided feedback to vendors and via my blog that the user should be able to inspect exactly what information is being sent to a tool - most vendors are doing that now. GitHub Copilot Agent Mode, interestingly, also allows to modify the data being sent. Anyhow, let’s focus on a novel new trick. Sneaky Instructions in Tool Metadata If you follow my blog you know about hidden prompt injections and ASCII Smuggling. You will also recall that Anthropic did not see hidden prompt injection a

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k FLOPs per byte that we can expect from our hardware. For example, for a H100 the accelerator intensity is 989e12/3.35e12≈295989e^{12} / 3.35e^{12} \approx 295989e12/3.35e12≈295 (GitHub Gist). By simple re-arrangement we get that we are in the compute-bound regime for operation intensities that exceed our accelerator intensity. Example: Dot Product Let’s look at a simple example: Consider the dot product (∘\circ∘) between two nnn-dimensional vectors u,v∈Rnu, v \in \mathbb{R}^nu,v∈Rn defined as

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also reimplement from scratch HNSWs (you can see my implementation in hnsw.c), because that was going to be my core data structure, and I didn’t want to grab some random code from GitHub and be happy with it. However, as I started reading the papers, I started to understand that a few pieces were missing. So, as I did in the past with HyperLogLog, where I had to fill a few gaps (here: https://antirez.com/news/75 ), there was already some new algorithmic challenges. Especially I wanted two things

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nted with automatic retries with exponential backoff; Timeouts - The SDK defaults to a 60 second timeout. You can configure this with a timeout option at the client or request level; Custom Client — You can override the httpx client to customize it for your use-case. Some common use-cases include support for proxies and transports What’s next? We can’t wait to see what you build with the Cartesia Python SDK! Your feedback helps us improve—let us know your thoughts by submitting issues on GitHub.

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iv preprint arXiv:2502.03461. [link]; Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. (2023). Swe-bench: Can language models resolve real-world github issues? arXiv preprint arXiv:2310.06770. [link]; Chowdhury, N., Aung, J., Shern, C. J., Jaffe, O., Sherburn, D., Starace, G., Mays, E., Dias, R., Aljubeh, M., Glaese, M., Jimenez, C. E., Yang, J., Liu, K., & Madry, A. (2024). Introducing swe-bench verified. OpenAI. [link]; Dunlap, L., Mandal, K., Darrell, T., St

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