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ClickHouse Workload for Microsoft Fabric brings sub-second analytics to OneLake data through a native Fabric workload, with dedicated ClickHouse Cloud compute for agents, dashboards, and applications.
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction The advent of advanced programming tools, which harnesses the power of large language models (LLMs), has significantly enhanced programmer productivity and accuracy. Notwithstanding these advancements, dominant coding assistants like Github Copilot, built upon proprietary LLMs, pose notable challenges in terms of cost, privacy, security, and potential copyright infringement. Recognizing the imperative for a more transparent and accessible alternative, the open-source community has embarked on a concerted endeavor to develop open codeLLMs.
Article URL: https://github.com/ag3ntsc4rn/autorun-audit Comments URL: https://news.ycombinator.com/item?id=50374018 Points: 1 # Comments: 0
Article URL: https://github.com/AlgoVesta/mcp Comments URL: https://news.ycombinator.com/item?id=50375559 Points: 1 # Comments: 0
On October 28, 2026, GitHub released the official version of Copilot Fusion, simultaneously introducing an experimental local model routing feature. This capability enables automatic invocation of local models such as MAI Code 1.1 Flash on Windows devices and activates intelligent routing within VS Code, the CLI, and the Copilot App. The system automatically selects and combines cloud models—like those from OpenAI, Anthropic, and Google—based on three effort levels: Standard, High, and Max, achieving performance close to Claude Code while cutting costs in half during coding evaluations.
At Universe 2026, GitHub unveiled Copilot Fusion, an intelligent orchestration layer that automatically routes and combines models from OpenAI, Anthropic, Google, and others to complete coding tasks—eliminating the need for developers to manually select models. It integrates existing automatic model-selection technologies with Project HydraFusion, previewed in September. Once the model selector is enabled, the system automatically adopts execution modes such as single-model, cascading, or critique, based on task complexity.
GitHub Copilot Fusion is generally available. Instead of picking a model for each task, choose Copilot Fusion and let Copilot intelligently route and orchestrate which models it uses for you.… The post Route and orchestrate models with GitHub Copilot Fusion appeared first on The GitHub Blog.
Work doesn’t always fit in a single repository or session. A feature can span several repositories. A launch plan can pull in specs, designs, and assets without touching a repository… The post GitHub Copilot app, Universe releases appeared first on The GitHub Blog.
Enterprises working with a GitHub or Microsoft account team can now get Copilot included with GitHub Enterprise for every licensed user. Previously, you had to manage two things: how many… The post GitHub Enterprise now includes access to GitHub Copilot appeared first on The GitHub Blog.
You can now work through your repository’s GitHub code scanning alerts without leaving the GitHub Copilot app. The new GitHub security extension, now in public preview, lets you find the… The post Security extension for the GitHub Copilot app appeared first on The GitHub Blog.
Agents should understand more than your codebase—they should understand how your team works. Your preferences, repository conventions, skills, and custom instructions guide how changes should be made, while code in… The post GitHub intelligence puts your code, context, and history to work appeared first on The GitHub Blog.
Shared Copilot Automations are rolling out, enabling teams to build and maintain AI-powered workflows together. With Copilot Automations, you can combine prompts, triggers, and tools to design workflows around the… The post Build shared workflows for your team with Copilot Automations appeared first on The GitHub Blog.
Repository plugin defaults and enterprise-managed plugin controls are now generally available in VS Code, Copilot CLI, and the GitHub Copilot app. Trusted repositories can install and enable default plugins for… The post Repository plugin defaults and enterprise plugin controls generally available appeared first on The GitHub Blog.
You can now generate and edit images using natural language in GitHub Copilot. When you ask for an image, your selected reasoning model calls a dedicated image-generation tool powered by… The post Generate and edit images directly in Copilot appeared first on The GitHub Blog.
Today we are shipping intelligent routing for local models into experimental preview. GitHub Copilot Fusion can now choose between supported on-device models and cloud models, enabling intelligent local model routing… The post GitHub Copilot Fusion now supports selection of local models appeared first on The GitHub Blog.
GitHub Copilot now supports server-to-server authentication for GitHub Apps installed on organizations, available in public preview. Services and agents built with the GitHub Copilot SDK can now make requests on… The post GitHub Copilot supports server-to-server authentication appeared first on The GitHub Blog.
Assisted permissions are now generally available in GitHub Copilot CLI, GitHub Copilot app, and Visual Studio Code. When Copilot requests permission to run a command, access a file system path,… The post GitHub Copilot assisted permissions generally available appeared first on The GitHub Blog.
GPT-6.1 Terra, OpenAI’s newest model, is now generally available in GitHub Copilot. It is designed for everyday agentic coding and terminal work at a lower cost than GPT-6.1 Sol. In… The post GPT-6.1 Terra in GitHub Copilot appeared first on The GitHub Blog.
On October 25, 2026, Microsoft Research open-sourced Agensh, a multi-agent runtime framework that operates without a central coordinator, enabling 1 to 1,024 LLM-powered coding agents to self-organize and collaborate on ProgramBench. The system achieves decentralized coordination through shared dashboards inspired by Gitea, Mattermost, and DeLM, with all agents following the same prompt-driven five-step loop; its scalability has been validated in a Linux + Docker environment.
Why it matters：This achievement represents a significant evolution in multi-agent system architectures—abandoning the traditional “master‑agent” paradigm and enabling large‑scale self‑organization through prompt engineering and lightweight service composition. Empirical results on ProgramBench (e.g., 128 agents achieving a 49% improvement in task load balancing across five tasks) establish a new benchmark for distributed AI engineering. Released under the MIT License and compatible with mainstream single‑agent tools such as Copilot CLI, it offers valuable guidance for practical development.
AWS has announced that SageMaker AI’s serverless model customization feature now includes “Training Sessions,” which are fully available starting today. Users can independently define post-training logic—such as custom loss functions, reward signals, or multi‑round reinforcement learning—while the platform automatically handles GPU allocation, distributed execution, fault tolerance, and checkpoint storage.
