ModelScope is a machine-learning model community that aggregates advanced models from multiple fields. The platform provides tools for exploring models, trying inference, training, deployment, and application development in one service. Users can discover, learn about, customize, and share machine-learning models through ModelScope. The service also supports collaborative development of an open-source model community.

Seed is an open-source agent starter that gives a language model a single exec tool for running shell commands. Its system prompt is loaded from self/SELF.md, and the agent can edit the self/ directory to preserve tools, notes, and behavior across otherwise ephemeral sessions. Rather than receiving a prebuilt framework’s memory, skills, tools, and conventions, each agent develops them over time by writing files into that directory. Running uvx --from git+https://github.com/vivekhaldar/seed.git seed in a new directory copies seed.py and run_seed.sh without overwriting existing files, creates self/SELF.md, initializes a Git repository, and opens a REPL. Each directory becomes a distinct agent whose retained files and Git history reflect its own interactions. The local runner can reopen an existing agent and optionally select a model, including gemini-2.5-pro. Seed records verbatim session transcripts in self/sessions/*.json after every turn, but does not automatically load those transcripts at startup. Model access is managed by Simon Willison’s llm library; the documented default is openai-codex/gpt-5.6-sol using a ChatGPT login through the Codex CLI. Supported providers include OpenAI, Anthropic, Gemini, and OpenRouter.

WeatherNext is an AI model that provides accurate cyclone forecasts, offering an additional day of warning. The model has been open-sourced to enhance forecasting capabilities for tropical cyclones, which cause significant global fatalities and economic losses.

An advanced agentic harness is being developed to enhance AI capabilities for various applications. This harness aims to improve decision-making and task execution efficiency in AI systems.

The Little Book of Reinforcement Learning provides a concise introduction to reinforcement learning concepts and algorithms. The associated GitHub repository includes the book, Pytorch-based implementations of various algorithms in the algos/ folder, and detailed explanations in the supplementary/ folder.

Zhinit has developed a kick drum diffusion model that utilizes advanced training techniques to enhance audio generation. The model aims to produce high-quality kick drum sounds for music production.

Gemma 4, a 26-billion-parameter open-weights mixture-of-experts model, runs on a 13-year-old Xeon server without a GPU at a speed of approximately five tokens per second. The server is a repurposed HP StoreVirtual storage box originally designed for disk storage.

To become a hireable graphics programmer, knowledge in real-time rendering techniques, shader programming, and graphics APIs is essential. Current trends suggest a potential shift away from machine learning hype towards other technologies like quantum computing in the coming years.

The AI Compute Extensions (ACE) specification introduces x86 extensions designed to accelerate computation tasks, particularly for matrix multiplication kernels and reduced precision data formats relevant to machine learning workloads. ACE defines new matrix multiplication primitives that enhance AVX and scalar code with features like ACE register state, data processing operations utilizing AVX input, and data move operations for managing tile register state.

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