Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An agentic Machine Learning Engineer
| Date | Stars |
|---|---|
| 2026-07-31 | 1519 |
| 2026-08-06 | 1525 |
Today
+6 stars today
This week
— stars this week
This month
— stars this month
Momentum
9.0
growth rate 0.00%/day
# Karpathy > **Note:** For more advanced capabilities and end-to-end machine learning, visit [www.k-dense.ai](https://www.k-dense.ai). [](https://opensource.org/licenses/MIT) [](https://github.com/K-Dense-AI/karpathy/pulls) [](https://github.com/K-Dense-AI/scientific-agent-skills) [](https://agentskills.io/) [](#quick-start) [](https://x.com/k_dense_ai) [](https://www.linkedin.com/company/k-dense-inc) [](https://www.youtube.com/@K-Dense-Inc) > **Stay up to date:** Follow K-Dense on [X](https://x.com/k_dense_ai), [LinkedIn](https://www.linkedin.com/company/k-dense-inc), and [YouTube](https://www.youtube.com/@K-Dense-Inc) for new skills, release announcements, walkthroughs, research workflow demos, and examples you can use with your own AI agent. > ⭐ **Help make AI for science easier to discover:** If [Scientific Agent Skills](https://github.com/K-Dense-AI/scientific-agent-skills) saves you time, teaches your agent a workflow, or helps your lab move faster, please [star that repository](https://github.com/K-Dense-AI/scientific-agent-skills). A star is a public signal that these open, reusable research skills are worth maintaining: it helps scientists, engineers, and open-source contributors find the project, shows which agent-skill standards are gaining real adoption, and gives us a clear reason to keep expanding the collection for the community. An agentic Machine Learning Engineer that trains state-of-the-art ML models using the Claude Agent SDK and Google ADK. This is a simple implementation demonstrating the power of Scientific Agent Skills for machine learning. ## Prerequisites - Python 3.13 or higher - [uv](https://github.com/astral-sh/uv) package manager - Claude Code installed and authenticated (see the [Claude Code documentation](https://docs.claude.com/en/docs/claude-code/overview)) ## Setup ### 1. Clone the Repository ```bash git clone https://github.com/K-Dense-AI/karpathy.git cd karpathy ``` ### 2. Install Dependencies Install dependencies using `uv`: ```bash uv sync ``` ### 3. Environment Variables Create a `.env` file in the `karpathy` directory with your API keys: ```bash OPENROUTER_API_KEY=your_openrouter_api_key_here AGENT_MODEL=your_model_name_here ``` The `OPENROUTER_API_KEY` is required for the agent to function properly. This is the same environment variable that will be copied to the `sandbox` directory so the agents can use any API keys you provide here. ## Quick Start Run the startup script to set up the sandbox and start the ADK web interface: ```bash python start.py ``` This automatically: 1. Creates a `sandbox` directory with skills from Scientific Agent Skills 2. Sets up a Python virtual environment with ML packages (PyTorch, transformers, scikit-learn, etc.) 3. Copies your `.env` file to the sandbox 4. Starts the ADK web interface 5. Navigate to **http://localhost:8000** in your browser 6. Select `karpathy` in the top left under 'Select an agent' 7. All outputs will be in the `sandbox` directory so continue to monitor that as you converse with the agent **Note:** Any files you want the agent to use (datasets, scripts, etc.) should be manually added to the `sandbox` directory. ## Scientific Agent Skills This repository is designed to work with **[Scientific Agent Skills](https://github.com/K-Dense-AI/scientific-
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4fe54a41e30002ef, topic:agentic-ai, desc:agentic
