semvec/pypi-packages huggingface dataset:
lifejacket 1.3.0
Consistent standard errors for longitudinal data collected under pooling online decision policies.
Requires Python: >=3.10

```python
  _ _  __     _            _        _
 | (_)/ _|   (_)          | |      | |
 | |_| |_ ___ _  __ _  ___| | _____| |_
 | | |  _/ _ \ |/ _` |/ __| |/ / _ \ __|
 | | | ||  __/ | (_| | (__|   <  __/ |_
 |_|_|_| \___| |\__,_|\___|_|\_\___|\__|
            _/ |
           |__/
```

Save your standard errors from pooling in online decision-making algorithms.

## Setup (if not using conda)
### Create and activate a virtual environment
- `python3 -m venv .venv; source /.venv/bin/activate`

### Adding a package
- Add to `requirements.txt` with a specific version or no version if you want the latest stable
- Run `pip freeze > requirements.txt` to lock the versions of your package and all its subpackages

## Running the code
- `export PYTHONPATH to the absolute path of this repository on your computer
- `./run_local_synthetic.sh`, which outputs to `simulated_data/` by default. See all the possible flags to be toggled in the script code.

## Linting/Formatting

## Testing
python -m pytest
python -m pytest tests/unit_tests
python -m pytest tests/integration_tests



## TODO
1. Add precommit hooks (pip freeze, linting, formatting)
iflow-mcp_vmlia-books-mcp-server 0.1.0
Add your description here
Requires Python: >=3.10

## Get the Project and Initialize
```
git clone https://github.com/VmLia/books-mcp-server.git
cd books-mcp-server
uv venv
```
if macbook or linux
````
source .venv/bin/activate
````
if windows
````
.venv\Scripts\activate.bat
````

### Install Python Packages
```
uv add "mcp[cli]" httpx openai beautifulsoup4 lxml 
```
If the network is slow, you can set up a domestic mirror source.
```
uv add "mcp[cli]" httpx openai beautifulsoup4 lxml --index-url https://pypi.tuna.tsinghua.edu.cn/simple
```

## Example of Using cherry-studio
**Method 1**: On the setting page of cherry-studio, click on the MCP server, then click "Add Server", and subsequently configure it on the page.
### Type
```
STDIO
```
### Command
```
uv
```
### Parameters
```
--directory
# your project dir
run
main.py
```

**Method 2**: Use the configuration parameters
```
{
  "mcpServers": {
    "books-mcp-server": {
      "name": "books-mcp",
      "type": "stdio",
      "description": "",
      "isActive": true,
      "registryUrl": "",
      "command": "uv",
      "args": [
        "--directory",
        "/Enter your local project directory/books-mcp-server",
        "run",
        "main.py"
      ]
    }
  }
}
```
cosmos-rl 0.4.0
Cosmos-RL is a flexible and scalable Reinforcement Learning framework specialized for Physical AI applications.
Requires Python: >=3.10

<p align="center">
    <img src="https://raw.githubusercontent.com/nvidia-cosmos/cosmos-rl/main/assets/nvidia-cosmos-header.png" alt="NVIDIA Cosmos Header">
</p>


## Getting Started

Cosmos-RL is a flexible and scalable Reinforcement Learning framework specialized for Physical AI applications.

[Documentation](https://nvidia-cosmos.github.io/cosmos-rl).

## System Architecture
Cosmos-RL provides toolchain to enable large scale RL training workload with following features:
1. **Parallelism**
    - Tensor Parallelism
    - Sequence Parallelism
    - Context Parallelism
    - FSDP Parallelism
    - Pipeline Parallelism
2. **Fully asynchronous (replicas specialization)**
    - Policy (Consumer): Replicas of training instances
    - Rollout (Producer): Replicas of generation engines
    - Low-precision training (FP8) and rollout (FP8 & FP4) support
3. **Single-Controller Architecture**
    - Efficient messaging system (e.g., `weight-sync`, `rollout`, `evaluate`) to coordinate policy and rollout replicas
    - Dynamic NCCL Process Groups for on-the-fly GPU [un]registration to enable fault-tolerant and elastic large-scale RL training

![Policy-Rollout-Controller Decoupled Architecture](https://raw.githubusercontent.com/nvidia-cosmos/cosmos-rl/main/assets/rl_infra.svg)

## License and Contact

This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

NVIDIA Cosmos source code is released under the [Apache 2 License](https://www.apache.org/licenses/LICENSE-2.0).

NVIDIA Cosmos models are released under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). For a custom license, please contact [cosmos-license@nvidia.com](mailto:cosmos-license@nvidia.com).
iflow-mcp_mrnugget-tailscale-mcp 0.1.0
Add your description here
Requires Python: >=3.13

# tailscale-mcp

Super small MCP server that allows Claude to query Tailscale status by running
the `tailscale` CLI on macOS.

VERY DRAFTY!

## Requirements

- Python
- Tailscale installed at `/Applications/Tailscale.app/Contents/MacOS/Tailscale`
- [uv](https://github.com/astral/uv) for dependency management

## Running the Server

### STDIO Transport (Default)

Run the server with stdio transport (default):
```bash
python tailscale.py
```

### HTTP/SSE Transport

Run the server with HTTP transport on a specific port:
```bash
python tailscale.py --transport http --port 4001
```