A Raspberry Pi project that displays the city and country where it is currently 5:00 p.m., updating every 15 minutes. It uses accurate timezone data and is a fun, lightweight build with potential for educational and practical applications.
This GitHub repository contains a collection of example files demonstrating various use cases and configurations for the llamafiles tools, including examples:
* **System Administration:** Scripts and configurations for Ubuntu, Raspberry Pi 5, and macOS.
* **LLM Interaction:** Examples of prompts and interactions with LLMs like Mixtral and Dolphin.
* **Text Processing:** Scripts for summarizing text, extracting information, and formatting output.
* **Development Tools:** Examples related to Git, Emacs, and other development tools.
* **Hardware Monitoring:** Scripts for monitoring GPU and NVMe drive status.
* **System Administration:** Scripts and configurations for Ubuntu, Raspberry Pi 5, and macOS.
* **LLM Interaction:** Examples of prompts and interactions with LLMs like Mixtral and Dolphin.
* **Text Processing:** Scripts for summarizing text, extracting information, and formatting output.
* **Development Tools:** Examples related to Git, Emacs, and other development tools.
* **Hardware Monitoring:** Scripts for monitoring GPU and NVMe drive status.
ASCVIT V1 aims to make data analysis easier by automating statistical calculations, visualizations, and interpretations.
Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.
- Accepts CSV or Excel files. Provides a data overview including summary statistics, variable types, and data points.
- Histograms, boxplots, pairplots, correlation matrices.
- t-tests, ANOVA, chi-square test.
- Linear, logistic, and multivariate regression.
- Time series analysis.
- k-means, hierarchical clustering, DBSCAN.
Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.
- Accepts CSV or Excel files. Provides a data overview including summary statistics, variable types, and data points.
- Histograms, boxplots, pairplots, correlation matrices.
- t-tests, ANOVA, chi-square test.
- Linear, logistic, and multivariate regression.
- Time series analysis.
- k-means, hierarchical clustering, DBSCAN.
Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
Mesop is a Python-based UI framework that allows you to rapidly build web apps like demos and internal apps. Easy to get started, fast iteration, and flexible & composable.
llama-cpp-python offers a web server which aims to act as a drop-in replacement for the OpenAI API. This allows you to use llama.cpp compatible models with any OpenAI compatible client (language libraries, services, etc).
The author has also automated their weeknotes by using an Observable notebook, which generates the "releases this week" and "TILs this week" sections.
The notebook fetches TILs from the author's Datasette, grabs releases from GitHub, and assembles a markdown string for the new post.
* `llm` CLI tool for running prompts against large language models
* Automation of weeknotes using an Observable notebook
* Notebook generates "releases this week" and "TILs this week" sections
* Tool stores prompts and responses in a SQLite database
The notebook fetches TILs from the author's Datasette, grabs releases from GitHub, and assembles a markdown string for the new post.
* `llm` CLI tool for running prompts against large language models
* Automation of weeknotes using an Observable notebook
* Notebook generates "releases this week" and "TILs this week" sections
* Tool stores prompts and responses in a SQLite database
