A list of 11 open source AI projects designed to help developers streamline their work, from training models to improving productivity and data management.
| Project Name | Description |
|----------------------|-----------------------------------------------------------------------------|
| Upscayl | Increases image resolution for enhanced detail, ideal for digital artwork. |
| Nyro | Automates mundane tasks like taking screenshots and resizing windows. |
| Geppetto | Enhances Slack documentation with help from LLMs and can request art from Dall-E. |
| E2B sandboxes | Allows LLMs to use web browsers, GitHub, and command-line tools for tasks like cloud management. |
| Dataline | Generates SQL commands to extract data and create data science reports locally. |
| Swirl Connect | Links standard databases with LLMs and RAG search indices for easier data access. |
| DSPy | Offers a systematic approach to LLM training by connecting modules and optimizers. |
| Guardrails | Integrates controls into generative AI pipelines to refine AI-generated answers and reduce errors. |
| Unsloth | Optimizes training of open-source models for faster and more accurate results. |
| Wren AI for SQL | Translates natural language questions into SQL queries, simplifying data retrieval. |
| AnythingLLM | Organizes digital documents and allows querying with any LLM or RAG system. |
| Project Name | Description |
|----------------------|-----------------------------------------------------------------------------|
| Upscayl | Increases image resolution for enhanced detail, ideal for digital artwork. |
| Nyro | Automates mundane tasks like taking screenshots and resizing windows. |
| Geppetto | Enhances Slack documentation with help from LLMs and can request art from Dall-E. |
| E2B sandboxes | Allows LLMs to use web browsers, GitHub, and command-line tools for tasks like cloud management. |
| Dataline | Generates SQL commands to extract data and create data science reports locally. |
| Swirl Connect | Links standard databases with LLMs and RAG search indices for easier data access. |
| DSPy | Offers a systematic approach to LLM training by connecting modules and optimizers. |
| Guardrails | Integrates controls into generative AI pipelines to refine AI-generated answers and reduce errors. |
| Unsloth | Optimizes training of open-source models for faster and more accurate results. |
| Wren AI for SQL | Translates natural language questions into SQL queries, simplifying data retrieval. |
| AnythingLLM | Organizes digital documents and allows querying with any LLM or RAG system. |
PocketPal AI is an application that brings language models directly to your phone, offering offline AI assistance and model flexibility for both iOS and Android devices.
This repository contains the Llama Stack API specifications as well as API Providers and Llama Stack Distributions. The Llama Stack aims to standardize the building blocks needed for generative AI applications across various development stages.
It includes API specifications and providers for the Llama Stack, which aims to standardize components needed for developing generative AI applications. The stack includes APIs for Inference, Safety, Memory, Agentic System, Evaluation, Post Training, Synthetic Data Generation, and Reward Scoring. Providers offer actual implementations for these APIs, either through open-source libraries or remote REST services.
It includes API specifications and providers for the Llama Stack, which aims to standardize components needed for developing generative AI applications. The stack includes APIs for Inference, Safety, Memory, Agentic System, Evaluation, Post Training, Synthetic Data Generation, and Reward Scoring. Providers offer actual implementations for these APIs, either through open-source libraries or remote REST services.
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.
A list of 13 open-source software for building and managing production-ready AI applications. The tools cover various aspects of AI development, including LLM tool integration, vector databases, RAG pipelines, model training and deployment, LLM routing, data pipelines, AI agent monitoring, LLM observability, and AI app development.
1. Composio - Seamless integration of tools with LLMs.
2. Weaviate - AI-native vector database for AI apps.
3. Haystack - Framework for building efficient RAG pipelines.
4. LitGPT - Pretrain, fine-tune, and deploy models at scale.
5. DsPy - Framework for programming LLMs.
6. Portkey's Gateway - Reliably route to 200+ LLMs with one API.
7. AirByte - Reliable and extensible open-source data pipeline.
8. AgentOps - Agents observability and monitoring.
9. ArizeAI's Phoenix - LLM observability and evaluation.
10. vLLM - Easy, fast, and cheap LLM serving for everyone.
11. Vercel AI SDK - Easily build AI-powered products.
12. LangGraph - Build language agents as graphs.
13. Taipy - Build AI apps in Python.
1. Composio - Seamless integration of tools with LLMs.
2. Weaviate - AI-native vector database for AI apps.
3. Haystack - Framework for building efficient RAG pipelines.
4. LitGPT - Pretrain, fine-tune, and deploy models at scale.
5. DsPy - Framework for programming LLMs.
6. Portkey's Gateway - Reliably route to 200+ LLMs with one API.
7. AirByte - Reliable and extensible open-source data pipeline.
8. AgentOps - Agents observability and monitoring.
9. ArizeAI's Phoenix - LLM observability and evaluation.
10. vLLM - Easy, fast, and cheap LLM serving for everyone.
11. Vercel AI SDK - Easily build AI-powered products.
12. LangGraph - Build language agents as graphs.
13. Taipy - Build AI apps in Python.
A ruby script calculates VRAM requirements for large language models (LLMs) based on model, bits per weight, and context length. It can determine required VRAM, maximum context length, or best bpw given available VRAM.
How to use Kubernetes to manage and streamline AI workflows, leveraging the power of open source tools and the Kubernetes AI Toolchain Operator.
Triplex is an open-source model that efficiently converts unstructured data into structured knowledge graphs at a fraction of the cost of existing methods. It outperforms GPT-4o in both cost and performance, making knowledge graph construction more accessible.
An extension that automatically unloads and reloads your model, freeing up VRAM for other programs.
Noema Research introduces Pinboard, a developer tool for improved productivity. Pinboard, a command-line tool, efficiently manages files and terminal references, enhancing development workflows. Key features include flexible pinning, contextual updates, clipboard integration, an interactive shell, and undo functionality.
