Project directory

Projects

Open-source AI projects tracked by the leaderboard, ordered by GitHub stars.

Python 28 stars

introlix/Swiftlet

SwiftLet is a lightweight Python framework for running open-source Large Language Models (LLMs) locally using safetensors

Overview: SwiftLet is a lightweight Python framework designed for local execution of open-source Large Language Models (LLMs) using safetensors. Key features: - Local execution of supported LLMs - Minimal dependencies, easy to understand - Clean architecture for adding more models - Designed for research, prototyping, and educational use - Open for contributions and experiments Tech stack: Python Why it matters: SwiftLet provides a simple and educational way to run and experiment with various LLMs locally, making it ideal for learning and research purposes.

Go 28 stars

johnpili/go-text-to-image

Generate text to image in Golang. I created this application for generating featured images for facebook or when sharing code snippet in WhatsApp.This comes with a simple web interface that lets you generate text to image.

Overview: This application converts text into images, suitable for media sharing on platforms like WhatsApp or social media. Key features: - Converts text to images - Web interface for generating featured images Tech stack: Go Why it matters: This tool simplifies the process of creating featured images for media sharing, making it easier to post code snippets or other text-based content on social media platforms. Repository: johnpili/go-text-to-image Primary language: Go GitHub stars: 28

Python 28 stars

kaiidams/voice100

Voice100 includes neural TTS/ASR models. Inference of Voice100 is low cost as its models are tiny and only depend on CNN without autoregression.

### Overview: Voice100 is a lightweight neural text-to-speech (TTS) and automatic speech recognition (ASR) model designed for efficient inference on various devices. It is particularly suitable for low-cost applications, such as smartphones and small embedded systems. ### Key Features: - **Tiny Models**: Voice100 is built with tiny models that do not require large datasets, ensuring compatibility with normal PCs, Raspberry Pi, and smartphones. - **CNN Dependencies**: The models are entirely dependent on CNNs, making them highly efficient and cost-effective. - **Inference Efficiency**: Inference is significantly faster compared to models that require complex autoregressive structures, making it ideal for real-time applications. ### Tech Stack: - **Python**: The primary programming language used for developing Voice100. ### Why It Matters: - **Efficiency**: Voice100's small size and reliance on CNNs make it highly efficient, suitable for running on low-power devices. - **Cost-Effectiveness**: The models are designed to be small, making them cost-effective for a wide range of applications, including those on smartphones and small devices. ### Repository: kaiidams/voice100 - **GitHub Stars**: 28 - **GitHub Description**: Voice100 includes neural TTS/ASR models. Inference of Voice100 is low cost as its models are tiny and only depend on CNN without autoregression. ### Sample Synthesis: - **English Sample Synthesis 1**: "beginnings are apt to be determinative and

Python 28 stars

kenny0312/videosense-agent

Ask your video library anything — an AI agent that watches, reasons in a tool-use loop, and answers with playable clips and charts.

### Overview: VideoSense is an AI agent designed to answer questions about your video library. It watches your videos, reasons about what it sees, and provides proof through clips and charts. The agent is built on Python and uses FastAPI for its backend, along with tools like Gemini 2.5, PostgreSQL, and Cloud Run for storage and processing. It's designed to be reliable and transparent, providing clear answers with detailed explanations and cost information. ### Key Features: - **Video Analysis**: Watches videos and reasons about what it sees. - **Answer Proofs**: Provides clips and charts to prove the answer. - **Interactive Demo**: A live demo showing a session and the answer. - **Detailed Explanation**: Answers are accompanied by timecodes, tool steps, and cost information. ### Tech Stack: - Python - FastAPI - Gemini 2.5 - PostgreSQL - Cloud Run - BigQuery ### Why It Matters: VideoSense offers a straightforward and reliable way to answer questions about your video library, providing proof and detailed explanations. It's built to be trusted and transparent, ensuring accurate and honest answers.

TypeScript 28 stars

kenryu42/pi-grok-cli

Use your X Premium or SuperGrok subscription in pi.

Overview: Pi-Grok-CLI integrates your X Premium or SuperGrok subscription into the Pi environment, providing a streamlined toolset for image generation and usage tracking. Key features: - OAuth login for seamless integration. - Support for multiple accounts and automatic quota management. - Detailed usage tracking and reset times. - Native image input capability. - Image generation directly or through the `image_gen` tool. Tech stack: TypeScript Why it matters: This tool simplifies the integration of premium Grok services into the Pi environment, making it easier to manage subscriptions and usage data efficiently. Repository: kenryu42/pi-grok-cli Primary language: TypeScript GitHub stars: 28

Python 28 stars

lt-asset/REPOCOD

For our ACL25 Paper: Can Language Models Replace Programmers? RepoCod Says ‘Not Yet’ - by Shanchao Liang and Yiran Hu and Nan Jiang and Lin Tan

### Overview: REPOCOD is a comprehensive code generation benchmark designed for evaluating language models in software development. It includes 980 problems from 11 popular real-world projects, focusing on tasks requiring file-level or repository-level context. REPOCOD features a long average canonical solution length and high average cyclomatic complexity, making it a valuable resource for assessing the capabilities of language models in real-world software development. ### Key Features: - 980 problems from 11 popular real-world projects - 58% of tasks requiring file-level or repository-level context - Longest average canonical solution length (331.6 tokens) - Highest average cyclomatic complexity (9.00) - 313.5 developer-written test cases per task for better correctness evaluation - Evaluation on ten LLMs, none achieving more than 30 pass@1 on REPOCOD ### Tech Stack: REPOCOD is built using Python, leveraging the Hugging Face dataset for tasks and evaluation. ### Why it Matters: REPOCOD highlights the necessity of developing stronger language models that can effectively assist developers in real-world software development, particularly in tasks requiring repository-level context information.

Python 28 stars

lugia19/Echo-XI

Speech to text to speech using Elevenlabs

### Overview: Echo-XI is a Python-based project that utilizes the ElevenLabs TTS service to convert speech to text and vice versa. It offers a user-friendly interface and supports multiple speech recognition and text-to-speech providers. ### Key Features: - **Speech to Text and Text to Speech**: Converts audio to text and vice versa using ElevenLabs TTS. - **GUI and CLI**: Provides a graphical user interface and a command-line interface for easy access. - **Sensitive Details**: Stores API keys in the system keyring for security. - **Translation**: Automatically translates speech to the user's preferred language. - **Multiple Providers**: Supports Vosk, Azure Speech Recognition, and Whisper (both locally and through OpenAI's API). - **OBS Synchronization**: Synchronizes detected text with OBS text sources. - **Translation Services**: Uses DeepL for supported languages and Google Translate for others. - **Supports Multiple Languages**: Compatible with various languages through ElevenLabs' multilingual model. ### Tech Stack: - Python ### Why it Matters: Echo-XI simplifies the process of converting speech to text and text to speech, making it accessible and user-friendly. Its integration with multiple speech recognition and text-to-speech providers ensures versatility and reliability. The ability to translate speech into the user's preferred language and synchronize with OBS adds value to the project, making it a valuable tool for developers and enthusiasts alike. ### Repository: - Repository: `https://github.com/lugia19/Echo-XI` - GitHub Stars: 28 -

TypeScript 28 stars

marceloeatworld/clopinette-ai

Cloudflare-native AI agent — 13 tools, codemode, 5-layer memory, self-learning, multimodal I/O. Telegram, Discord & WhatsApp bots. Web search, image gen, TTS, document RAG, calendar, task delegation. 100% serverless on Workers + Durable Objects.

Overview: ClopinetteAI is a Cloudflare-native AI agent featuring persistent memory, a skills hub, and built-in Telegram, Discord, and WhatsApp bots. It offers 13 tools, codemode, 5-layer memory, self-learning, and multimodal I/O capabilities. Key features: - Chat: Streamed WebSocket with Markdown, images, voice, and drag-and-drop upload. - Tool-driven runtime: Web, docs, memory, history, skills, notes, calendar, todo, image, TTS, and clarification. - 5-layer memory: MEMORY.md / USER.md, hybrid FTS5+Vectorize session search, memory flush, skills index, and optional Honcho. - Multi-platform: Telegram, WhatsApp, Discord, and MCP. - 14 personality presets: helpful, concise, technical, kawaii, pirate, shakespeare, noir, uwu, etc. - Skills Hub: Install skills from the built-in catalog or trusted GitHub repos. - Hub hardening: Scans skills for prompt-injection / exfiltration structure. - BYOK: Bring your own key across 13 providers. - BYOK media models: Image generation, TTS, and voice transcription. - Cross-provider auxiliary: e.g., primary OpenAI + auxiliary Anthropic. - Self-learning: Updates memory after N turns. - Async delegation: Web-only research sub-agents run in parallel, auto-resume. - Multimodal: Vision (images), Whisper (voice), PDF / DOCX / text extraction. Tech stack: TypeScript. Why it matters:

Jupyter Notebook 28 stars

microsoft/sql-ai-datathon

### Overview: This repository is designed to help developers prepare for building AI-powered applications with Microsoft SQL. It includes four missions that guide you through embedding, semantic/hybrid search, Data API Builder endpoints, and an RAG agent. Completing these missions will help you level up your skills and potentially win a ticket to SQLCon 2026. ### Key features: - Embeddings & Search - Retrieval Augmented Generation (RAG) - Orchestrate SQL + AI workflows in C# or Python - Building a Full-Stack AI Application in C# or Python - Open Hack Project Submission ### Tech stack: Jupyter Notebook ### Why it matters: This repository provides a comprehensive guide to building AI applications using SQL, helping developers prepare for future projects and potentially win a prize at SQLCon 2026. ### GitHub stars: 28 ### Repository: microsoft/sql-ai-datathon ### Primary language: Jupyter Notebook ### GitHub description: No description provided. ### Quick Start: 1. Fork this repo. 2. Clone, or open in Codespaces. 3. Complete all missions, 1-4. 4. Learn how to submit an entry for the Open Hack project in `open-hack.md`. 5. Submit your project by **February 26 at 11:59 PM PT** ### Mission Briefs: 1. Embeddings & Search 2. Retrieval Augmented Generation (RAG) 3. Orchestrate SQL + AI workflows in C

Jupyter Notebook 28 stars

ml6team/fondant-usecase-controlnet

Example Fondant pipeline preparing data to train a Controlnet model

Overview: This project demonstrates an end-to-end Fondant pipeline for preparing data to fine-tune a ControlNet model, specifically for interior design. Key features: - Prompt Generation - Image URL Retrieval - Download Images - Add Captions - Add Segmentation Maps Tech stack: Jupyter Notebook Why it matters: This pipeline provides a comprehensive solution for generating and processing data for fine-tuning a ControlNet model, focusing on interior design, allowing users to generate realistic images of their desired interior spaces.

Python 28 stars

naivestud/auricle

Streaming ASR, audio captioning and speech QA over a whisper-style encoder with pluggable LLM backends

Overview: Auricle is a streaming ASR, audio captioning, and speech QA toolkit built on a whisper-style encoder with pluggable LLM backends. Key features: - Whisper-style encoder - Streaming ASR - Audio captioning - Speech QA - Pluggable LLM backends - Evaluation suite - CLI Tech stack: Python Why it matters: Auricle provides a comprehensive solution for speech understanding, enabling real-time transcription, captioning, and question answering, making it valuable for applications requiring continuous speech processing.

JavaScript 28 stars

pixelpainter/comfyui-AI-prompt-editor

A full prompt-writing studio in a single ComfyUI node - AI editing, style targeting, prompt browsing, and wireless prompt injection, powered by your local Ollama models. No cloud, no keys.

Overview: Enhance Prompt Editor (EPE) is a full prompt-writing studio integrated into a single ComfyUI node, powered by local Ollama models, offering AI editing, style targeting, prompt browsing, and wireless prompt injection without cloud APIs or keys. Key features: - Enhanced prompt editor with up to 10 tabs for multi-tab editing - Send to tab feature to share results across tabs - What's in the prompt box is what renders, ensuring consistency - Editor: multi-tab prompt editor with undo/redo, find & replace, case/sort/dedupe/trim, cleanup, word wrap, and live word + token counts - Synonyms: suggests plain and creative alternatives for selected words - Flag words: highlights empty quality words and offers replacements Tech stack: JavaScript Why it matters: No cloud APIs, no keys, making it perfect for local workflows and security-conscious environments.

Rust 28 stars

pur4v/p2ptokens

Distributed, peer-to-peer, decentralized network for LLM inference — peers seed & leech completions, metered by BitTorrent-protocol-style upload/download ratios. OpenAI-compatible · Rust · libp2p.

Overview: P2P TOKENS is a decentralized network designed for LLM inference, enabling peers to seed and leech completions without a central authority. It uses a barter ratio model based on the BitTorrent protocol, ensuring honest behavior and trustless interactions. Key features: - Distributed, peer-to-peer LLM inference - Seed tokens, leech tokens, no middleman - OpenAI-compatible API - Built with Rust, libp2p, and licensed under MIT Tech stack: Rust, libp2p Why it matters: P2ptokens offers a decentralized and trustless solution for GPU inference, making it ideal for those looking to avoid middlemen and ensure fair and honest interactions in the decentralized computing space.

JavaScript 28 stars

qishuilalala/dsh-voice-mode

DSH 全双工语音插件：流式语音识别入草稿、语音合成按句朗读 + 实时字幕、开口即打断；本地识别免 API Key，可选唤醒词。 · Full-duplex voice plugin for DeepSeek Harness: streaming speech-to-text into an editable draft, text-to-speech read-aloud with live captions, true barge-in and wake word; on-device ASR, no API key.

### Overview: 在 DeepSeek Harness 会话中，只需轻触麦克风即可开始语音输入、编辑草稿并按句朗读，支持实时字幕和发声打断。识别在宿主端进行，无需额外语音 API Key。 ### Key features: - 🎤 实时出字（流式识别） - 🔊 按句朗读 - ⏸ 发声打断 ### Tech stack: JavaScript ### Why it matters: 无需额外语音 API Key，识别在宿主端本地推理，支持本地 VITS 和 Kokoro 中英混读，无需依赖额外服务。

Jupyter Notebook 28 stars

ratschlab/aestetik

AESTETIK: Convolutional autoencoder for learning spot representations from spatial transcriptomics and morphology data

### Overview: AESTETIK is a convolutional autoencoder model designed to learn accurate spot representations from spatial transcriptomics and morphology data. It integrates transcriptomics and morphology information on a spot level and topology on a neighborhood level, capturing biological complexity. ### Key Features: - **Spot Representation Learning**: AESTETIK learns accurate spot representations that capture biological complexity. - **Multi-Modal Integration**: It integrates spatial transcriptomics, morphology, and topology information to provide a comprehensive understanding of biological processes. - **Dimensionality Reduction**: The model uses dimensionality reduction techniques like PCA to preprocess the spot representations. - **Neighborhood Fusion**: The modality-specific representations are fused through concatenation, and the grid per spot is built as input for the autoencoder. ### Tech Stack: - **Python**: The primary programming language used for implementing AESTETIK. ### Why It Matters: AESTETIK offers a powerful tool for researchers to gain a multi-modal understanding of key biological processes through spatial transcriptomics, making it an essential resource for those working in the field of spatial transcriptomics and related disciplines.

Python 28 stars

redis-developer/LLM-Recommender

Use OpenAI, Redis, and streamlit to recommend hotels using Large Language Models

Overview: The LLM Hotel Recommender is a Streamlit app utilizing Redis and the OpenAI API to generate hotel recommendations based on user preferences. It allows users to search for hotels in the US by specifying state, city, positive qualities, and negative qualities. Key features: - Semantic search capabilities in Redis - Hypothetical Document Embeddings (HyDE) approach for generating fake reviews - Redis vector search for semantic similarity - Streamlit app for easy deployment Tech stack: Python Why it matters: This project showcases the integration of Redis, OpenAI, and Streamlit to create a powerful hotel recommendation system, leveraging semantic search and LLMs for personalized recommendations.

Python 28 stars

romilandc/streamlit-ollama-llm

A Streamlit user interface for local LLM implementation on Ollama. With just three python apps you can have a localized LLM to chat with. I'm running Ollama Windows (just updated) and DuckDuckGo browser and it's working great as a coding assistant.

Overview: This Streamlit project provides a user-friendly interface for localizing and using an LLM (Large Language Model) on Ollama, making it accessible for coding and everyday queries. Key features: - Streamlit webapp UI for local LLM implementation - Three Python libraries required: torch, streamlit, and ollama - Works with Ollama Windows and DuckDuckGo browser - Supports both GPU and CPU - Available models from Ollama library Tech stack: Python Why it matters: This project simplifies the process of integrating an LLM into a local environment, making it accessible for developers and users alike.

Go 28 stars

sgaunet/perplexity-go

A comprehensive Go client library for the Perplexity AI API with support for chat completions, async jobs, streaming, multimodal messages, structured outputs, and web search integration

### Overview: This Go library provides a comprehensive client for interacting with the Perplexity AI API, supporting chat completions, async jobs, streaming, multimodal messages, structured outputs, and web search integration. It simplifies the interaction with the Perplexity models and provides features like authentication, model management, and request validation. ### Key features: - **Chat Completion Requests**: Simple and easy-to-use interface for chat completion requests. - **Search API Support**: Direct access to Perplexity's real-time web index without the generative LLM layer. - **Model Support**: Handles all Perplexity models, including online LLMs. - **Authentication and Key Management**: Supports authentication and API key management. - **Request Validation**: Comprehensive request validation. - **Concurrent Requests**: Supports concurrent request handling with thread-safe operations. - **Context-Aware Handling**: Handles context-aware request handling with cancellation support. ### Tech stack: The library is built using Go, leveraging its simplicity and efficiency for rapid development and deployment. ### Why it matters: This library is essential for developers looking to integrate Perplexity AI capabilities into their projects, providing a robust and easy-to-use interface for interacting with the Perplexity models and accessing the Perplexity Search API. The lightweight and thread-safe nature of the library makes it suitable for both small and large-scale applications.

C 28 stars

shyringo/deepseek-v4-flash-0731-in-c

Run the native 284B-A13B DeepSeek-V4-Flash-0731 LLM locally on one laptop CPU: pure C, 8 GB RAM minimum, no GPU, best TPOT 0.892 s/token, resident OpenAI-compatible API with function tools. | 在笔记本单颗 CPU 上本地运行原生 284B-A13B DeepSeek-V4-Flash-0731 大模型：纯 C，最低 8 GB 内存，无需 GPU，最优 TPOT 0.892 秒/token，支持模型常驻的 OpenAI 兼容接口与函数工具。

Overview: This project provides a native implementation of the DeepSeek-V4-Flash-0731 model running on a single laptop CPU, utilizing pure C programming language with no GPU, CUDA, PyTorch, or weight conversion. It is designed for efficient inference with a prompt-lookup TPOT of 0.892 seconds per token on a 32 GB x86 laptop, making it suitable for applications requiring high performance and minimal resource usage. Key features: - Pure C implementation - No GPU, CUDA, PyTorch, or weight conversion - Supports prompt-lookup TPOT of 0.892 seconds per token on a 32 GB x86 laptop - 8 GB minimum system RAM requirement - Runs on a single laptop CPU Tech stack: Pure C programming language Why it matters: This project offers a lightweight and efficient solution for running large language models on a single CPU, making it ideal for applications requiring high performance and minimal resource usage. The absence of GPU and weight conversion ensures optimal performance without the need for additional hardware resources, making it suitable for environments with limited computational power. Repository: shyringo/deepseek-v4-flash-0731-in-c Primary language: C GitHub stars: 28 GitHub description: Run the native 284B-A13B DeepSeek-V4-Flash-0731 LLM locally on one laptop CPU: pure C, 8 GB RAM minimum, no GPU, best TPOT 0.892 s

Python 28 stars

small-cactus/Jarvis-ChatGPT-VoiceAssistant

Jarvis powered by GPT-3.5/GPT-4

Overview: Jarvis, a voice assistant powered by GPT-3.5/GPT-4, is designed to enhance user interactions by providing personalized and intelligent responses. Key features: - Voice recognition and synthesis - Natural language processing - Customizable voice commands - Integration with various devices Tech stack: Python Why it matters: Jarvis leverages cutting-edge AI technology to offer a seamless and intuitive user experience, making it a valuable tool for anyone looking to enhance their communication with technology.