Trail of Bits announces the open-sourcing of Buttercup, their AI-driven Cyber Reasoning System (CRS) developed for DARPA’s AI Cyber Challenge (AIxCC). The article details how Buttercup works, including its four main components (Orchestration/UI, Vulnerability discovery, Contextual analysis, and Patch generation), provides instructions for getting started, and outlines future development plans.
A detailed blog post discussing OpenAI's newly released open-weight GPT models, including performance benchmarks, initial testing on various hardware (Mac laptops, Cerebras), and comparisons to other open-source models. It covers aspects like reasoning capabilities, tool calling, and the new OpenAI Harmony prompt format.
This article details 10 open-source AI tools for developers, covering their benefits, features, and use cases. It emphasizes transparency, offline capabilities, and community support as key advantages of open-source AI.
| **Tool Name** | **Description** | **Key Features** | **What I Like About It** |
|---|---|---|---|
| **Talkd.ai** | Prototyping AI Agents | No-code, JSON/YAML config, API integration | Fast prototyping, no backend needed |
| **Marimo** | Python Notebooks for Apps | Reactive cells, version control, UI widgets | Stable, shareable, version-controlled apps |
| **Unsloth AI** | LLM Fine-Tuning | Memory-optimized training, supports Llama 3 | Accessible fine-tuning on modest hardware |
| **HackingBuddyGPT** | AI for Ethical Hacking | Offline operation, recon tools, payload generation | Offline security, privacy |
| **Giskard** | AI Testing & Debugging | Test case creation, continuous monitoring | Engineering discipline for AI quality |
| **OpenWebUI** | Self-Hosted ChatGPT UI | Local LLMs, plugin support, persistent memory | Privacy, local control |
| **Axolotl** | LLM Fine-Tuning | YAML config, supports QLORA/PEFT/LORA | Simplified fine-tuning, reproducibility |
| **FastRAG** | RAG Pipeline | Local operation, fast query times | Quick, lightweight RAG setup |
| **Nav2** | Robot Navigation Framework | Real-time obstacle detection, multi-robot coordination | Flexible, modern ROS 2 integration |
| **MindsDB** | Machine Learning in Database | SQL-based training/inference, supports various DBs | Easy integration with existing SQL workflows |
| **Tool Name** | **Description** | **Key Features** | **What I Like About It** |
|---|---|---|---|
| **Talkd.ai** | Prototyping AI Agents | No-code, JSON/YAML config, API integration | Fast prototyping, no backend needed |
| **Marimo** | Python Notebooks for Apps | Reactive cells, version control, UI widgets | Stable, shareable, version-controlled apps |
| **Unsloth AI** | LLM Fine-Tuning | Memory-optimized training, supports Llama 3 | Accessible fine-tuning on modest hardware |
| **HackingBuddyGPT** | AI for Ethical Hacking | Offline operation, recon tools, payload generation | Offline security, privacy |
| **Giskard** | AI Testing & Debugging | Test case creation, continuous monitoring | Engineering discipline for AI quality |
| **OpenWebUI** | Self-Hosted ChatGPT UI | Local LLMs, plugin support, persistent memory | Privacy, local control |
| **Axolotl** | LLM Fine-Tuning | YAML config, supports QLORA/PEFT/LORA | Simplified fine-tuning, reproducibility |
| **FastRAG** | RAG Pipeline | Local operation, fast query times | Quick, lightweight RAG setup |
| **Nav2** | Robot Navigation Framework | Real-time obstacle detection, multi-robot coordination | Flexible, modern ROS 2 integration |
| **MindsDB** | Machine Learning in Database | SQL-based training/inference, supports various DBs | Easy integration with existing SQL workflows |
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.
IBM announces Granite 3.3, featuring a new speech-to-text model (Granite Speech 3.3 8B), enhanced reasoning capabilities in Granite 3.3 8B Instruct, and RAG-focused LoRA adapters for Granite 3.2. The release also includes activated LoRAs (aLoRAs) for improved efficiency and all models are open source.
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.
