Hugging Face
generalOpen-source hub for ML models, datasets, and inference.
General AI and machine learning tools include platforms for building, deploying, and managing ML models, along with infrastructure, evaluation, and workflow tools that support AI development broadly. With 674 tools, this category covers a wide spectrum from no-code ML builders to developer-facing MLOps infrastructure.
Open-source hub for ML models, datasets, and inference.
Remove watermarks and unwanted objects from photos
Frontend for ChatGPT, Claude, Gemini, and other LLMs with low costs
ChatGPT interface supporting GPT-3.5, GPT-4, and Claude with free credits
Create an AI clone of yourself
Real-time LeetCode solutions for live coding interviews
Meeting transcription and note-taking with AI insights
Multimodal LLMs for document extraction
AI-generated soundscapes for focus and productivity
ChatGPT chatbot for WordPress sites
Learn languages through media with AI translation
Clone voices from short audio samples
Automate legal document review, drafting, and analysis
AI assistant for generating and refining building designs
Ask questions about video content
Home floor plan design with drag-and-drop editor and 3D view
Preserve family stories as a printed book
Ask questions about Tesla stock and company information
Grade essays in minutes using AI rubrics
Write search-optimized press releases in minutes
Open-source API for grammar and spell checking in multiple languages
Generate buyer personas and convert features into benefits
Summarize text, articles, PDFs, and videos in 50+ languages
This category includes tools aimed at very different audiences. Platforms like Ultracode and Workverse lean toward automation and productivity applications built on AI, while infrastructure tools like EdgeTrace serve engineers managing model pipelines and monitoring production systems. Tools like Userpersona and Hippo Scribe apply ML techniques to specific tasks like persona generation or medical transcription. The unifying thread is that they are powered by machine learning but do not fit neatly into a narrow vertical like image generation or speech-to-text. When navigating this category, the most useful filters are technical depth (no-code vs. API-first), deployment environment (cloud vs. self-hosted), and target use case. Many enterprise-grade tools here require custom pricing quotes, while developer tools often offer usage-based billing. Evaluating model accuracy and latency on your specific data is almost always necessary before committing to production use.