TNY.dev
generalAPI-first link shortener built for AI agents
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.
API-first link shortener built for AI agents
AI vocal isolation and removal tool
Multi-channel customer service agent platform
End-to-end AI and machine learning consulting
AI video summarization and key information extraction
Interactive product onboarding tutorials
Compare AI models by performance and cost
AI-powered developer hiring and skills matching
AI user research through behavioral simulation
Have conversations with any online image
Plan trips with personalized itineraries
AI writing refinement for clarity and style
Fast AI-powered image annotation
Bilingual writing editor for ESL writers
AI travel planning and booking
Access ChatGPT quickly from Mac with a draggable window
Use ChatGPT in multiple languages
Check grammar, style, tone, and plagiarism in your writing
Fast, responsive ChatGPT app for Windows
AI research assistant that gathers and summarizes sources
Access 15+ AI models in one platform
Unified interface for multiple AI models
Extract insights and summarize documents with AI
Chrome extension with AI chat and image generation
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.