Monica
generalAI assistant supporting multiple major models
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.
AI assistant supporting multiple major models
Chrome extension combining multiple AI models
Transcribe and summarize meetings
Web scraping and data extraction
Rewrite text in your own words instantly
Captions and subtitles for video and audio in 120+ languages
AI shopping comparison app for finding products
AI automation for sales and revenue operations
AI assistant for financial advisors and planning tasks
ChatGPT on Mac with a global hotkey
Clone voices, train AI models, and compose melodies
Monitor electoral procedures and count voters
Directory of over 12,000 AI tools and websites
Real-time bot detection for user surveys and traffic
Blog posts generated in multiple languages
Transcribe WhatsApp voice messages instantly
Ask questions to subject matter experts
Discover ingredients in your favorite snacks
Convert audio and video to text with fast transcription
Write emails in seconds with free AI Chrome extension
Design interior and exterior spaces with AI
Extract vendor, date, and amounts from receipts
Code review automation
Online photobooth with AI styling options
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.