Telborg
generalDaily data on US energy, power grids, and permits
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.
Daily data on US energy, power grids, and permits
Converts presentations to text notes
Auto-generate step-by-step guides
ChatGPT app for iPhone, iPad, and Mac with Siri integration
Generate buyer personas and convert features into benefits
Generative AI transformations for batch image editing
Summarize text, articles, PDFs, and videos in 50+ languages
Business AI assistant
Directory of new Australian casinos
Knowledge bases
Extract data from receipts with OCR and AI
Virtual AI clones
Practice coding interviews with AI hints and feedback
AI-powered tool for fast, accurate book translation for authors and publishers
Screen short-term rental guests with AI
Android chat app with multiple AI models and voice support
Summarize Slack threads and channels
QR code generation with AI
AI-powered threat intelligence collection and analysis
Learning resources for large language models and AI fundamentals
AI-powered chat summaries for LiveChat conversations
Search and book travel directly from ChatGPT
Research and experiments in safe artificial intelligence from DeepMind
AI-powered podcast search and discovery platform
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.