Friends & Fables
generalText-based RPG with AI dungeon master and world-building tools
This category covers tools built around large language models: infrastructure for deploying, fine-tuning, evaluating, and monitoring LLMs in production. With 369 tools listed, it is one of the more technical categories on the site, aimed primarily at developers and ML engineers rather than end users.
Text-based RPG with AI dungeon master and world-building tools
Browser with agentic AI that takes actions on your behalf
Create CSS animations using AI descriptions
Build a blog or website in minutes
Voice and audio to structured notes
Quick and accurate translation across multiple languages
Automatically create short social clips from long videos
Free AI image and audio tools without signup
Generate HD videos from text prompts and images
Audio and video transcription in 98+ languages
Organize and search technical documentation across teams
Rewrite text for low-literacy users across any language
Make AI-generated text read naturally
Text humanization for AI-generated content
Turn articles into professionally edited video
Extract text from images via OCR
Explain or summarize highlighted text
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Free paraphrasing tool for essays and articles
Write research papers and essays with citations
Converts still images into short videos
AI tool that generates text descriptions, captions, and prompts from images
Free AI video editor with auto captions
Fast audio and video transcription to text
LLM tooling has exploded alongside the models themselves, and the category now spans several distinct problem areas. Deployment and serving tools like PeriFlow and Dstack help teams run models efficiently at scale. Evaluation and observability tools like UpTrain and AIWatch track model quality, drift, and cost over time. Memory and retrieval tools like Cognee add persistent context or RAG capabilities to LLM applications. When choosing, the key questions are infrastructure fit (cloud, on-prem, or hybrid), model compatibility (OpenAI-only vs. open-weight models), and whether the tool addresses your actual bottleneck, whether that is latency, cost, accuracy, or developer velocity. Pricing structures vary: some tools charge per token processed, others per seat or per API call. Open-source options exist across most sub-categories, which is worth considering for teams with engineering capacity to self-host.