Riffusion
generalGenerative AI for creating and remixing music
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.
Generative AI for creating and remixing music
Turn text prompts into AI-generated videos
App translation with full context and human review
Convert AI text to natural, human-like writing
Portfolio platform with galleries and zero-commission sales
Generate Excel formulas and analyze spreadsheets without coding
Long-form writing editor for storytellers
Open-source rich text editor framework with extensions
Convert video to 3D animation instantly
Free AI sound effect generator from text
Generate unique text for blogs, marketing, and writing projects
AI-powered ad creation with optimization for conversions
AI regex generator and tester
Generate short videos from text prompts
Extract text from images, PDFs, and handwritten notes
Turn text into short-form video
Automate workflows across macOS apps
Write essays and homework with AI assistance
Make AI text read more naturally
Automate podcast editing and social clips
AI essay writing tool for students and professionals
Generate Anki flashcards from study materials
AI marketing content and campaign management
Create videos from text prompts
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.