VoiceDub
generalAI voice covers for songs with 10,000+ voices
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.
AI voice covers for songs with 10,000+ voices
Create and schedule AI photos, videos, and voiceovers
Chat interface to automate tasks in apps
Convert PDFs into interactive courses with adaptive quizzes
Rewrite AI content to read like human writing
Audio and video transcription in 98+ languages
Colors line art sketches and turns them into finished illustrations
Writing tools for brainstorming, drafting, and copy refinement
Converts AI text to match human writing style
Adjust lighting in photos and videos with AI
Organize and search technical documentation across teams
Text-to-speech with natural-sounding voices
Launch, automate, and scale Meta ads from one dashboard
Open-source framework for building AI applications
Translate text in images across 130+ languages
Software deals for entrepreneurs
Generate custom logos from text
Generate story branches and plot ideas
Record and share video messages
Makes AI-generated text read like human writing
AI content creation and SEO assistant
AI medical documentation for clinical notes
Rewrite text for low-literacy users across any language
Make AI-generated text read naturally
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.