Text Reader
generalConvert text to audio with natural 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.
Convert text to audio with natural voices
Transcribe and summarize meetings and lectures
Rephrase text quickly and clearly
Generate sound effects with AI
Auto-generate podcast summaries
AI video editor with auto-transcription
Script and video editor for social media
Convert articles and text to video
Rewrite AI text to bypass detection
Humanize AI text to bypass detection
Extract text from images and scanned docs
AI video maker and editor
Content and code generation platform
Translate images to 130+ languages while preserving formatting
AI-powered writing and productivity tools
Write product descriptions, ad copy, and blog outlines
Generate royalty-free music across multiple styles
Turn notes into study decks with AI tutoring
Write high-quality blog posts and content fast
Auto-generate and publish SEO blog content daily
Turn text, images, and prompts into interactive maps
Online gaming platform
Text to speech conversion
AI video generation
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.