ClipLab
generalTransform story ideas into short-form videos
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.
Transform story ideas into short-form videos
Fast audio and video transcription to text
Collaborative writing app with AI help
AI marketing team for e-commerce stores
Learn Japanese numbers, grammar, and conversational meaning
Rewrite text to be completely original
AI image editing with style transfer and contextual modifications
Make AI text bypass detection tools
Humanize AI text for free without signup
Transform complex ideas into consistent educational videos
Check for AI content and remove detection markers
Free tools for PDF, images, YouTube, and online utilities
Summarize web links into bullets and key quotes
Check if content was written by AI or humans
Generate free printable coloring pages with AI
Convert text descriptions into full songs
AI tool to write and edit college essays
Text to speech in over 20 languages
Copywriting tool with advanced formatting and styling
Generate original music, vocals, and lyrics from text
AI-powered text-to-speech with natural-sounding output
Browse and book tickets for upcoming events
Send SMS reminders before meetings to reduce no-shows
Convert text into short videos for YouTube and social media
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.