Letterly App
generalVoice-to-text transcription for notes and messages
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.
Voice-to-text transcription for notes and messages
Generate Excel formulas and analyze spreadsheets without coding
Long-form writing editor for storytellers
Turn text into memes with AI
Real-time speech translation with AI voices
Open-source rich text editor framework with extensions
Fast, open-source search and AI retrieval engine
Convert video to 3D animation instantly
Extract text from images with OCR
Search and analyze video with AI
Text-based RPG with AI dungeon master and world-building tools
Free AI sound effect generator from text
Browser with agentic AI that takes actions on your behalf
Listen to articles and PDFs
SEO-optimized blog posts from real data
Generate unique text for blogs, marketing, and writing projects
AI-powered ad creation with optimization for conversions
Create CSS animations using AI descriptions
AI regex generator and tester
AI assistant with web search and file integration
Write SEO-optimized blog content with ease
Summarize articles, PDFs, and videos instantly
Write and schedule posts for Twitter, LinkedIn, and Threads
Generate short 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.