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Free tool that auto-generates conversational, browsable documentation for any public GitHub repo, from the makers of Devin.
Developer API suite (Reader, Embeddings, Reranker) that turns web content into LLM-ready data for search and RAG.
Infrastructure company building large-scale GPU data centers and compute for AI, including Anthropic's compute buildout.
AI knowledge platform (Alani) that turns files, video, audio and docs into cited, source-grounded answers with persistent memory.
An AI-powered learning platform that turns sources into concept maps, flashcards, quizzes and summaries with multi-model chat.
No public pricing
No public pricing
No public pricing
- ✦AI-generated documentation for GitHub repos
- ✦Conversational Q&A about a codebase
- ✦Browsable index of popular repositories
- ✦Deep code indexing via Devin
- ✦Reader API converts URLs to Markdown
- ✦Multimodal multilingual embedding models
- ✦Reranker for stronger search relevance
- ✦Web search endpoint returning SERP data
- ✦MCP server for use inside LLMs
- ✦Native inference inside Elasticsearch
- ✦Large-scale GPU and data-center infrastructure for AI
- ✦Power acquisition and data-center design/build
- ✦Fast deployment (gigawatts in ~6 months)
- ✦Operates both hardware and software stack
- ✦Source-cited, grounded AI answers
- ✦Persistent memory across sessions
- ✦Video, audio, PDF and doc parsing
- ✦Choice of LLM (GPT, Claude, Gemini)
- ✦Alani Hub, Connect and Insights products
- ✦Collaboration and sharing
- ✦Enterprise-grade privacy
- ✦Concept map builder
- ✦Flashcards with spaced repetition
- ✦AI quiz and summary generation
- ✦Import from PDFs, YouTube, websites, PubMed and arXiv
- ✦Multi-model AI chat (GPT, Claude, Gemini, Qwen)
- ✦Cross-format conversion and sharing
- →Understanding an unfamiliar codebase quickly
- →Onboarding to open-source projects
- →Answering questions about repo internals
- →Ground LLMs with clean web content
- →Build semantic and RAG search
- →Rerank retrieved results
- →Give AI agents live web access
- →Training and running large AI models at scale
- →Provisioning GPU compute for AI labs
- →Building dedicated AI data-center capacity
- →Personal 'second brain' knowledge base
- →Verified research and analysis
- →Content communities and monetization
- →Enterprise data workflows and insights
- →Visualizing and studying complex topics
- →Turning research sources into study materials
- →Literature review and dissertation prep