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Onboard.ai
The Problem Every developer has lived through it: you join a new project and spend the next two days reading files, tracing imports, decoding undocumented environment variables, and asking teammates questions they've answered a dozen times. Onboarding a non-trivial codebase routinely costs 8–16 hours of reading that produces no code and blocks contribution from day one. Wikis go stale. READMEs are incomplete. There is no standard automated solution. The Solution onboard.ai eliminates that ramp-up time. Paste a public GitHub URL — in under a minute you get a full structured report: a plain-English project summary, a live Mermaid architecture diagram, a file-by-file structure guide, a step-by-step local setup walkthrough, beginner-friendly starter tasks grounded in specific real files, and concrete optimization suggestions. Everything downloads as a single Markdown file that renders natively on GitHub, diagrams and all. What Makes It Unique Most repo summarizers make one slow sequential AI call and return a blob of text. onboard.ai fires four specialized Groq LLM calls concurrently using asyncio.gather — each with its own dedicated API key and token budget — so all four run in parallel without hitting rate limits. The backend handles async GitHub tree traversal, binary and secret file exclusion, a 20,000-character context cap, and a Mermaid post-processor that strips parentheses from node labels before they reach the frontend. The React frontend renders each section in its own tab, renders the architecture diagram live in-browser, and offers one-click Markdown export with no second backend round-trip. The Impact Onboarding a developer to a real-world codebase went from roughly two days of reading to under twenty minutes — measured live against our own production test repository.
27 Sep 2026
