Key Features of What To Build
- Context-Aware Project Ideation: Go beyond keyword matching. Our AI synthesizes GitHub trends, language adoption curves, and real-time issue sentiment to propose original, technically grounded projects — with built-in scaffolding: suggested file trees, starter scripts, and deployment-ready configs.
- Repository Intelligence Dashboard: Visualize any GitHub repo like never before — interactive dependency graphs, commit heatmaps overlaid with contributor roles, issue triage timelines, and automated “health score” breakdowns (maintainability, responsiveness, documentation completeness).
- Smart README Studio: Generate dynamic, living documentation that adapts to your repo’s structure. Auto-detect frameworks (React, FastAPI, etc.), infer common workflows, and insert contextual snippets — all editable in real time with instant preview and export options.
- Contribution Compass: Discover open-source opportunities calibrated to *your* growth goals — filter by “first-timer friendly”, “mentor available”, “docs-only”, or “small bug fix”. Each recommendation includes a direct link to relevant issues, contributor onboarding tips, and estimated time-to-first-PR.
- Team & Contributor Insights: Compare GitHub profiles side-by-side — not just stars or commits, but meaningful signals: comment quality, review turnaround time, issue resolution ratio, and collaboration network density. Ideal for hiring, mentoring, or finding peers with complementary strengths.
These aren’t isolated features — they’re interconnected levers. A generated project idea links directly to similar repos for analysis; that analysis suggests README improvements; those improvements highlight missing docs — which the Contribution Compass then matches to beginner-friendly issues elsewhere. Real-world impact? Developers report cutting idea-to-PR time by 65%, reducing README drafting from hours to under 90 seconds, and landing their first meaningful open-source contribution 8x faster than manual searching.
Why Choose What To Build?
Because GitHub isn’t just a code host — it’s a living ecosystem of collaboration, learning, and reputation. What To Build respects that complexity. It doesn’t force abstraction; it surfaces meaning *within* the GitHub context. While other tools treat repositories as static archives, What To Build reads them as narratives — decoding maintainer intent, community rhythm, and technical debt signals. Its AI is fine-tuned exclusively on GitHub data (not generic web text), ensuring suggestions reflect actual development realities, not theoretical ideals.
This focus translates to trust: no hidden data harvesting, no mandatory account creation for core features, and transparent attribution for all AI-generated content. Free access covers full idea generation, repo analysis, README creation, and contribution discovery — empowering students, hobbyists, and indie devs without paywalls. For teams, enterprise plans add private repo support, custom trend alerts, and contribution analytics dashboards — scaling seamlessly from solo exploration to org-wide developer enablement. In short: What To Build doesn’t ask you to adapt to AI. It adapts AI to *how you already work on GitHub.*
Use Cases and Applications
A junior developer uses the Contribution Compass to find a well-maintained TypeScript library with “good first issue” labels — then runs What To Build’s repo analysis to understand its testing strategy before submitting a PR. A bootcamp instructor generates 10 project ideas filtered for “React + Firebase + accessible UI”, exports the roadmap as a classroom syllabus, and uses the README Studio to scaffold student repos with consistent structure. An open-source maintainer analyzes their own repo’s health dashboard, spots declining contributor retention, and uses the Developer Comparison tool to benchmark against peer projects — revealing gaps in onboarding docs that they fix using the auto-generated README template.
Even non-coders benefit: technical writers use repo analysis to identify documentation debt hotspots; engineering managers run team-wide comparisons to spot knowledge silos; and recruiters source candidates based on *meaningful* contribution patterns — not just commit counts. Every use case starts with intention and ends with concrete next steps — no vague inspiration, just GitHub-native action.