GitShow Repo Showroom – a landing page for any GitHub repo

Hacker News

I wanted a way to quickly see what's going on in an open source project without clicking through 10 GitHub tabs. Contributors, languages, commit activity, community health, recent PRs - all scattered across different pages. GitShow now generates a showroom page for any public repo. Just visit gitshow.dev/owner/repo. Try a few: gitshow.dev/facebook/react gitshow.dev/vercel/next.js gitshow.dev/solidjs/solid gitshow.dev/ofershap/mcp-server-devutils Each page shows: - Top contributors with accurate total count. GitHub's API returns max 30 per page, so I use the Link header pagination trick (request per_page=1, read the last page number) to get the real number. facebook/react shows ~2,000 contributors, not 30. - Language breakdown as a visual bar and weekly commit sparkline from the participation stats API. - Community health score: does the repo have a README, license, contributing guide, code of conduct, issue/PR templates? Pulled from GitHub's community profile endpoint. - Recent open PRs and recently merged fixes. Bot authors (dependabot, renovate, etc.) are filtered out. Markdown and HTML in body snippets are stripped to plain text. - Quick actions inside the hero card: Star on GitHub, Fork, Clone (copies the git clone command), and an npm link for JS/TS repos. - Breadcrumb navigation: GitShow / owner / repo. Every page includes Schema.org SoftwareSourceCode structured data and a BreadcrumbList, so it surfaces in AI search engines (ChatGPT, Perplexity, Google AI). There's also a structured data block at the bottom of each page specifically for LLM agents scraping the web - key stats in a machine-readable format. Stack: Next.js with 1-hour ISR, all data from GitHub REST API, zero client JS except two small interactive bits (clone-to-clipboard and scroll-to-top). Tailwind, Vercel. No signup, no AI processing, no paywall. MIT licensed. Source: https://github.com/ofershap/gitshow

Visit product View on Hacker NewsFeb 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, 000 · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal

25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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