Type Chart Calculator – Fast Pokemon type effectiveness lookup

Hacker News

Hey HN! I built a Pokemon type effectiveness calculator that focuses on speed and simplicity. The problem: Existing type charts are either complex 18×18 tables or buried in wikis. When you're in a battle and need to know "what's Fire/Flying weak to?", you want an instant answer. The solution: A static-generated Next.js site with: - 43 SEO-optimized pages (18 single types + 20 popular combos) - Default examples (Fire/Flying loads with results immediately) - Quick example buttons (one-click to try popular combinations) - Battle simulator with STAB calculations Tech stack: Next.js 14, TypeScript, Tailwind CSS, fully SSG for <1s load times. Live: https://www.typematchup.org Source: [if you want to open source it] Would love feedback on the UX and any features competitive players would find useful!

Visit product View on Hacker NewsJan 28, 2026

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2points

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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: single, open · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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