APA citation generator that explains missing metadata

Hacker News

I built this because many citation tools return incomplete APA references (especially missing author/date -> n.d.), and users still have to fix everything manually. This tool takes a URL or DOI and: - extracts metadata with rule-first parsing - uses AI to fill likely missing fields - shows confidence + “needs review” warnings - lets you edit and copy both reference + in-text citation quickly Current focus is APA 7 for websites/DOI. No-login usage is available for quick tests. Would love feedback on: 1) citation accuracy edge cases 2) UI/UX clarity for review warnings 3) features you’d want next (BibTeX export, Zotero flow, etc.)

Visit product View on Hacker NewsFeb 26, 2026

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

57%57% 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.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, plain · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

19%19% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

Correct prediction on native model

Similar products

YesCite54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Citation generator for APA, MLA, and Chicago

Indie Hackers1education

CSVGen12%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI metadata generator for microstock contributors

Product Hunt+6

Quoted – a citation verifier that refuses rather than guesses46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Quoted – a citation verifier that refuses rather than guesses

Hacker News2

ACS Citation Generator3%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Free Online ACS Citation Generator

Product Hunt+2

Tmfi: The Missing Firefox Installer37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tmfi: The Missing Firefox Installer

Hacker News2

WebGPU is missing in ncnn43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

WebGPU is missing in ncnn

Hacker News1

The missing stroustrup();41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The missing stroustrup();

Hacker News2

Denigma – AI that explains code33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Denigma – AI that explains code

Hacker News3

Scholarjoiner59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The privacy-first citation generator and scholarship finder.

Indie Hackerscommitment-side-project

Scholarjoiner7%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The privacy-first citation generator & scholarship finder

Product Hunt+1

Browse all products Analyze your launch