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Convert PDFs to clean Markdown online (preserves tables/formulas)

Hacker News

Convert PDFs to clean Markdown online (preserves tables/formulas)

Hey HN, Sharing a web app I built for converting PDFs to Markdown. I was tired of broken tables and formulas, so I focused on making sure those elements (along with headings and text) convert cleanly. Features: Preserves headings, text flow, formulas, and table structure. Online editor for post-conversion tweaks. Doesn't store your PDF content or generated Markdown (privacy focused). Try 10 pages anon, or 50 pages/month free when logged in. (Unlimited plans available, same quality). It's live here: https://pdftomarkdown.pro/ Let me know if you have any feedback or run into issues!

Visit product View on Hacker NewsMar 31, 2025

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

59%59% 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: io · Missing: https docs, excited, just released

51%51% 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 · Missing: mac, agents, macos

39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, 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.

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

20%20% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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