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Parseflow, how to parse documents when you're broke

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Parseflow, how to parse documents when you're broke

Hello HN, I built Parseflow, it's a simple, evidence focused extraction API which can extract take PDFs, DOCX and TXT files and extract/chunk the info inside them to improve LLM context and reduce token usage. If you want to try out a demo, you can find it here: demo.parseflow.tech I am still a student dev, graduating high school this year so I still have a lot to learn. I am trying to build this project to help pay for tuition this year but also to help me learn. So any feedback, advice, questions, etc... are super appreciated and either I will try to respond to the comments or you can email me at hello@parseflow.tech Thanks, bollethegoalie

Visit productView on Hacker NewsMay 21, 2026
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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 HuntOn track for Day 1 leaderboard · Strong signals: email, context · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% 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
27%27% 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
17%17% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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