PDFFiddler – PDF Playground to solve all your PDF problems

Hacker News

PDFFiddler Playground is a free PDF playground for manipulating PDF, extracting data from PDF, form filling, archiving, merging grouping and many many more. It is powered through Domain-driven custom scripting language (equivalent to Javascript) which can be quickly written through powerful editor with intellisense support. It has dozens of ready made template to play with. Please check out below link https://playground.pdffiddler.com/?apps=true Few templates, quick links has been added below 1) Merging group of PDF - https://playground.pdffiddler.com/playground/2X8P2F 2) Redacting sensitive information from PDF- https://playground.pdffiddler.com/playground/2X8P3l 3) Add digital Signature to PDF - https://playground.pdffiddler.com/playground/2X37ya 4) PDF Archiving - https://playground.pdffiddler.com/playground/2X37EY 5) Add USPS IMB barcode to PDF - https://playground.pdffiddler.com/playground/2X37zw 6) Automated Form filling - https://playground.pdffiddler.com/playground/2X37DX 7) Watermarking PDF - https://playground.pdffiddler.com/playground/2X37Gn 8) Extracting any data from PDF - https://playground.pdffiddler.com/playground/2X37Gy and many many more If you do have any feedback or any business requirement, we would love to hear about it. Please contact us at query@pdffiddler.com

Visit product View on Hacker NewsMay 13, 2020

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

60%60% 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 NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% 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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps, code · Missing: mac, agents, macos

11%11% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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