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BentoPDF, Hyper Compress and Kura

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BentoPDF, Hyper Compress and Kura

Hello. I developed an open source tool called BentoPDF. Its an open source PDF toolkit that runs in your browser. With the latest update, you can actually edit existing pdf text, images and objects right in your browser. Live Website: https://www.bentopdf.com Repository: https://github.com/alam00000/bentopdf Along with the latest update I would like to share with you Hyper Compress. Its a high fidelity, content preserving compression engine that preserves PDF conformance and surpasses all the other open source PDF compression tools. It runs everywhere: CLI, Node SDK, C API, self hosted service, and also in browser via WebAssembly. Live Website: https://hyper.bentopdf.com Repository and benchmarks: https://github.com/alam00000/bentopdf-hyper-compress Kura is a PDF standards, conversion and preflight engine. It supports: - All 11 PDF/A conformance levels: PDF/A-1a, PDF/A-1b, PDF/A-2a, PDF/A-2b, PDF/A-2u, PDF/A-3a, PDF/A-3b, PDF/A-3u, PDF/A-4, PDF/A-4e and PDF/A-4f - Accessibility: PDF/UA-1 and PDF/UA-2 - Print production: PDF/X-1a, PDF/X-3, PDF/X-4, PDF/X-4p, PDF/X-5g, PDF/X-5n and PDF/X-5pg - Engineering and variable data printing: PDF/E-1 and PDF/VT - E-invoices: Factur-X, ZUGFeRD, XRechnung and Order-X - 396 bundled print-preflight profiles It has been tested against several standards suites, including the veraPDF corpus, Isartor, BFO, Ghent Output Suite 5.0, the PDF/UA Reference Suite and Cal Poly's PDF/VT suite. Across 30,677 PDF conversions it had zero crashes and zero timeouts, with a 0.05 second median conversion time. Like Hyper, it ships as a CLI, C library, npm package, Docker image and WebAssembly build. Live Website: https://kura.bentopdf.com/ Repository and benchmarks: https://github.com/alam00000/bentopdf-kura Both Kura and Hyper Compress are running the WASM build so none of your PDF is uploaded and everything runs in your browserr If possible I would like you guys to try them out and give me feedback on how it worked for you. Thank you.

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, including · Missing: reddit linkedin, podcasting, created
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: dock, open · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
54%54% 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 · Strong signals: host · Missing: plus, platform, intuitive
49%49% 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
42%42% 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.

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