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SignForge – Free e-signature tool, no account needed to sign

Hacker News

SignForge – Free e-signature tool, no account needed to sign

Hey HN, I built SignForge as a free alternative to DocuSign/PandaDoc for freelancers and small businesses who just need to get documents signed without the $30/month tax. How it works: Upload a PDF → drag signature/date/text fields onto it → send to signer via email → they sign with a link (no account needed) → you get the signed PDF with a full audit trail and verification certificate. Tech stack: Next.js + FastAPI + PostgreSQL + PyMuPDF. PDFs are stamped server-side (image overlay, not AcroForm) so it works reliably on any PDF. Every signed document gets a QR code and cryptographic verification so you can prove it wasn't tampered with. Also built 12 free PDF tools (merge, split, compress, watermark, etc.) that run entirely on the server with no file storage — processed and returned immediately. Would love feedback on the signing UX and what features matter most for your use case.

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% 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: ide, io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
42%42% 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
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: email, code · Missing: mac, agents, macos
14%14% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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