Payload, P2P file transfer app, goes online

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Payload is a x-platform desktop app for file transfers, with focus on E2EE security, speed and UX. Last time[1] it was LAN only, and over the last 2 years (wow, time flies) it's now online (WAN) as well. - There's no storage/hosting. This is device-to-device transfers only. - Unlike FTP, syncthing etc, you don't need to grant permissions to your file system. The receiver decides where to put the files. - Hybrid P2P - relay architecture: This imo is the most complex, fun, frustrating and interesting part. Hole punching is fascinating. If you are curious, I highly recommend Tailscale's article[2]. - Privacy: Server maintains contact list for online status and connection assistance (in other words, not anonymous ). However, transfers are always end-to-end encrypted, including metadata. - Speed: The ambition is to saturate both LAN and WAN links. If you got a 1-10 Gbps+ line, I'd love to stress test it with you. - Tech stack: Tauri, Go, SQlite, Svelte, etc (ask!) This is a one-man show, with no outside funding. Planning to monetize with paid data plans for heavy users + permanent free tier for casuals. LAN transfers remain free & unlimited. [1]: https://news.ycombinator.com/item?id=32100352 [2]: https://tailscale.com/blog/how-nat-traversal-works

Visit product View on Hacker NewsNov 20, 2024

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

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released

55%55% 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: users, way · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, users · Missing: plus, intuitive, reviews

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new · Missing: mac, agents, macos

19%19% 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.

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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