Payload – Cross-platform desktop app for LAN file transfers

Hacker News

Hi HN. I built Payload to make file transfers easy for less-technical users who need large/fast transfers, so I have focused on auto-discovery, drag-and-drop, visually distinct device icons. It's using Tauri (an "Electron alternative" built on Rust) which keeps my binaries small and bundles to .msi, .dmg, .deb and .appimage. No CLI, iOS or Android support (yet). The network stack is a separate binary written in Go. It uses mDNS for local network discovery and TLS over TCP or Quic, with a public Ed25519 keypair for each device. The protocol is ad-hoc and symmetrical control stream using JSON and binary data streams. Planning to open source these parts eventually.. Transfers should saturate the local network link. It reaches ~116 MB/s wired at my home, but if you have a >1000 Mbit link, I'd be curious to see how much speed you can squeeze out. See also: https://news.ycombinator.com/item?id=21575869 https://news.ycombinator.com/item?id=24351111

Visit product View on Hacker NewsJul 14, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: open source, 000, io · Missing: https docs, excited, just released

75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, visual · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, 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

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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