Software Escrow for Developers

Hacker News

New regulations (NIS2, DORA, etc.) mean your enterprise clients will start asking you to demonstrate software resilience. The easiest way to do this is through software escrow - storing a copy of your code in a third-party vault so they can keep using their software if something happens to your company. The traditional process involves zipping your repo quarterly and dealing with lawyers who want to negotiate what counts as "source code" while your actual deployment involves deployment configs, build scripts, and dependencies they've never heard of. We built software escrow that just connects to your repos and handles uploads automatically. OAuth with GitHub/GitLab/whatever tool you use, syncs daily, done. Pretty straightforward automation for something that was surprisingly manual. Check it out here: software-escrow.com

Visit product View on Hacker NewsJun 26, 2025

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

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% 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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

18%18% 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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