Sweet – Open Source Network device configuration backups and change alerts

Hacker News

The OpsBot team has released a new tool to capture your network device configurations and alert you to changes. It collects each device’s configuration on a regular basis, stores it in Git, and sends you an email if something changes. Based on RANCID with the only dependency of Git - Sweet was built on Go by our team as a great learning opportunity. You can see our announcement at: https://opsbot.com/introducing-sweet/<p>And the tool itself lives at http://gosweet.org

Visit product View on Hacker NewsJul 24, 2014

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

76%76% 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.

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

67%67% 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 · Missing: mobile apps, ios, personal

48%48% 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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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