go

go-nbd – A Pure Go NBD Server and Client

Hacker News

go-nbd – A Pure Go NBD Server and Client

Hey HN! I just released go-nbd, a lightweight Go library for effortlessly creating NBD servers and clients. Its a neat tool for creating custom Linux block devices with arbitrary backends, such as a file, byte slice or what I'm planning to use it for, a tape drive. While there are a few partially abandoned projects like this out there already, this library tries to be as maintainable as possible by only implementing the most recent handshake revision and baseline functionality for both the client and the server, while still having enough support to be useful. I'd love to get your feedback :)

Visit product View on Hacker NewsMar 29, 2023

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

54%54% 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

41%41% 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

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

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

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

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

22%22% 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 · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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