iceoryx2 v0.3.0 released – zero-copy IPC middleware in Rust

Hacker News

Today, we released iceoryx2 v0.3.0. It comes with some cool new features, bug fixes, and additional examples; see this here https://github.com/eclipse-iceoryx/iceoryx2/releases/tag/v0.... Have a lot of fun with it. For those of you who do not know iceoryx yet, it is a library written in Rust that makes platform-agnostic inter-process communication as easy and fast as possible using zero-copy techniques. It runs on Linux, Windows, macOS, and FreeBSD (x64, aarch64) but not yet on a toaster ;). You can check out our roadmap here: https://github.com/eclipse-iceoryx/iceoryx2/blob/main/ROADMA... To summarize our work for Q2.2024, we want to add a C binding and introduce decentralized resource monitoring so that the resources of crashed applications no longer need to be cleaned up manually. We are always happy to receive feedback. So, if we forgot an important feature or you want to see something reprioritized - don't hesitate and get in touch with us.

Visit product View on Hacker NewsApr 18, 2024

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

66%66% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

51%51% 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

47%47% 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

39%39% 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: way · Missing: mobile apps, ios, personal

28%28% 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 · Strong signals: introduce · Missing: web3, chat, crypto

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

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