Ra

Ractor – a Rust-based actor framework with clusters and supervisors

Hacker News

Ractor – a Rust-based actor framework with clusters and supervisors

SOME HIGHLIGHTS. * We have a full supervision tree so actors can monitor other actors for exits and unhandled panic!s (at least the ones that can be caught) * The actor lifecycle is handled for you in a simple single-threaded, message handler primitive * You have a mutable state with each message handling call, so you have an easy way to create stateful actors and update that state as messages are processed * Actors talk to other actors by message passing, but there are remote-procedure-calls (RPCs) so actors can "ask a question" to another actor and wait on the reply. * A lot of the concurrency primitives are handled by the framework, such as cancellation/termination of actors (both graceful and forceful) * A Factory primitive in order to formulate distributed processing pools with multiple job routing options * Early but stable support for a distributed epmd-like cluster environment, where you can talk to actors over a network link. It's an additional crate (ractor_cluster) that builds on ractor to facilitate the inter-connection between nodes and support remote casts and calls to actors on a remote node. We're openly seeking feedback, so please feel free to utilize the library and let us know if there's anything you find missing or doesn't work as expected!

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: single, open · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · 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 · Strong signals: calls · 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
29%29% 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
22%22% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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