Glidesort, a new stable sort in Rust up to ~4x faster for random data

Hacker News

Hi all, I've talked about glidesort a few times on HN already, but it's finally ready for release. If you have any questions, feel free to ask. An academic paper on glidesort that goes into a lot more detail than the readme is upcoming, but is not ready yet. I will be giving a talk on glidesort tomorrow at FOSDEM 2023 in the Rust Devroom at 16:10, you can seek me out there as well. In other news, I am leaving academia soon, so if you have interesting (Rust) jobs the coming months feel free to approach me.

Visit product View on Hacker NewsFeb 3, 2023

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

54%54% 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.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% 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 · Strong signals: soon · Missing: plus, platform, intuitive

36%36% 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

35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

17%17% 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

10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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