Wi

WisniaLang – Compile binaries 41x faster and 27x lighter than Rust

Hacker News

WisniaLang – Compile binaries 41x faster and 27x lighter than Rust

Blog post: https://belijzajac.dev/wisnialang-compiler-project/ Due to a lack of time, I haven't yet begun optimizing IRs and machine code to speed up the runtime, and the compiler still lacks many features that are far behind the originally proposed grammar for the language (such as loops, classes, and more data types). Despite these limitations, the compiler is functional, and example programs can be found in the tests/programs directory. Both code and blog post feedback are welcomed.

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Actual performance

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
57%57% 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: mac, code · Missing: agents, macos, agent
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
29%29% 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
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
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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