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Clawn – A learning friendly statically typed programming language

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Clawn – A learning friendly statically typed programming language

I am developing a new statically-typed compiled programming language, Clawn, with the goal of making it easy to write high-quality programs. Currently, Clawn has the following features ・Simple syntax ・Higher rank polymorphism ・Safe "any" ・Automatic memory management (WIP) I value a gentle learning curve, and Clawn is designed to be easy to learn. For example, Clawn's function definitions have no type annotations and are defined as polymorphic functions, with the goal of reducing the amount of knowledge you have to be aware of from the beginning in introductory programs such as defining and calling simple functions, omitting type definitions and entry points (int main(), etc.). It also has a “requirement" feature (like composable C++’s “concept”), C FFI, etc. Clawn is still a work in progress, but will be actively developed after 2023 (after my college entrance exam).

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, 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
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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