Manool v0.6 is Out

Hacker News

Manool v0.6 is Out

MANOOL is a dynamically typed homoiconic programming language with functional core and value (copy-on-write instead of reference) storage semantics. News: + All values, regardless of type, are now totally ordered, which implies fewer arbitrary restrictions and more generality, useful to construct general caches, etc. + Parallel `for`-loops can now iterate over views of different sizes, useful to iterate over unbounded and bounded views at the same time. + Symbols starting with underscores and uninterned symbols are now excluded from the set of symbols that denote themselves by default, which means better diagnostics but implies lesser generality (a tradeoff; this required a few fixes in the standard library code). + Those undefined symbols are now actually bounded to a special "error" entity instead of being reported directly, for consistency with the overall language feel. + Internal cosmetic changes. The current release should be regarded stable and still has less than 10 KLOC in C++! As always, I am providing pre-compiled binaries for 14 combinations of OSes/ISAs/ABIs: https://github.com/rusini/manool/releases/tag/v0.6

Visit product View on Hacker NewsAug 21, 2020

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

60%60% 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: way, para · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting

37%37% 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

36%36% 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 · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% 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

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

Correct prediction on native model

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