Co

Combinators in Array Languages

Hacker News

Combinators in Array Languages

Raymond Smullyan’s "To Mock a Mockingbird" book's aviary of combinator birds implemented in APL-derived languages and the problem implementing the Sage bird (Fixed Point or Y combinator) in an eagerly evaluated language. Newer APLs support lazy evaluation. My eager language has a workaround for this. The Z combinator is the standard strict-language fix: wrap the self-application in one extra function layer (λv. x x v), so the recursion is a value (a delayed call) rather than an executing expression. sw-MLPL expresses that delay as a named partial — z_step/z_recur/applicative_sage — because the language has no anonymous lambdas. Z has no Smullyan name (and no zebra in the aviary, despite the letter). The book predates and ignores the strictness problem, so it has no applicative-order variant. "Z combinator" is programmer folklore for the eta-expanded Y

Share card

Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
29%29% predicted probability of success on BetaList, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
27%27% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Qu
Quine in 0b100M Languages48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Quine in 0b100M Languages

Hacker News1
Ta
TabNine, an autocompleter for all languages48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

TabNine, an autocompleter for all languages

Hacker News607
Ca
Cardinal - Memorize vocab and phrases in 7 languages41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cardinal - Memorize vocab and phrases in 7 languages

Hacker News2
Py
Py2many – Transpile Python3 to 7 languages48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Py2many – Transpile Python3 to 7 languages

Hacker News9
TabNine
TabNine31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Autocompleter for all languages

Indie Hackerscommitment-side-project
La
Languages Interoperability Tool48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Languages Interoperability Tool

Hacker News3
PBC Languages
PBC Languages41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Practise languages by videocall with people worldwide

Product Hunt+1
Se
Semi-AI that tries to categorize the world in many languages25%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Semi-AI that tries to categorize the world in many languages

Hacker News2
Mo
Mondly – the Siri for learning languages52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Mondly – the Siri for learning languages

Hacker News7
Li
LibLET a (preliminary) lib to play with formal languages algos32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LibLET a (preliminary) lib to play with formal languages algos

Hacker News2