Letlang specification inspired by Python PEPs (WIP)

Hacker News

Hi HN, It's been some time now since I've started working on my own programming language. But I never wrote the spec. I love the concept of PEP (Python Enhancement Proposals) and it inspired me to start writing the spec in a similar format. As of now, I've written 4 LEPs: - LEP-001: Language Target - https://letlang.dev/lep/001/ - LEP-002: Import resolution - https://letlang.dev/lep/002/ - LEP-003: Statements - https://letlang.dev/lep/003/ - LEP-004: Type System - https://letlang.dev/lep/004/ And I've started to write the following LEPs: - LEP-005: Functions - LEP-006: Side Effects - LEP-007: SAT Solver I think this will help me stop changing my mind for starters, but also help me get the implementation right (which is still a work in progress). The Github repository is not yet opensource, I intend to open it once the "hello world" example compiles. I'm still wondering what would be a good license to use for a compiler, I'd love your feedback.

Visit product View on Hacker NewsMay 6, 2022

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

67%67% 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: open · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, 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.

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.

Incorrect prediction on native model

Similar products

I made a boxen library for Python (inspired by sindresorhus)63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made a boxen library for Python (inspired by sindresorhus)

Hacker News2

An ORM for Python objects, inspired by Django59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An ORM for Python objects, inspired by Django

Hacker News13

Reactive Signals for Python – inspired by Angular's reactivity model37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Reactive Signals for Python – inspired by Angular's reactivity model

Hacker News31

Consecution: A Python Pipeline Tool Inspired by Apache Storm Topologies50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Consecution: A Python Pipeline Tool Inspired by Apache Storm Topologies

Hacker News3

Ariadne, Python lib for writing GraphQL servers, inspired by Apollo62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Ariadne, Python lib for writing GraphQL servers, inspired by Apollo

Hacker News7

PyBench 2.0 – Python benchmark tool inspired by Geekbench51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PyBench 2.0 – Python benchmark tool inspired by Geekbench

Hacker News1

Pcons: new software build tool in Python, inspired by SCons and CMake66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Pcons: new software build tool in Python, inspired by SCons and CMake

Hacker News1

XKCD-inspired StackSort52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

XKCD-inspired StackSort

Hacker News948

Experimental Hyperdocument System Inspired by Doug Engelbart51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Experimental Hyperdocument System Inspired by Doug Engelbart

Hacker News1

Heresy, a BASIC-inspired functional Lisp62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Heresy, a BASIC-inspired functional Lisp

Hacker News34

Browse all products Analyze your launch