Pith language. JavaScript with a little bit of Python syntax

Hacker News

I've been writing a lot of JavaScript and Python lately. I like Python's syntax, so I created a language called Pith that's JS with some Python-like syntax (but without trailing colons). It's not a full compiler. It really just does search-and-replace, but I find it fun and useful in spite of some sharp edges. I use it on my smartphone to write and run short programs. Here's an example using the HN API ( https://github.com/HackerNews/API ): max = 6 ids = await getApi('topstories') for id in ids.slice(0, max) getItem(id) async def getItem(id) const div = document.createElement('div') results.appendChild(div) div.style = 'padding-bottom: 1em;' item = await getApi('item/' + id) ? item.title, '|', item.score url = '//news.ycombinator.com/item?id=' div.innerHTML = item.title.link(url + id) async def getApi(path) url = '//hacker-news.firebaseio.com/v0/' return getJson(url + path + '.json')

Visit product View on Hacker NewsSep 1, 2022

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2points

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

61%61% 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: new, using · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% 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

38%38% 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 · Strong signals: smart · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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