Python) Markdown Exec, execute code blocks and render their output

Hacker News

Hey everyone, here's an extension I made for Python-Markdown ( https://github.com/Python-Markdown/markdown ). It builds on top of PyMDown Extensions' SuperFences ( https://facelessuser.github.io/pymdown-extensions/extensions... ), and allows Markdown writers to execute their Markdown code blocks to render the execution output in place of / in addition to the code blocks. Languages supported: python/pycon; sh/bash/console; md (literate programming!) Special fences: tree, to easily render file trees; pyodide, to render an interactive Ace.js editor that runs Python code thanks to Pyodide Adding examples to the gallery is quite fun :) Don't hesitate to create an issue if you'd like me to add examples for another tool or Python library!

Visit product View on Hacker NewsJun 15, 2024

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Actual performance

2points

Did not reach leaderboard

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

73%73% 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: user, code · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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