PseudoRun – I built a pseudocode runner by yelling at AI

Hacker News

A few months ago I shared PseudoRun , my interactive pseudocode runner. I haven’t added massive features since, but the story behind it is still fun and maybe useful for others. I built it mostly out of frustration: ads everywhere, broken educational tools, and the usual syntax headaches. My approach was simple—write a spec for my own pseudocode, then “yell at AI” until it followed the rules and executed correctly. What it does: Live pseudocode execution: Write something like FOR i = 1 TO 10 PRINT i and see results instantly. Custom pseudocode interpreter: Works on a specification I designed myself. No distractions: No ads, no clutter, just a clean environment to experiment. Instant feedback: Mistakes or logic errors show up right away. It’s still mostly the same runner as before, but I think the bigger point is that building tools—even small, niche ones—can be surprisingly fun and educational, especially when AI is involved in the process. Would love to hear if anyone else has tried building a tool like this “from frustration” or used AI in a weird way to make something that just works.

Visit product View on Hacker NewsNov 29, 2025

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

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, 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

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month, way, education · Missing: mobile apps, ios, personal

35%35% 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

14%14% 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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