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A bad Lisp dialect and my dive into C

Hacker News

A bad Lisp dialect and my dive into C

Bad: A bad Lisp dialect written in C I was going to wait a while before I posted this anywhere but I figured I'd release early and see where it goes. This is my first real C application after years of indirectly avoiding it. What a life you all (C developers) live. Very fun once you get the hang of it but I've had to learn things that I didn't even know were challenges in computing and language design. It's eye opening! I'm also new to Lisp which is why there's not really many features at the moment. At first I was going to just implement Arc but I think I'll save that for when I'm more skilled. If anyone has any feedback on the code, implementation, anything, feel free to let me know! Above all else, I want to learn. I can take criticism. In fact that is why I am posting such a new project. :) https://github.com/bit-42/bad

Visit product View on Hacker NewsOct 11, 2021

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

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

60%60% 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 · Missing: mobile apps, ios, personal

41%41% 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

32%32% 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

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