Aarde – Self Replicating Programs

Hacker News

Last weekend I created a new visualization/demo. It's a sort of crossover between artificial life and defragmentation on MSDOS. The organisms "live" in the memory and continuously copy themselves to a free piece of memory. The programming language consists of only 5 instructions to keep it simple: S - Program start E - Program end F - Find location to copy to C - Copy to location J - Jump to start of program Every time a code is copied, a mutation can occur. We start with the following simple life form: SFCJE The rest is evolution.

Visit product View on Hacker NewsApr 11, 2024

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3points

1comments

Did not reach leaderboard

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

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

nativeThis product was originally launched on this platform.

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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, visual, code · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% 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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% 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

16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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