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Code garden deep-dive: my Forth C64 tetromino game

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Code garden deep-dive: my Forth C64 tetromino game

(The article is permalinked to a tag, for latest:) Deep-dive: https://github.com/ekipan/sss/blob/main/Design.md Repo front page: https://github.com/ekipan/sss The Silent Soviet Stacker is, in order: 1. A tinker-toybox I wrote and pick at to relax. 2. A technical deep-dive writeup, showing Forth by example. 3. A game that works and you can play. Try it (<5 minutes): 1. C64 emulator [1] 2. Load durexforth cart [2] 3. Copy [3] contents to clipboard 4. Edit > Paste in VICE. 5. Type `help` then `new`. [1]: https://vice-emu.sourceforge.io/ [2]: https://github.com/jkotlinski/durexforth/releases [3]: https://github.com/ekipan/sss/blob/share-hn/sss.fs $ wc README.md Design.md Tinkering.md 90 445 2928 README.md 823 4863 27985 Design.md 182 1029 6512 Tinkering.md 1095 6337 37425 total $ wc sss.fs # docs-to-code ratio >4:1! 284 1997 8680 sss.fs

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% 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: new, code · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, 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
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% 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
33%33% 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
16%16% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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