I have made a comic book using only code

Hacker News

Hi HN, I am making an online comic book that respects all of the HN rules of what a good website should be : no JS, no tracking. I have even gone a step further : no assets and no dependencies. One can do really cool things using HTML and CSS only. Please visit at least two pages for the full experience by clicking on 次 at the bottom. The text is in Japanese please forgive me for this and enjoy the concept more than the content. Also if you are a Japanese speaker, the text has not been proofread and likely contains language mistakes) To create the illustration I use two tools. With https://www.pixelartcss.com/ I create CSS from video game tiles I use the following to create the illustrations from the tiles : https://www.thecodingfox.com/interactive/pixel-world-editor/

Visit product View on Hacker NewsMar 27, 2022

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

87%87% 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.

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

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

43%43% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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