Co

Comics Written and Drawn by AI

Hacker News

Comics Written and Drawn by AI

Hey, I made a website which outputs daily comics written and drawn by a machine. https://dailyaicomics.com I know it's fun to ask ChatGPT for a joke about X, but I wanted to take a step further by asking it for: 1. a comic in three parts 2. to include lines and a title 3. prompts to draw it I'm asking ChatGPT to output JSON with the comic and then I parse it and move it to the next step of the pipeline (drawing). Tools on their own are cool, but chaining them is the true multiplier of creativity. Check the source on Github: https://github.com/infomiho/dailyaicomics The hardest thing so far was figuring out how to get a bit more consistent style since each panel is draw in isolation. I've tried asking "draw in the style of X famous comic writer" but that had two drawbacks: - their style varies (color, black and white) - since it's a comic author, it oftentimes draws a whole comic (multiple panels) Now I'm being very explicit around the style (ink, pastel colors etc.) and it's okay. I'm wondering what's the best approach to get consistency?

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
52%52% 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.
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42%42% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, chatgpt · Missing: agents, macos, agent
32%32% 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: pipe, io · Missing: https docs, excited, just released
29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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