Build tiny AI apps by chaining prompts together

Hacker News

Hi HN, I'm a solo bootstrapped founder from England who has been launching tiny projects on the internet for the past few years. I've written about my projects here: https://tinyprojects.dev PromptBase it the 8th project I've built - I launched it a year ago as a platform for creators to sell their prompts. Today I released a no-code editor that lets people build tiny AI apps by chaining together prompts - it's a bit like Zapier but for prompts. You can use it to create AI automations without having to mess around with connecting up APIs. Some use cases are building apps to easily generate designs / prints to sell elsewhere. It supports numerous models and AI tools (e.g. background removers, image upscalers) that you can chain together. It's free to try - I would love some feedback if you get a chance to give it a go!

Visit product View on Hacker NewsJan 31, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, models · Missing: mac, agents, macos

94%94% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created

88%88% 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

66%66% 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 · Strong signals: apps · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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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