Pledge boards without singups

Hacker News

I made this web app to replace the "who brings what" and "who does what" lists on google docs for our upcoming kindergarten fest. The stack is react, drizzle, nextjs and postgres. I'm working on letting OpenAI generate initial content for the pledge board, based on title and description. One topic I want to explore is subtle AI integration that doesn't annoy users, like placeholder content in the input fields that fit the theme of the board. There are lots tweaks I have in mind for the future, like locking input fields when a user is entering their name, or encrypting the content with a passphrase. The landing page is still AI generated, which has a lot of fluff. I want to trim it down and give it some soul. Any feedback is welcome! Is this useful at all, or is google docs good enough?

Visit product View on Hacker NewsSep 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, openai · Missing: mac, agents, macos

78%78% 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.

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

48%48% predicted probability of success on TrustMRR, 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

44%44% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, 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.

Correct prediction on native model

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