An

An online guestbook interface that accepts drawings

Hacker News

An online guestbook interface that accepts drawings

When we built the guestbook on our company website [1] we received a lot of positive feedback and a big response on Hacker News [2]. Fast forward to today. We've since built a complete blogging and site platform called Pika [3]. We got to thinking that it would be great if people on our platform had the ability to experience this guestbook thing for themselves. While we couldn't send a printer out to everyone, we could make a completely digital, art-based guestbook for folks on our platform. The guestbook provides a drawing canvas in the browser. When the drawing is submitted, it is then converted to PNG file for display in the guestbook itself. Please give it a try! Note: Guestbook entries are not automatically posted to our guestbook. Give us a minute to approve them. (We will immediately delete all troll drawings and messages.) [1] https://guestbook.goodenough.us/ [2] https://news.ycombinator.com/item?id=37941234 [3] https://pika.page/

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: hacker news, ide · Missing: https docs, excited, just released
81%81% 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 · Strong signals: platform, interface · Missing: plus, intuitive, reviews
71%71% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
24%24% 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
11%11% 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.

Correct prediction on native model

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