Br

Bravoboard.xyz – a collaborative place for all your kudos and gratitude

Hacker News

Bravoboard.xyz – a collaborative place for all your kudos and gratitude

Bravoboard is an online group card that allows you to create a board and share a unique link with contributors. Contributors can post messages on the board to celebrate a co-worker, friend or a family member. I built Bravoboard to scratch my own itch. My friends and colleagues use a competitor (anonymized intentionally) and while it's a great tool, the pricing is steep and the free tier is barely usable at just a few posts per board. The most basic version requires you to pay a decent amount, yet, there are multiple basic features behind a paywall. Judging by how often my friends use the platform and are forced to upgrade each time, I decided to spin up a platform that would enable them to have a cap-free experience at a fraction of the cost. Bravoboard has all the core features. Creators have full control over the board and can moderate any post on their boards. Bravoboards can be scheduled for delivery at a later date and reminders can be set for users to be reminded of upcoming events. Gifs are powered by GIPHY. A single Bravoboard with unlimited posts and unlimited users costs less than $1. This can run up to a lot with other leading platforms. While you're here, check out our - - see our samples (< https://www.bravoboard.xyz/#samples >) - live demo (< https://www.bravoboard.xyz/board/69d95f4f94bc4554b403c17e682... >) - 404 board (by accessing a random path e.g. < https://www.bravoboard.xyz/404 >) and leave us a post Twitter - < https://twitter.com/TeamBravoboard >

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, single · Missing: mac, agents, macos
53%53% 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: ide, io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
29%29% 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
13%13% 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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