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Bucket – Feature flagging that's purpose-built for B2B

Hacker News

Bucket – Feature flagging that's purpose-built for B2B

Howdy HN! We're a team of 7 that have worked on Bucket for the past 2 years (Seed funded). How are we different than other feature flagging tools? * Purpose-built: Bucket is built for the B2B use case, which makes everything simpler for you. * Companies over users: In B2B, you mostly target customer accounts, not individual users. * Quality and craft: We're sweating the details so you can focus on releasing better features. * Product engineering: We've made it easy to get feedback and adoption metrics on new releases. * Entitlements: Easily manage feature access based on customer SaaS subscription. Would love to get your feedback on the product!

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Actual performance

8points
1comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos
82%82% 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 · Missing: supports, reddit linkedin, podcasting
82%82% 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
61%61% 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: users · Missing: plus, platform, intuitive
53%53% 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
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas, subscription · 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.

Correct prediction on native model

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