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Curiora.com – collaborative knowledge sharing for teams

Hacker News

Curiora.com – collaborative knowledge sharing for teams

Hi HN! I built Curiora to solve the problem of scattered knowledge and disconnected discussions within companies. It's essentially an internal Reddit-like platform that helps teams create focused communities, share valuable content, and surface the best insights through upvotes. Key features: - Create topic/team-specific communities within your organization - Share links and insights with clear descriptions - Upvote system to highlight valuable content - Threaded discussions and comments - Personalized feeds for different communities The main difference from existing solutions: - More engaging than traditional knowledge bases - More structured than chat/messaging apps - More focused than general social platforms - Built specifically for company use Tech stack: Nextjs, shadcn, Tailwind Pricing is simple: Starter: $9/mo (1 community, up to 10 members) Company: $15/mo (3 communities, up to 30 members) Enterprise: $25/mo (unlimited communities and members) I'd love to hear your thoughts, feedback and suggestions! What other features would make this more valuable for your team?

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps · 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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps · Missing: mobile apps, ios, entrepreneurs
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, lua, existing · Missing: https docs, excited, just released
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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