Zarvia – Visual workspace rethinking how teams organize information

Hacker News

I built Zarvia to rethink how teams collaborate — beyond scattered chats and documents. After years of frustration with messy group work and disconnected tools, I wanted a more structured, visual way to keep team communication and shared knowledge in sync. Zarvia introduces a new paradigm: a shared visual workspace where chats, files, and links live together in a flow-style folder map. It gives fast-moving teams clarity and context — without the noise or complexity of traditional setups. It’s early-stage and free to try: https://zarvia.co — would love to hear your thoughts.

Visit product View on Hacker NewsJun 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, context, visual · Missing: mac, agents, macos

83%83% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

62%62% predicted probability of success on AppSumo, 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

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

nativeThis product was originally launched on this platform.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting

37%37% predicted probability of success on Indie Hackers, 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 · Strong signals: chat, collaborate, introduce · 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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