Zu

Zuni (YC S24) – AI Copilot for the Browser

Hacker News

Zuni (YC S24) – AI Copilot for the Browser

Hi HN, I'm Will, and along with my co-founder George, we've built Zuni ( https://zuni.app ) - a browser extension that adds contextual AI capabilities to your browser. It understands what you're reading and working on, whether that's email, research, or anything else in your tabs. We started out building a full email client with AI built in (you might have seen that version showcased in YC’s AI Design Review - https://www.youtube.com/watch?v=DBhSfROq3wU&t=1601s ), but learned that people don't actually want to leave their existing tools - they just want them to work better. Gmail might be frustrating, but it has years of features people rely on. So we pivoted to enhance the tools people already use, rather than replace them entirely. Some specific things Zuni does today: - Analyzes emails as you read them in Gmail, identifying action items and suggesting possible responses - Lets you discuss how to handle tricky emails, almost like having a thought partner - Maintains context across your browsing session so you can ask follow-up questions naturally - Runs locally first for speed and privacy - Doesn't store chats, emails or anything sensitive in the cloud We're still early and focusing on getting the core experience right before adding more integrations. The goal is to make AI actually useful in your daily work, rather than just another "AI feature" checkbox. Would genuinely love feedback from the HN community - what would make this truly useful for your workflow? What are we missing? Happy to answer any questions about the technical implementation too.

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Actual performance

17points
12comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: email, context, using · Missing: mac, agents, macos
94%94% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
52%52% 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 · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
34%34% 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
22%22% 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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