Qordinate – AI that talks for you (coordination-first, early build)

Hacker News

Hey HN - founder here. I'm building Qordinate with a simple (big) idea: Most AI assistants talk to you. I want one that talks for you - one that can share, negotiate, and coordinate on my behalf with others. Reality: we're early. Today, Qordinate can: - send clean pings on your behalf (with your approval) - collect info/files via tiny forms and track replies - nudge people politely and close the loop with you - do basic actions via connectors (Gmail/Calendar/GitHub/Linear/Drive/Slack) - keep lightweight lists in chat (tasks, contacts, logs) Why I'm doing this The real pain isn't knowing what to do, it's getting others to do their part. Coordination is the tax. I want to cut that. Where I'm unsure - how far "talks for you" should go before it's annoying or risky - what approval flow feels safe but not heavy - which coordination templates (collect docs, pick a time, chase invoices, etc) you'd actually use Roadmap - better multi-party scheduling If you're curious: - https://qordinate.ai/ I know less than many of you here. Happy to be told what's dumb, missing, or risky.

Visit product View on Hacker NewsOct 31, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, tiny, tasks · 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

68%68% predicted probability of success on Indie Hackers, 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

36%36% 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 · Missing: mobile apps, ios, personal

32%32% 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

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

28%28% predicted probability of success on AppSumo, 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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