Hybrid Groups – Agentic AI for human team collaboration [video]

Hacker News

Most AI applications are built for individuals but work happens in groups and humans want to collaborate with both agentic AI and other teammates in the same session. We created Hybrid Groups for that purpose. In Hybrid Groups, agents join group chats as virtual team members in Slack and GitHub. They participate in group conversations, proactively contribute when needed and perform actions on behalf of individual users, like managing your calendar for meeting suggestions or updating your todo list without sharing access to your private resources to the group. The project is open-source at https://github.com/gradion-ai/hybrid-groups and can be run locally. To try it: Run the Docker container, connect your Slack or GitHub, and use the predefined demo agents or create your own agents. More info in the quickstart: https://gradion-ai.github.io/hybrid-groups/quickstart/ . We're early in development and would appreciate your feedback on the platform, potential improvements and other ideas!

Visit product View on Hacker NewsJul 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, agentic · Missing: mac, macos, cursor

96%96% 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 · Strong signals: platform, users · Missing: plus, intuitive, reviews

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

53%53% 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

39%39% 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: video, users · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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, collaborate · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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