An open-source app to query 10 AI models at once

Hacker News

Hey HN, My workflow for any complex queries is to ask it in multiple AI chats (Gemini, Claude, o3,..) in parallel and then continue the conversation with the chat response that I found the most useful. I built a simple open source app that queries 10+ AI models at once and summarizes their answers with a selected combiner AI model. There's a GIF in the github repo that shows it in action. You can try it on your local machine: https://github.com/Nexarithm/multi_model_chat If you are interested, I also made a detailed blog post on technical details, feature of the personal helper sites, and vibecoding limitations: https://www.proxai.co/blog/archive/multi-model-ai-chat-app I'd love to hear your feedback. Feel free to open an issue or a pull request on GitHub!

Visit product View on Hacker NewsJun 26, 2025

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

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

64%64% 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: open source, io · Missing: https docs, excited, just released

48%48% 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: personal, answers, para · Missing: mobile apps, ios, entrepreneurs

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

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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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