We beat Google DeepMind but got killed by Zhipu AI

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Two months ago, my friends in AI and I asked: What if an AI could actually use a phone like a human? So we built an agentic framework that taps, swipes, types… and somehow it’s outperforming giant labs like Google DeepMind and Microsoft Research on the AndroidWorld benchmark. We were thrilled about our results until a massive lab (Zhipu AI) released its results last week to take the top spot. They’re slightly ahead, but they have an army of 50+ phds and I don't see how a team like us can compete with them, that does not seem realistic... except that they're closed source. And we decided to open-source everything. That way, even as a small team, we can make our work count. We’re currently building our own custom mobile RL gyms, training environments made to push this agent further and get closer to 100% on the benchmark. What do you think can make a small team like us compete against such giants? Repo’s here if you want to check it out or contribute: https://github.com/minitap-ai/mobile-use Our discord: https://discord.gg/6nSqmQ9pQs

Visit product View on Hacker NewsAug 20, 2025

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

93%93% 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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: month, google, way · Missing: mobile apps, ios, personal

64%64% 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

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

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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