MiniPlay is a gamified cognition miniapp on @MiniPay. Enhance speed, logic, memory, focus, & social, through fun mini-games. built by @zenoVision_ | $SKILLX

MiniPay
Joined January 2025
Most game tokens pay for activity. We built the opposite. 🧠 40M+ games. ~350K players. 🎮 FairPlay’s ML models separate humans from farms at claim time. $SKILLX is how that integrity becomes an economy, not a faucet. 🫡
Bots don’t lose at games. They farm reward pools. So we stopped treating “anti-cheat” like a side quest. FairPlay is MiniPlay’s integrity ML engine, trained where the money is real. 🧵 👇
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Bots don’t lose at games. They farm reward pools. So we stopped treating “anti-cheat” like a side quest. FairPlay is MiniPlay’s integrity ML engine, trained where the money is real. 🧵 👇
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The model rediscovered the golden rule on its own: 'has paid' beat every other clean signal by 2×. We didn’t tell it money mattered. The data did. Gen 4 closes the loop: detectors flag → mods review → labels retrain the model. “Compounds over time”: 0% → 0% → 12% → 91% The longer it runs, the harder it gets to farm.
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This isn’t just for games. Grants. Airdrops. Waitlists. Any system paying humans. FairPlay started as MiniPlay defense. It’s becoming human-verification infrastructure. Skill > Volume. Always. miniplay.studio/fairplay 🕹️
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MiniPlay retweeted
Games that make @MiniPay real fun: 🎨 @Modeto by @GigaHierz 🕹️ ArcadeX by @AadarshArmil 📝 Nerdos by @camilosaka ⚡ @MiniPlayStudio by @Mbarrbosa ✅ @MiniQuizxyz 🧱 @PlayBlokaz by @TweetleHQ
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Zeno.Vision isn’t an agency. It isn’t a consultancy. It isn’t an investment fund. It’s a venture creation studio. We build products from first principles around one long-term thesis: Help AI understand and interact with the physical world.
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Gen 4 of MiniPlay's anti-sybil/bot model has been training on ~40M games played. KS separation: 0.982.
Claim-gate precision: 98.4%.
Recall: 92.8%. 310K+ accounts scored continuously. Here's a bit more of its progress 🧵
Big update on MiniPlay's Fair Play system. What started as bot/sybil detection for a crypto gaming platform is quietly becoming something much bigger. Here's the evolution 🧵
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Gen 1–3: 4 separate detectors, each calling quarantine directly. No coordination. Race conditions. False-positive risk at every path. Gen 4: detectors stamp a dirty flag only. One unified verdict engine reads the recomputed score and applies all policy gates exactly once. One write path. Auditable. Reversible.
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The model self-trains. AUC went 0.975 → 0.9944 across generations without rewriting the feature set. Near-perfect separation. Engagement beats identity.
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Big update on MiniPlay's Fair Play system. What started as bot/sybil detection for a crypto gaming platform is quietly becoming something much bigger. Here's the evolution 🧵
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This isn't a gaming problem. Grants. Airdrops. Waitlists. Content monetisation. Any system distributing value to humans faces the same non-human inception — bots, sybil farms, AI-generated personas competing against real people. We just happened to have one of the sharpest training grounds on the planet.
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The detection stack — four layers, self-improving, built on real production data from one of the hardest bot environments out there — is becoming its own thing. If you're building something that needs to know it's talking to a human, we're working on making this available. More soon.
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