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Apple has started replacing macOS version names with version numbers instead across several support pages and more.

NimConf 2026 will take place on June 20th at 11am UTC. Streamed live and for free from YouTube.

Connect your databases, documents, Google Drive, Notion etc. so your AI systems pull the right data, stop hallucinating, and deliver results you can actually trust.

Hey all, I was scrolling through the latest launches on pump.fun and noticed a token ticker called GWIFS. WTF is this lol submitted by /u/AugmentedGlobal [link] [Kommentare]

🏇 Open-Source Hong Kong Horse Racing ML Pipeline — Feedback Welcome Hi everyone, I've been working on an open-source horse racing prediction project focused on Hong Kong Jockey Club (HKJC) data. 📦 Repo: catowabisabi/horse-racing-model-training 🌐 Live Dashboard: catowabisabi.github.io/horse-racing-model-training 🎯 Goal The goal is not to claim "AI can beat horse racing", but to build a reproducible ML pipeline and test whether there is any measurable edge after controlling for leakage. 📦 What's Included LightGBM and XGBoost training pipeline Feature engineering from HKJC historical race data With-odds and no-odds model comparison Ensemble predictions Kelly Criterion simulation Quinella, QPL, Tierce, Quartet betting simulations Out-of-sample validation HTML report dashboard Unit tests for betting math, DB schema, and odds merge logic 📊 Headline Result The interesting finding: the no-odds model outperformed the with-odds model for quinella ROI. My interpretation is that public odds already price favourites quite efficiently, while the fundamental model may still catch some mispriced combinations. 🙋 Feedback I'm Looking For Does the validation setup look clean? Better ways to avoid leakage? Are the betting simulation assumptions reasonable? Ideas for improving feature engineering? Would a ranking / listwise model make more sense than independent horse-level classification? If you find the project useful or interesting, a ⭐ GitHub star would really help me keep building it. Thanks! submitted by /u/Marshallmatta [link] [Kommentare]

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The VNX+ Development Platform accelerates demonstration and development of a complete self-contained RF payload capable of hosting AI/ML applications.