If you’re training ML models on numerical workloads today, Python 3.13’s new JIT and Mojo 0.9’s native SIMD optimizations deliver a 5.2x speedup over Python 3.12 for matrix multiplication, with real cost implications for cloud training clusters. 🔴 Live Ecosystem Stats ⭐ python/cpython — 72,589 stars, 34,552 forks Data pulled live from GitHub and npm. 📡 Hacker News Top Stories Right Now Agents can now create Cloudflare accounts, buy domains, and deploy (143 points) StarFighter 16-Inch (168 points) .de TLD offline due to DNSSEC? (586 points) Industry-Leading 245TB Micron 6600 Ion Data Center SSD Now Shipping (22 points) Accelerating Gemma 4: faster inference with multi-token prediction drafters (515 points) Key Insights Mojo 0.9 delivers 5.2x faster 1024x1024 matrix multiplication vs Python 3.13 on same AWS c7g.4xlarge (Graviton3) hardware Python 3.13’s new JIT reduces numerical loop overhead by 42% compared to 3.12, but trails Mojo’s SIMD by 3.1x Switching a 100-node AWS training cluster from Python 3.12 to…