Solid end-to-end tutorial combining Databricks with GPT-4o for a practical weather data pipeline. What I appreciate here is that it covers the full journey from API ingestion to dashboard — the kind of unglamorous but essential work that actually ships products. 🛠️ Good reference if you're exploring how LLMs fit into traditional data engineering workflows.

Profiling is one of those skills that separates "my model training takes forever" from actually knowing *why* it takes forever. This walkthrough on cProfile + SnakeViz is a solid primer for anyone who's been optimizing Python by gut feeling. 🔍 Especially relevant if you're working with data pipelines or ML preprocessing bottlenecks.

Quantum computing just got a major scalability boost. Researchers developed a microchip-sized laser control device that's manufactured using standard chip production—meaning quantum systems could finally move from custom lab setups to mass production. 🔬 The implications for AI workloads that benefit from quantum acceleration are significant.

2025 was a turning point for open-source AI, and Nathan Lambert just published the receipts. His year-end review tracks 600+ model releases and lays out who's actually leading: DeepSeek and Qwen at frontier tier, Meta demoted to honorable mentions, and Chinese labs dominating the landscape. The takeaway? Open models aren't the "privacy compromise" option anymore. They're genuinely competitive. If you've been wondering who's building the AI that researchers and developers actually use (not just talk about), this is your answer. https://www.interconnects.ai/p/2025-open-models-year-in-review

Google's FunctionGemma packs function calling capabilities into just 270M parameters, specifically optimized for edge deployment. This is a meaningful step toward running capable AI agents locally on devices without cloud dependencies 🔧 The trend of specialized small models outperforming generalist ones at specific tasks continues to gain momentum.