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Today on Pulse on AI: we dig into India’s agrovoltaics boom—stacking solar panels above crops—and how AI-driven modeling makes it viable for shade-tolerant crops while optimizing energy yields and irrigation savings. We revisit why data visualization is the crucial bridge from insights to decisions, with practical tips to keep visuals clear and honest. Next, Python meets Mojo: when offloading tight loops to Mojo beats NumPy, when it doesn’t, and how to minimize interop overhead.We break down Alibaba’s open-source Tongyi DeepResearch agent—Mixture-of-Experts architecture, on-policy RL, and test-time scaling—plus why Apache-2.0 code and weights matter for on-prem deployments. Then, IBM and ETH Zürich’s Analog Foundation Models: a training recipe that makes LLMs robust to analog in-memory computing noise, with surprising benefits for low-precision digital too. We zoom out to China’s rapid manufacturing loop and what it means for robotics and AI hardware iteration. We cover ManticAI’s top-10 finish in the Metaculus Cup and why hybrid human+AI forecasting is rising. Finally, we discuss reports that OpenAI is partnering with Apple supplier Luxshare on a pocket-size, context-aware AI device—what it could be good for, and the real design challenges.Three takeaways: combine models with real-world constraints; open, reproducible agent pipelines are becoming deployment-ready; and performance is expanding beyond GPUs via Mojo and analog-robust training.
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By Max DreyfusToday on Pulse on AI: we dig into India’s agrovoltaics boom—stacking solar panels above crops—and how AI-driven modeling makes it viable for shade-tolerant crops while optimizing energy yields and irrigation savings. We revisit why data visualization is the crucial bridge from insights to decisions, with practical tips to keep visuals clear and honest. Next, Python meets Mojo: when offloading tight loops to Mojo beats NumPy, when it doesn’t, and how to minimize interop overhead.We break down Alibaba’s open-source Tongyi DeepResearch agent—Mixture-of-Experts architecture, on-policy RL, and test-time scaling—plus why Apache-2.0 code and weights matter for on-prem deployments. Then, IBM and ETH Zürich’s Analog Foundation Models: a training recipe that makes LLMs robust to analog in-memory computing noise, with surprising benefits for low-precision digital too. We zoom out to China’s rapid manufacturing loop and what it means for robotics and AI hardware iteration. We cover ManticAI’s top-10 finish in the Metaculus Cup and why hybrid human+AI forecasting is rising. Finally, we discuss reports that OpenAI is partnering with Apple supplier Luxshare on a pocket-size, context-aware AI device—what it could be good for, and the real design challenges.Three takeaways: combine models with real-world constraints; open, reproducible agent pipelines are becoming deployment-ready; and performance is expanding beyond GPUs via Mojo and analog-robust training.
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