World Wire

A team from Google Research and USC has released Mobility-Embedded POIs (ME-POIs) , a framework that folds aggregate human movement into text-based place embeddings. The premise is that language models describe what a place is , but not how it is used . Two coffee shops can share a category, an address block, and a text vector, while one runs commuter turnover and the other holds customers for ninety minutes. ME-POIs encodes each visit as a contextualized vector, then uses contrastive learning to align those visits with one learnable prototype per POI. Across five map-enrichment tasks on Los Angeles and Houston mobility data, adding ME-POIs to strong text encoders improved 34 of 35 model-task pairings in Los Angeles, with relative gains up to 81.9% F1 on visit intent and a 24.7% MAE reduction on busyness. Notably, a variant trained on mobility alone beat Gemini embeddings on price-level classification. Is it deployable? Partially, it is a framework you rebuild, not a checkpo...

In this tutorial, we explore a LabPlot -inspired scientific data analysis workflow in Python while preserving the structure and terminology of LabPlot’s aspect tree, analysis kernels, plotting system, and project model. We build reusable components to import tabular data, compute descriptive statistics, smooth and differentiate signals, perform Fourier analysis and filtering, detect peaks, integrate curves, reduce data, and fit nonlinear models with detailed statistical diagnostics. We then apply these tools to a realistic spectroscopy example: removing periodic interference, identifying overlapping peaks, fitting a multi-Gaussian model, inspecting residuals, visualizing results through themed worksheets, exporting figures, and saving project data in LabPlot-compatible .lml-style files. Finally, we extend the same workflow to batch processing so we can analyze multiple temperature-dependent spectra and fit secondary trends across the resulting measurements. Copy Code Copied Use a ...

Frontier open-weight models are shipping faster than the hardware assumptions around them. Kimi-K3 , GLM-5.2 and DeepSeek-V4-Flash are closing the capability gap with proprietary systems, but releasing parameters only determines who can obtain a model — not who can afford to run it. Serving them still assumes datacenter-class GPU clusters, and as agentic workloads push inference demand up, that cost lands hardest on individual developers and small teams. Meanwhile, more than a hundred million consumer machines already carry discrete GPUs. A team of researchers from UC Berkeley and UT Austin propose FreeToken . The research team argued the missing piece is not hardware but a serving system: it treats a personal machine as a unified, elastic inference platform rather than a small GPU, and continuously maps computation and model state onto whatever GPU, CPU, memory and interconnect bandwidth the machine actually has. The result is a 35B model at interactive speed on an 8 GB laptop GP...