World Wire

In this tutorial, we explore TileGym GPU programming by building a practical Colab workflow that runs across different hardware conditions. We begin by probing the available CUDA environment, checking whether NVIDIA cuTile runs directly, and falling back to Triton when standard Colab GPUs lack the required cuTile stack. Through this setup, we learn the core tile-programming idea: instead of writing code for one thread at a time, we operate on entire data tiles, load them into the kernel, compute on them efficiently, and store the results back. We use this model to implement vector addition, fused GELU, row-wise softmax, tiled matrix multiplication, and flash attention, while comparing each result against PyTorch for correctness and benchmarking. CUDA Environment Probe Copy Code Copied Use a different Browser import os, sys, math, time, textwrap def rule(t=""): print("\n" + "=" * 78) if t: print(t) print("=" * 78) rule("0....

Robbyant, the embodied AI unit inside Ant Group, has released the LingBot-VA 2.0 .The first embodied-native foundation model. It describes a video-action foundation model for generalist robot manipulation. The research team pretrains the whole stack for embodiment instead of fine-tuning a video generator. What is LingBot-VA 2.0? Most video-action models reuse two components built for digital content creation. One is a reconstruction-oriented VAE. The other is a bidirectional video-diffusion backbone, with an action module attached. This creates three limitations. Pixel-reconstruction latents preserve appearance but carry limited physical structure. Iterative denoising over video tokens is too slow for closed-loop control. Generic video objectives never teach how actions reshape the world. A fourth mismatch is structural. Backbones use bidirectional attention, while control unfolds strictly forward in time. LingBot VA Version 1.0 finetuned that stack into a causal model...

In this tutorial, we build an autonomous data science agent around DeepAnalyze-8B and run it. We begin by preparing a stable runtime, installing the required machine-learning dependencies, and loading the DeepAnalyze tokenizer and model in 4-bit mode to keep the workflow practical on limited GPU memory. We then create a sandboxed execution environment that allows the model to generate Python code, execute it safely, observe the results, and continue its analysis in an agentic loop. By the end of the workflow, we give the agent a realistic multi-file e-commerce workspace and let it clean, join, analyze, visualize, and summarize the data as a structured analyst-grade report. Installing DeepAnalyze-8B Runtime Dependencies Copy Code Copied Use a different Browser import os, sys, subprocess os.environ["MPLBACKEND"] = "Agg" def _pip(*args): subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", *args]) _SE...

Most wearable health models are built one outcome at a time. That approach breaks down at thirty-five endpoints. Labels are expensive and retrospective annotation is infeasible. Google Research introduced SensorFM, a foundation model for wearable health pre-trained on more than 1 trillion minutes of sensor data from 5 million people. https://ift.tt/VZrNcgC What is SensorFM? SensorFM is a Large Sensor foundation Model for wearable time-series representation learning. It ingests 34 one-minute aggregate features drawn from five sensors: PPG, accelerometer, EDA, skin temperature, and altimeter. Those features are organized into seven categories, over a 24-hour context window. The backbone is a ViT-1D encoder trained with a masked-autoencoder objective and a patch size of [20, 1]. Pretraining used 5,000,000 consented participants, sampled between September 2024 and September 2025. That corpus spans 100+ countries, all 50 U.S. states, and 20+ Fitbit and Pixel Watch mode...