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In this tutorial, we implement an advanced hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for writing efficient CUDA-style kernels directly in Python. We start by preparing a Colab-friendly environment, checking the available GPU, driver, CUDA, and cuTile installations before running any kernel code. We then build tiled examples for vector addition, matrix addition, and matrix multiplication, while keeping a PyTorch fallback. Hence, the notebook remains executable even when Colab does not meet cuTile’s latest runtime requirements. Through this approach, we understand how tiled programming works, how tensors are loaded, computed, stored, and validated, and how custom GPU kernels can be compared against standard PyTorch operations. Setting Up NVIDIA cuTile Python and Checking GPU, CUDA, and Driver Runtime in Colab Copy Code Copied Use a different Browser import os import sys import math import time import json import shutil import subprocess...

A new working research from Perplexity and Harvard offers field evidence on what AI agents do to knowledge work. It draws on production data from two Perplexity products: Search and Computer. The setup is a natural comparison. Search is a conversational answer engine. Computer is an agent that plans and executes tasks end to end. The same users touch both products, so the team can hold the task roughly constant. What the Study Actually Measures The research study covers a 90-day window, February 27 through May 27, 2026. Computer launched two days before that window opened. The core method matches near-identical query pairs across the two products. The research team found 10,000 session pairs with cosine similarity above 0.99. Each pair is effectively the same task attempted both ways. Computer pairs are gated to sessions that invoke an execution tool. These ‘do’ tools include code execution, browser actions, file writes, and connector calls. That gate en...

In this tutorial, we use the ClawHub Security Signals dataset to examine how different security scanners assess AI skills and related files. We load the dataset directly from the Hugging Face Parquet conversion to avoid compatibility issues with newer dataset metadata, then inspect the main columns, verdict distribution, scanner outputs, and severity labels. After exploring scanner disagreement and overlap patterns, we build a practical machine learning pipeline that combines SKILL.md text with numerical scanner signals to predict the final ClawScan verdict. It gives us a complete workflow for loading, analyzing, visualizing, and modeling security signal data in a Colab-ready environment. Setting Up the Colab Environment and Imports for Security Signal Analysis Copy Code Copied Use a different Browser !pip -q install -U "huggingface_hub>=0.23" pyarrow scikit-learn pandas numpy matplotlib seaborn import warnings, numpy as np, pandas as pd warnings.filterwarnings(...

Inference speed is becoming a competitive metric for large language models. Xiaomi’s MiMo team just released MiMo-V2.5-Pro-UltraSpeed, built in collaboration with the TileRT systems group. It decodes faster than 1000 tokens per second on a 1-trillion-parameter model. Xiaomi team describes this as a first at trillion-parameter scale. Demos show generation peaks near 1200 tokens per second. The notable part is the hardware: it runs on commodity GPUs, not custom silicon. What is MiMo-V2.5-Pro-UltraSpeed UltraSpeed is a high-speed serving mode for the existing MiMo-V2.5-Pro model. The base model uses a Mixture-of-Experts (MoE) architecture at trillion-parameter scale. UltraSpeed targets generation speed rather than model capability. It changes how fast the model produces output tokens. The speedup comes from three coordinated techniques across the model and the serving system. Xiaomi calls this approach extreme model-system codesign. Crucially, the entire stack runs on a sing...

Google Research team has introduced a new agentic RAG framework. It is built into the Gemini Enterprise Agent Platform. It powers a feature called Cross-Corpus Retrieval, now in public preview. The target is a known failure mode in enterprise search. Standard single-step RAG was not built for multi-source, multi-hop queries. Ask “What are the specs of the server used in Project X?” The system may find a document naming a server ID. It will not know to take that ID and search a second database for specs. The answer comes back partial, or as “not found.” What is Google’s New Agentic RAG Agentic RAG plans, reasons, and iteratively interacts with data sources. It handles complex queries to increase dependability and accuracy. Google’s version is the Gemini Enterprise Agent Platform-hosted Cross-Corpus Retrieval powered by Agentic RAG. Like other multi-agent RAG frameworks, it uses agents that work together. Unlike them, it adds a sufficient c...