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Meet Flash-KMeans: An IO-Aware, Exact K-Means That Runs Over 200× Faster Than FAISS on GPUs

k-means has been an offline tool for decades. You run it once to preprocess data, then move on. A team of researchers from UC Berkeley and UT Austin released Flash-KMeans, a new open-source library that targets a different setting. Modern AI pipelines now call k-means inside training and inference loops. At that frequency, latency per […]

Meet Flash-KMeans: An IO-Aware, Exact K-Means That Runs Over 200× Faster Than FAISS on GPUs Read More »

Z.ai Launches GLM-5.2 With a Usable 1M-Token Context, Two Thinking-Effort Levels, and No Benchmarks at Launch

GLM-5.2 is the latest large language model from Z.ai, becoming the third major release in the GLM-5 line. It follows GLM-5 (February 11), GLM-5-Turbo (March 15), and GLM-5.1 (April 7). That makes four flagship-tier coding releases in roughly four months. Usable 1M-Token Context Window GLM-5.2’s standout spec is a 1,000,000-token context window. Z.ai labels the

Z.ai Launches GLM-5.2 With a Usable 1M-Token Context, Two Thinking-Effort Levels, and No Benchmarks at Launch Read More »

Claude Code Guide 2026: 25 Features with Examples + Demo

Claude Code started as a terminal coding assistant. It now runs as a layered agentic system. Underneath, Claude Code separates memory, hooks, skills, subagents, plugins, and MCP into distinct layers. Each layer changes what the model can see or do. This article covers 25 features and strategies for scaling Claude Code. It is written for

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A Coding Hands-On on FineWeb for Streaming, Filtering, Deduplication, Tokenization, and Large-Scale Web Corpus Analytics

In this tutorial, we explore the FineWeb dataset through an advanced hands-on workflow. We stream a manageable sample of the dataset without downloading the full multi-terabyte corpus, inspect its schema and metadata, and analyze key fields such as URL, language, language score, and token count. We also reproduce simplified versions of FineWeb’s quality-filtering pipeline, apply

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Databricks Open-Sources Omnigent: A Meta-Harness That Composes, Governs, and Shares AI Agents Across Claude Code, Codex, and Pi

Databricks released Omnigent, an open source ‘meta-harness’ for AI agents. The project ships under the Apache 2.0 license. The Databricks AI team built it with Neon. A harness is the wrapper around a model that turns it into an agent. Claude Code, Codex, and Pi are harnesses. Omnigent sits one level above them. It treats

Databricks Open-Sources Omnigent: A Meta-Harness That Composes, Governs, and Shares AI Agents Across Claude Code, Codex, and Pi Read More »

How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing

In this tutorial, we implement a QwenPaw workflow that provides a practical environment for building and testing an agent-powered assistant. We install and initialize QwenPaw, configure its working directory, set up authentication, connect optional model providers via Colab secrets, and create a structured workspace with custom skills and local knowledge files. We also launch the

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Anthropic Disables Claude Fable 5 and Mythos 5 After US Government Order

Anthropic has disabled its two most capable models for every customer. The shutdown followed a US government export control directive. The order arrived on June 12, 2026. It named Claude Fable 5 and Claude Mythos 5 specifically. Both models had launched only three days earlier, on June 9. The directive cited national security authorities, according

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Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6

This week, Moonshot AI released Kimi K2.7-Code. It is a coding-focused, agentic model. The model weights ship on Hugging Face under a Modified MIT license. You can also reach it through the Kimi API and Kimi Code. K2.7-Code targets long-horizon software engineering, not general chat. It plans, edits, runs tools, and debugs across many steps.

Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6 Read More »

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A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric

In this tutorial, we build an end-to-end spatial graph learning pipeline using city2graph. We start by collecting real urban POI data and street network information from OpenStreetMap, with a synthetic fallback to ensure the workflow remains reliable. We then engineer spatial features, construct multiple proximity graph families, and compare how different graph-building strategies represent the

A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric Read More »

Google Releases Gemini-SQL2: Gemini 3.1 Pro Text-to-SQL Scores 80.04% on BIRD Single-Model Leaderboard

Google Research team has announced the launch of Gemini-SQL2 on X. They described this system as a breakthrough text-to-SQL capability powered by Gemini 3.1 Pro. Gemini-SQL2 posted 80.04% execution accuracy on the BIRD Text-to-SQL Leaderboard (Single Model). Google’s chart places it above its own Gemini-SQL, the prior top entry. The metric measures whether generated SQL

Google Releases Gemini-SQL2: Gemini 3.1 Pro Text-to-SQL Scores 80.04% on BIRD Single-Model Leaderboard Read More »