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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 variant glm-5.2[1m] in its own configuration. Each response can return up to 131,072 output tokens. That is roughly a 5x jump from GLM-5.1’s 200,000-token window. A 1M-token window changes how a coding agent works in practice. The agent can hold an entire mid-sized repository in working memory. That includes source files, tests, configuration, and conversation history. It avoids the constant summarization that smaller windows force. The release also adds two thinking-effort levels: High and Max. Z.ai recommends Max effort for complex, multi-step coding work. In Claude Code, the /effort command contro...

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 AI engineers, software engineers, and data scientists. Every code example follows a documented format and runs as written. Each item is labeled by status, so you know what ships with Claude Code and what does not. What is Claude Code Claude Code is Anthropic’s agentic coding tool. It works in the terminal, the desktop app, and your IDE. It can read files, run commands, edit code, and call external tools. Under the hood, it runs an agentic loop. That loop chooses tools, accumulates context, and manages long sessions through compaction. Safety boundaries come from permission modes, checkpoints, sandboxing, and managed settings. The s...

In this tu t orial , 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 MinHash-based near-duplicate detection, verify token counts with the GPT-2 tokenizer, and generate useful analytics on domains, language scores, document lengths, and tokenizer efficiency. Copy Code Copied Use a different Browser import subprocess, sys def pip(*pkgs): subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], check=True) pip("datasets>=2.19", "datasketch", "tiktoken", "pandas", "matplotlib", "tqdm") import re, math, random, collections from urllib.parse import urlparse ...

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 each harness as an interchangeable part of a larger system. Many engineers now juggle four or five agents at once. They copy text between coding agents, search tools, Docs, and Slack. Each harness only understands its own sessions. Omnigent adds a shared layer where composition, control, and collaboration live. What is Omnigent Omnigent is a common interface above command-line agents and agent SDKs. It wraps terminal coding agents such as Claude Code, Codex, and Pi. It also wraps SDKs like OpenAI Agents and the Claude Agents SDK. The design rests on one observation. However a harness calls its model internally, the user-facing interface...