This article shows how macOS 27 includes a built-in LLM accessible via /usr/bin/fm that runs entirely offline and needs no account. The author tests it on an M1 MacBook Air, noting its limited memory, occasional inaccuracies, and a range of Terminal-based tricks.

• macOS 27 includes an offline, account-free LLM ("Apple Foundation Models") accessible via /usr/bin/fm in Terminal
• Runs smoothly on a base M1 MacBook Air with 8GB RAM, though it has limited memory and no awareness of current dates
• Useful for practical tasks like drafting emails, translation, summarizing Markdown, generating regex/SQL, and shell scripting help
• Prone to hallucinating dates or inventing functions when pushed beyond simple tasks

Claude Code is a command-line AI agent that reads, edits, and runs code and files on your computer based on plain English prompts. It handles everything from file management and data gathering to custom workflows, with built-in tools for permissions, version control, and session memory.

• Claude Code executes actions (editing files, running commands, installing packages) directly in your terminal instead of just chatting about code, pausing for approval on risky actions unless pre-approved via settings.local.json or blocked via a deny list.
• Context window fills up with conversation/file history, so at 85–95% capacity you need /compact (summarize) or /clear (reset), and billing is token-based (~0.75 words/token) tracked via /cost.
• You can switch models on the fly with /model (Haiku for speed/cost, Sonnet as default, Opus for max power), and CLAUDE.md plus automatic memory persist project-specific context and preferences across sessions.
• Customization extends via slash commands, on-demand "skills," and hooks that trigger background scripts (like auto-formatting), giving fine-grained control over autonomy and tool access.

The article reviews significant trends and developments in the LLM space throughout 2025, highlighting breakthroughs in reasoning, the rise of coding agents, and the increasing use of LLMs in command-line interfaces. It notes the evolution of tools and models, including the impact of asynchronous coding agents and the normalization of YOLO mode for improved efficiency.

• Reasoning models became mainstream in 2025, significantly boosting LLM performance on complex tasks
• Coding agents surged in popularity, with asynchronous agents enabling developers to offload larger chunks of work
• "YOLO mode" (letting agents run commands without manual approval) went from risky novelty to normalized practice for efficiency
• LLMs increasingly moved into command-line interfaces, embedding AI more directly into developer workflows

Explore around 30 pro-tips for maximizing the efficiency of Gemini CLI, an open-source AI assistant designed for command-line use. The guide covers setup instructions, essential features, and advanced techniques for coding, debugging, and automating tasks through natural language prompts.

• Gemini CLI is a free, open-source command-line AI assistant, and the repo compiles ~30 practical tips for using it effectively in coding workflows
• Covers concrete techniques like setting up context/config files, using custom slash commands, and chaining prompts for debugging and automation
• Focuses on efficiency tricks (e.g., scripting repetitive tasks, managing context windows) rather than just basic feature explanations