Agent Skills (I): How AI Learns to Act Like a Specialist

When people hear “AI agent,” they often imagine one super-smart system that can do everything. In reality, useful agents are built very differently.

They don’t rely on one giant brain doing everything at once. Instead, they use agent skills—small, reusable instructions that define how the agent should behave in specific situations.

Think of skills as mini playbooks for behavior.

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Ornith 1.0: A New Agentic Coding Layer on Top of Qwen and Gemma

Ornith 1.0 has recently gained attention in the open-source AI community as a new generation of “agentic coding models.” Early impressions often describe it as a standalone frontier model family. However, a closer look reveals a more nuanced and technically important reality.

Ornith 1.0 is not a new foundation model architecture. Instead, it is a post-training reinforcement learning system built on top of existing strong base models such as Qwen 3.5 and Gemma 4.

Its contribution is not in replacing foundation models, but in reshaping how they behave in software engineering environments—especially in multi-step coding, debugging, and terminal-based workflows.

This article breaks down what Ornith actually is, how it works, how it compares with models like Qwen3-Coder, and what it means for real-world systems such as Jetson-based AI agents.

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Raspberry Pi Zero 2 W vs. Pico 2 W

Choosing between the Raspberry Pi Zero 2 W and the Raspberry Pi Pico 2 W can be confusing. They share similar names, look like tiny green rectangles, and feature wireless connectivity. However, they belong to entirely different classes of computing.

The Raspberry Pi Zero 2 W is a Single-Board Computer (SBC) designed to run a full operating system. The Raspberry Pi Pico 2 W is a microcontroller, built to run a single piece of code directly on the hardware.

Choosing the wrong board can stall your project before it even starts. This guide breaks down their differences to help you choose the right tool for the job.

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