Understanding Short-Term Memory, Long-Term Memory, and Working Memory in AI Agents Imagine meeting someone for the first time. During your conversation, you remember their name,…
Agent Skills (IV): Skill Composition-How Multiple Skills Work Together to Solve Complex Tasks
Understanding How AI Agents Solve Problems That No Single Skill Can Handle In the previous articles, we learned that: But many real-world tasks are too…
Agent Skills (III): Designing Effective Agent Skills
How to Define Clear Skill Boundaries for Smarter AI Agents Imagine hiring employees for a company. Would you hire one person whose job description is:…
Agent Skills (II): How AI Agents Choose the Right Skill
A simple guide to agent routing logic (without the complexity overload) An AI agent becomes useful not just because it has skills—but because it knows…
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.
Installing Qdrant on Jetson or Linux for Local AI Systems
Building a high-performance vector memory engine for AI agents, RAG systems, and local LLM workflows 📌 Overview Qdrant is a high-performance vector database designed for…
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.
Qwythos-9B Review: Exploring the 1M Context Open-Source Reasoning Model
Qwythos-9B: Bringing Million-Token Reasoning to Open-Source AI A Deep Dive into Empero AI’s Long-Context Reasoning Model Built on Qwen3.5-9B Introduction The race toward more capable…
Tailscale, NetBird, Netmaker, and ZeroTier: Building Secure Private Networks for AI, IoT, and Homelab Systems
A Practical Guide to Modern Mesh VPNs for Raspberry Pi, Jetson, Local LLMs, and Remote Access Introduction As more developers, makers, and AI enthusiasts deploy…
Practical Process to Develop a Simple LSTM Time Series Prediction Model
Univariate Model for One-Step Ahead Prediction In this tutorial, we have explored how to use LSTM (Long Short-Term Memory) to predict time series using a…
