A small code change can lead to a lot of extra work when a project has broad instructions. You ask Codex to fix a button label, but AGENTS.md tells it to read the database guide, study the deployment process, and run every check three times.

Codex CLI sessions are easy to manage when only one task is active. The workflow becomes less predictable when several sessions operate in different repositories or terminal tabs. A task may be blocked on an approval while another is still running and a third is ready for review.

DeepSeek Harness is often described as an open-source coding agent. That description is correct, but incomplete. The more interesting part is its architecture. DeepSeek Harness is a configurable runtime for constructing agents from model adapters, tools, session services, execution backends, permission policies, interfaces, and agent loops.

Developer tools are often introduced through documentation, release notes, and online demos. That is enough to explain features, but it is not always enough to help people use a new tool in a real project.

Hey devs! When you use Codex, putting too many tasks into a single conversation makes the model slow, confused, and unreliable. To solve this problem, I highly recommend using subagents (or Custom Agents).

AI coding assistants are becoming better every day. But most still work one prompt at a time. You ask something, get an answer, and then guide the next step manually.

Google recently released the Agentic CLI, a powerful tool that makes building, testing, and deploying AI agents faster and more intuitive. I think it’s super useful, so I’ll walk you through the entire lifecycle of an AI agent using the CLI.

In this article, I’ll show you how to unlock the full power of GitHub Copilot agents inside VS Code. There are actually three main types of Copilot agents-most people only know about one, but I’m going to show you all of them. By the end, you’ll be able to create custom agents that act as your own specialized “sub-agents.”

GitHub Copilot CLI brings Copilot directly into your terminal. You can ask questions, understand a project, write and debug code, review changes, and interact with GitHub without leaving the command line.

Every week, I watch tutorials, save articles, bookmark tools, and collect ideas I want to come back to later. But a few days later, the problem shows up: I may remember the topic, but not the details. I know I saw something useful, but I cannot explain it clearly or apply it with confidence.

Imagine having a personal AI agent running on your computer. It can read files, run commands, automate tasks, and remember your workflows. In this guide, you will learn how to run OpenClaw with Ollama locally and choose the best local LLM models.

If you are a new developer, you have probably hit the “subscription wall.” You have $20 a month to spend on an AI coding assistant, but you don’t know which one to pick.

AI app builders have become very good at generating interfaces. From a technical perspective, that part is mostly solved. The harder problem is what comes next: connecting real data, enforcing user permissions, handling workflows, and deploying the app after the generation.

This guide covers how to set up OpenClaw (formerly Clawdbot) on your local machine and, most importantly, how to secure it so strangers can’t access your computer. If you are ready, then let’s get started! :)

In January 2026, Ollama added support for the Anthropic Messages API, enabling Claude Code to connect directly to any Ollama model. This tutorial explains how to install Claude Code, pull and run local models using Ollama, and configure your environment for a seamless local coding experience.

Recently, I started using Session Teleportation in Claude Code. It allows you to move an entire conversation, including context, history, and the working branch, between the web and your local terminal.

In this tutorial, I show you how it works and how to use it to make your workflow seamless.

If you are using the Codex CLI and find yourself writing the same instructions over and over again, you are not using the tool to its full potential. Codex offers a powerful feature called Skills that allows you to package reusable workflows and give your AI agent new capabilities on demand. If you want to know more about it, then read this article until the end.

Ollama has become the standard for running Large Language Models (LLMs) locally. In this tutorial, I want to show you the most important things you should know about Ollama.

There are now so many AI tools for coding that it can be confusing to know which one to pick. Some act as simple helpers (Assistant), while others can do the work for you (Agent). This guide breaks down the top AI coding tools that you should be aware of. We will look at what they do, who they are for, and how much they cost.

Have you ever been in the middle of a long coding session with an AI, only to lose everything because of a network glitch, a dead battery, or an accidental terminal close? It’s frustrating to start over from scratch.