Visit sintra.ai/leonvanzyl or use my promo code leonvanzyl at checkout to receive limited time offer of 72% off on all plans 🚀 Free resources: source code, prompts, workflows + peer discussion https://skool.com/leonvanzyl 🧪 Agentic Labs: AI coding courses, live Q&A, weekly builder challenges, direct access to me + a serious builder community https://skool.com/agentic-labs OpenCode is the open source coding agent that is quickly becoming a serious alternative to Claude Code. In this opencode tutorial, you will learn how to install OpenCode, connect free AI models through OpenCode Zen or premium providers like OpenAI, and master advanced features including agent skills, sub agents, MCP servers, and background agents. ⏱️ CHAPTERS: 00:00 - OpenCode intro and overview 00:39 - Installing OpenCode in seconds 01:25 - Connecting providers and OpenCode Zen 02:20 - Free AI coding models walkthrough 03:46 - Scaffolding a Next js project 04:55 - Subscriptions vs API keys 06:00 - Connecting OpenAI and GPT 5 07:30 - Installing agent skills from skills sh 10:30 - AGENTS md memory file setup 13:00 - Sub agents and context window protection 15:30 - Design system for consistent UI 15:50 - Sponsor message 18:00 - Building the AI Project Planner app 22:30 - Saving plans for future sessions 24:30 - Creating custom sub agents 28:30 - Playwright MCP server setup 30:30 - Auto testing the app with browser MCP Business & sponsorship enquiries: leon.vanzyl@gmail.com #opencode #agenticcoding

Claude Code Tutorial: Beginner to Advanced in 20 Minutes. Join our FREE community with free prompts & guides: https://www.skool.com/ai-launchpad/about Chapters: 0:00 - Intro 1:10 - What is Claude & Setup 3:54 - Plan mode & prompting 6:18 - First results & Iteration 8:18 - Claude.md & MCP 12:12 - Context management & extended thinking 16:10 - Deploy your builds 17:07 - Skills & Hooks & Routines 18:39 - Outro For business inquiries including sponsorships and collaborations, please email: business@zinhomedia.com

There's a lot of new terms for how you manage context with coding agents. I don't think it needs to be this complicated. Here's what you need to know, and some history on how we've gotten here. https://cursor.com/blog/dynamic-context-discovery https://cursor.com/docs/context/skills https://cursor.com/docs/context/subagents https://cursor.com/docs/context/rules https://cursor.com/docs/agent/hooks

Learn to use Anthropic's Claude Agent SDK (formerly Claude Code SDK) for AI-powered development workflows! https://platform.claude.com/docs/en/agent-sdk/overview https://x.com/trq212 **AI Summary** This workshop by Thariq Shihipar (Anthropic) details the architecture and implementation of the **Claude Agent SDK**. The session moves from high-level theory—defining "agents" as autonomous systems that manage their own context and trajectory—to a live-coding demonstration. Shihipar builds an agent "Harness" from scratch, implementing the core **Agent Loop** (Context Thought Action Observation), integrating the **Bash tool** for general computer use, and demonstrating **Context Engineering** via the file system to maintain state across long tasks. **Timestamps** 00:00 Introduction: Agenda and the "Agent" definition 05:15 The "Harness" concept: Tools, Prompts, and Skills 10:10 Live Coding Setup: Initializing the Agent class and environment 15:45 implementing the "Think" step: Getting the model to reason before acting 25:20 The Agent Loop: connecting `act`, `observe`, and `loop` 33:10 Tool Execution: Handling XML parsing and tool inputs 42:00 The "Bash" Tool: Giving the agent command line access 49:30 Safety & Permissions: "ReadOnly" vs "ReadWrite" file access 58:15 Context Engineering: Using `ls` and `cat` to build dynamic context 01:05:00 The "Monitor": Viewing the agent's thought process in real-time 01:12:45 Handling "Stuck" States: Feedback loops and error correction 01:21:20 Multi-turn Complex Tasks: Building a "Research Agent" demo 01:35:10 Refactoring patterns: "Hooks" and deterministic overrides 01:48:39 Q&A: Reproducibility, helper scripts, and non-determinism 01:50:31 Q&A: Strategies for massive codebases (50M+ lines) 01:52:00 Closing remarks and future SDK roadmap * **Evolution of AI Capabilities:** Shihipar argues we are shifting from **LLM Features** (categorization, single turn) to **Workflows** (structured, multi-step chains like RAG) to **Agents**. He defines agents as systems that *"build their own context, decide their own trajectories, and work very autonomously"* rather than following a rigid pipeline. * **The Claude Agent SDK Architecture:** The SDK is built directly on top of **Claude Code** because Anthropic found they were *"rebuilding the same parts over and over again"* for internal tools. * **The Harness:** A robust agent requires more than just a model; it needs a "Harness" containing Tools, Prompts, a **File System**, Skills, Sub-agents, and Memory. * **Opinionated Design:** The SDK bakes in lessons from deploying Claude Code, specifically the "opinion" that general computer use (Bash) is often superior to bespoke tools. * **The Power of the Bash Tool:** A key technical insight is that the **Bash tool** is often the most powerful tool for an agent. Instead of building custom tools for every action (e.g., a specific API wrapper for a file conversion), giving the agent access to the shell allows it to use existing software (like `ffmpeg`, `grep`, or `git`) to solve problems flexibly, similar to how a human developer works. * **Context Engineering:** Shihipar introduces the concept of **Context Engineering** via the file system. Instead of just "Prompt Engineering," the agent uses the file system to manage its state and context. * **Files as Memory:** The agent can write to files to "remember" things or create its own documentation (e.g., `CLAUDE.md`) to ground future actions. * **Verification:** The file system serves as a ground truth for the agent to verify its work (e.g., checking if a file was actually created). * **The Agent Loop & Intuition:** Building a successful agent loop is described as *"kind of an art or intuition"*. The loop generally follows a **Gather Context Take Action Verify Work** cycle. Shihipar emphasizes that this loop allows the agent to self-correct, a capability missing from rigid workflows. * **Strategies for Determinism (Hooks):** During the Q&A, a technique for controlling agent behavior is discussed: **Hooks**. * If an agent hallucinates or skips a step (e.g., guessing a Pokemon stat instead of checking a script), a hook can intercept the response and inject feedback: *"Please make sure you write a script, please make sure you read this data."* * This enforces rules like "read before you write" without retraining the model. * **Scaling to Large Codebases:** For massive codebases (50M+ lines), standard tools like `grep` or basic context window stuffing fail. * **Semantic Search Limitations:** Shihipar notes that while semantic search is a common solution, it is *"brittle"* because the model isn't trained on the specific semantic index. * **Solution:** He recommends good **"Claude MD"** files (context files) and starting the agent in a specific subdirectory to limit scope, rather than trying to index the entire 50M lines at once.

Get started with Codex, OpenAI’s coding agent, in this step-by-step onboarding walkthrough. You’ll learn how to install Codex, set up the CLI and VS Code extension, configure your workflow, and use Agents.md and prompting patterns to write, review, and reason across a real codebase. This video covers: 00:00 | Installing Codex (CLI + IDE) 04:00 | Setting up a repo and getting your first runs working 08:12 | Writing a great Agents.md (patterns + best practices) 14:14 | Configuring Codex for your environment 16:26 | Prompting patterns for more consistent results 27:02 | Tips for using Codex in the CLI and IDE 34:59 | MCP Configuration 46:50 | Advanced workflows: headless mode + SDK 52:02 | Codex resources to get you started Sign up: https://openai.com/codex/ Codex overview + docs: https://developers.openai.com/codex Codex Cookbook: https://cookbook.openai.com/topic/codex Install + setup VS Code extension: https://marketplace.visualstudio.com/items?itemName=openai.chatgpt Agents.md standard: https://agents.md Agents.md repo: https://github.com/agentsmd/agents.md Prompting + workflows Prompting guide: https://developers.openai.com/codex/prompting/ Exec plans (Agents.md patterns): https://cookbook.openai.com/articles/codex_exec_plans Config reference: https://github.com/openai/codex/blob/main/docs/config.md#config-reference Updates Changelog: https://developers.openai.com/codex/changelog Releases: https://github.com/openai/codex/releases

Learn how to use Claude Code in your development workflow. Learn about prompting, CLAUDE.md files, context, MCP servers, subagents and more.