How to Build ANYTHING with Oz by Warp

AI Agents: Revolutionizing Productivity

The Challenge of Keeping Up with AI News

• The speaker expresses frustration about the rapid pace of AI news, noting that by the time they check updates, many announcements are already outdated.

• They propose a solution involving a team of AI agents that would work overnight to research and rank AI stories, draft tweets, and update a live dashboard.

Introduction to Oz

• The speaker introduces Oz as a cloud coding agent platform developed by Warp, which allows users to deploy multiple AI agents in the cloud rather than on local machines.

• Key features include orchestration capabilities where agents can be scheduled to run autonomously or triggered via platforms like Slack or GitHub.

Unique Features of Oz

• Cloud Environments: Agents operate in isolated Docker containers, preventing local machine slowdowns from running multiple agents simultaneously.

• Scheduling: Users can set specific intervals for agent tasks, allowing them to receive results without being present at their computers.

• Steering Capability: Users can intervene mid-task if necessary, adjusting agent actions through Warp or web interfaces.

Creating Skills for AI Agents

• The speaker emphasizes the importance of creating "skills" for agents—essentially playbooks that define how tasks should be performed consistently without repeated instructions.

Skill Creation Example 1: Skill Creator

• The first skill demonstrated is called "Skill Creator," designed to help build other skills by understanding best practices and directory structures.

• This meta-skill generates new skills based on user prompts while adhering to established guidelines for structure and content.

Skill Creation Example 2: Browser Automation

• The second skill focuses on browser automation using tools like Playwright or Puppeteer. It enables agents to perform tasks such as logging into websites and scraping data dynamically.

• A detailed prompt is provided for building this skill, emphasizing its ability to handle complex interactions with web pages beyond basic API calls.

YouTube Summarizer and AI Pulse Development

YouTube Summarizer Skill

• The speaker discusses the importance of setting up prerequisites for a new skill, emphasizing the need for testing before finalizing.

• Introduces the YouTube summarizer skill, which extracts transcripts from YouTube videos using YouTube DLP, a powerful open-source tool.

• The summarizer generates structured summaries with timestamps, allowing users to quickly access key parts of lengthy videos.

• The summary includes an overview, section breakdowns with timestamps, key announcements, product launches, and resources mentioned in the video.

• It addresses edge cases like videos without captions by utilizing alternative AI models for transcription.

Building the YouTube Summarizer

• The process involves creating a skill that automatically uses existing tools to build functionality efficiently.

• Highlights how this skill integrates various elements such as timestamp formats and output templates to enhance user experience.

• Emphasizes that these skills contribute to an agent system that improves over time through stacking capabilities.

Introduction to AI Pulse

• Transitioning from skills to a larger project called AI Pulse—an automated AI news monitoring system composed of three main components.

• Describes the backend API responsible for researching AI news via web searches and scoring stories based on their trending status.

• Discusses front-end dashboard features displaying latest stories and tweet drafts while maintaining real-time updates through scheduled agents.

Setting Up Oz Environment

• Details setting up an Oz environment that encompasses both backend (AI-Pulse API) and frontend (AI-Pulse site), facilitating cross-repo tasks seamlessly.

• Explains using Oz CLI to create a persistent cloud container tailored for agent operations without taxing local resources.

Agent Collaboration in Real-Time

• Illustrates launching an agent for backend development while simultaneously managing another agent for frontend tasks—showcasing efficiency in multitasking.

• Highlights how agents scaffold projects autonomously in the cloud while allowing real-time adjustments through a web view interface.

• Demonstrates collaborative steering with agents where user inputs can be integrated instantly into ongoing processes.

AI Agents in Cloud Development

Parallel Development of Frontend and Backend

• Two agents are running simultaneously in the cloud, each working on different repositories but within the same environment. Agent one focuses on API development while agent two builds a Next.js dashboard.

• The shared environment allows the frontend agent to access API endpoints directly from the other repository, eliminating guesswork and avoiding manual schema copying between repos.

• Both agents complete their tasks in about 20 minutes, showcasing efficiency that would typically take a whole day if done manually.

Scheduled Automation with AI Agents

• Three scheduled agents are set up:

• The first runs every 3 hours to fetch new AI stories and alerts if any score above an 8 out of 10.

• The second operates every 6 hours to generate tweet drafts based on top stories from the last 12 hours.

• The third agent runs daily to clean up old stories, check for broken links, and update dependencies—tasks that are essential yet tedious.

Proactive Notifications and Real-Time Updates

• Oz's proactive agents notify users instead of waiting for user prompts. Once set up, these agents operate independently without requiring constant oversight.

• After running for about 24 hours, the system displays trending stories sourced from various platforms like Reddit and TechCrunch, enhancing content curation.

Simplifying Social Media Engagement

• A feature called "tweet this" allows users to draft tweets quickly based on trending stories. This functionality helps overcome writer's block by providing ready-to-use content.

• Users can modify generated tweets before posting them on social media platforms like Twitter/X, streamlining real-time engagement without spamming.

Impact of AI-Assisted Software Development

• The project demonstrates how AI systems can collaborate effectively in software development; both backend API and frontend were developed by autonomous coding agents.

• Notifications work seamlessly across multiple messaging channels (SMS, Telegram, Slack), ensuring users stay updated with minimal effort required on their part.

Advantages of Oz Infrastructure

• Oz distinguishes itself not through smarter or faster agents but via its infrastructure that supports parallel operations across multiple repositories without overloading local machines.

• Features such as scheduling allow agents to work during off-hours while providing an orchestration panel for easy monitoring—all contributing to significant time savings for developers.