5 AI Agents You Can Build This Weekend (beginner to advanced)

Shaw Talebi

138 views • 9 hours ago

Video Summary

This video explores five practical AI agents that can be built over a weekend, offering real-world applications for various skill levels. The agents leverage AI systems to interact with the world using tools, ranging from organizing Notion content and validating product ideas to refining user interfaces and personalizing education. One particularly accessible agent allows users to grant AI full access to their Notion account, eliminating the need for manual copy-pasting of information and significantly reducing retrieval time. Another agent focuses on validating product ideas before coding, enabling parallel research on multiple concepts simultaneously.

The discussion then delves into more advanced applications, such as creating an AI agent to evaluate app frontends by implementing an MCP server with specialized design principles and tools like screenshot and DOM retrieval. For personalized education, an agent is proposed that grounds AI tutors in high-quality YouTube playlist content, extracting transcripts and storing them in a vector database. Finally, an agent for content marketing is presented, designed to iterate on AI-generated outputs using well-defined evaluations to avoid ineffective content. A fascinating aspect highlighted is the iterative refinement process for content generation, ensuring outputs meet specific criteria before final deployment.

Short Highlights

AI agents are systems that use tools to interact with the real world.
A no-code Notion agent can be implemented in under a minute, giving AI full access to your Notion account for easier content retrieval.
A product idea validation agent using Claude Code's skills feature allows for rapid research and parallel validation of 10 ideas.
An agent for UI refinement can be built using an MCP server with tools like get page screenshot and get DOM for evaluating app frontends.
A personalized AI tutor agent can be created by grounding it in high-quality YouTube playlist transcripts, utilizing Chroma and OpenAI's agents SDK.
A content marketing agent can iterate on AI outputs using well-defined evaluations to ensure quality and avoid ineffective content.

Key Details

Building Your First AI Agent: Notion Integration [00:15]

AI agents are defined as AI systems capable of using tools to interact with the real world.
A practical application is giving an AI full access to your Notion account, which can be implemented in less than a minute with no coding required.
This agent allows users to ask AI to retrieve specific pages or content from Notion, significantly reducing the time spent getting feedback.

Validating Product Ideas with AI [01:54]

To avoid building products nobody wants, an agent can be created to validate product ideas before coding.
This can be set up using Claude Code's skills feature, allowing specialized instructions for specific use cases.
Steps include creating a folder for the skill, a skill.md file with name, description, and instructions, and then launching Claude Code to validate an idea.
The agent enables spinning up 10 instances of Claude Code to research 10 ideas in parallel for quicker validation.

"Now, instead of spending hours researching ideas one at a time, I can now spin up 10 instances of Claude Code to research 10 ideas in parallel to kill the bad ones as quickly as possible."

Refining Application GUIs with Specialized Agents [03:17]

A specialized agent can be created to evaluate app frontends using opinionated design principles and custom tools.
Implementation involves creating an MCP server that provides the AI model with necessary resources.
Key steps include initializing the MCP server, adding a system prompt with design principles, implementing get page screenshot and get DOM tools using Playwright, and setting up local transport.
A bonus is creating a sub-agent in Claude Code to evaluate GUIs using this MCP server.

Personalized AI Tutors Grounded in Quality Content [04:48]

AI can make a massive impact in personalized education, but off-the-shelf tools can lead to generic information.
An agent grounded in high-quality content, like a YouTube playlist, can mitigate this issue.
The process involves curating a YouTube playlist, extracting video IDs, and using the YouTube transcript API to get transcripts.
Transcripts are chunked, stored in a vector database (Chroma), and then an OpenAI agent is created with a custom tool to fetch relevant chunks for queries.

"The best way to mitigate this is to build an agent grounded in high-quality content as opposed to generic knowledge from the internet."

Iterative Content Marketing with AI Evals [06:16]

AI-generated content can be easily spotted and ineffective; an agent can iterate on outputs using well-defined evaluations.
Steps include uploading examples to Claude for style generation, performing error analysis by reviewing outputs and noting mistakes, and analyzing the biggest mistakes.
Low-hanging errors are fixed by updating the system prompt, while more complex mistakes are addressed by defining a set of evaluations (e.g., sentence length, hook visibility).
The process involves creating a function to run evals, crafting a feedback prompt, and running the content agent in a while loop for iterative refinement.

"While the architecture of this agent is straightforward, the most important part is crafting high-quality evals that are actually aligned with your desired output."