AI Coding Devlog - Aider ON Sonnet 3.5 - CURATE your ELITE information DIET

Summary

In this video, Andy Deb Dan introduces an AI-powered tool designed to filter out noise and focus on valuable content for engineers and product builders. Using AER with Sonnet 3.5, the tool scrapes content, checks for updates, and curates an information diet. The video demonstrates the AI coding process, emphasizing the importance of fine-tuning information streams for better decision-making in the tech industry. The tool, 'Zero Noise,' aims to keep viewers updated with the latest changes in selected blogs, showcasing the power of AI in content curation and workflow automation.

Takeaways

• 🌟 The video is about creating an AI coding session to build a 'zero noise' tool for filtering and focusing on important content updates.
• 🛠️ It uses AER with Sonnet 3.5 for AI coding, prompting almost every line of code to build a tool that tracks content updates based on the last seen date.
• 🔍 The tool provides a list of URLs and notifies users of any updates since the last check, using the 'last seen' date to track changes.
• 📝 The video demonstrates the scraping functionality to ensure it aligns with documentation and actual content.
• 🔑 The importance of curating an information stream is highlighted, emphasizing 'junk in, junk out' and the need for a refined personal knowledge base.
• 💡 The video shows how to use streamlit for the UI and reuses an 'agentic workflow framework' with steps like retrieve, act, learn, and notify.
• 📑 The script discusses the technique of 'context chaining' in AI coding prompts to be more specific about where changes are needed in the code.
• 🔄 The video emphasizes the iterative nature of the workflow, including updating the configuration file with new dates and changes detected.
• 📈 The tool aims to help engineers and product builders make better decisions by filtering out noise and focusing on valuable content.
• 🚀 The presenter discusses the paradigm shift in coding due to AI, suggesting that AI coding is the future and provides a significant advantage.
• 🔗 The video concludes with a demonstration of the tool in action, showing how it can detect updates and provide a summary of changes in a user-friendly format.

Q & A

What is the main purpose of the tool being developed in the video?

The main purpose of the tool is to act as a zero-noise, minimal-agentic information diet tool that filters through the noise to focus on content that is valuable for decision-making as a product builder and engineer.

What does the tool do with a list of URLs provided by the user?

The tool checks the provided URLs for any updates based on the last seen date and notifies the user if there are new updates since the last check.

Which AI coding tools and models are being used in the video?

The video uses AER with Sonnet 3.5 for AI coding, employing prompts for almost every line of code to build the tool.

What is the significance of the 'context chaining' technique mentioned in the video?

Context chaining is a useful AI code prompting technique that allows for more specificity in the changes desired in the code, making it easier for the AI to understand exactly what and where changes need to be made.

What is the role of Streamlit in the development of this tool?

Streamlit is used for creating the user interface (UI) of the tool, allowing users to interact with the tool and view updates from the information streams.

How does the tool help in curating an information stream for an engineer or product builder?

The tool helps by filtering through various information sources and only presenting updates from selected URLs, ensuring that the user receives relevant content that can aid in making better decisions.

What is the importance of being plugged into the latest tools, technologies, and creators in the field of engineering as mentioned in the video?

Being plugged into the latest tools, technologies, and creators is important because that's where the real engineering and value creation happen. It allows engineers to stay updated with cutting-edge developments and innovations.

How does the tool differentiate between 'zero noise' and other information filtering tools?

The 'zero noise' tool focuses specifically on providing updates from curated sources that are known to produce high-quality content, reducing the amount of irrelevant or 'noise' information that the user has to sift through.

What is the workflow structure used in the development of the tool's agentic capabilities?

The workflow structure used is Retrieve, Act, Learn, and Notify, which is a framework for building agentic workflows that ensure the tool can autonomously perform tasks and learn from them.

How does the tool handle the detection of changes in the content of the URLs?

The tool scrapes the content from the URLs and compares it with the last seen date stored in the configuration file. If new updates are detected since the last seen date, the tool notifies the user and updates the configuration file.

What is the significance of using markdown format for prompts in the video?

Markdown format is used for prompts because it provides a clear and structured way to communicate with the AI coding assistant, making it easier to read and understand for both the AI and human engineers.

How does the video demonstrate the use of AI coding in the current age of development?

The video demonstrates the use of AI coding by showing the process of writing code with the assistance of AI, highlighting how AI can generate code drafts, understand context, and integrate into the development workflow to create a more efficient coding process.