I put TOP AI AGENTS to the test to see which one's the BEST | Summary and Q&A

TL;DR

The author challenges three popular autonomous agents, Small AI, Jupiter Engineer, and Baby AGI, to build a simple to-do app and a Google Chrome extension. Small AI and Jupiter Engineer produced apps that didn't work, while Baby AGI showed potential but ultimately failed to deliver.

Key Insights

• 😀 Small AI and Jupiter Engineer struggled to build functional apps, indicating limitations in their abilities to generate code.
• 😀 Jupiter Engineer showed some improvement by asking clarifying questions and tweaking the prompt, but still fell short of producing fully functioning apps.
• 🤳 Baby AGI demonstrated potential with its self-healing properties and ability to fix code, but encountered looping issues and couldn't complete tasks.
• 🪡 The complexity of the Chrome extension challenge highlighted the need for human involvement and a well-structured prompt in more complicated projects.
• 😚 GPT Engineer performed the best overall, showcasing better understanding and producing results closest to the author's expectations.
• 🔠 Small AI's debugging feature was helpful in identifying and resolving issues, despite limitations in API usage.
• 😀 With further improvements, Baby AGI might become capable of building the best app among the agents.

Transcript

all of you have requested this and finally in today's video I'm going to Challenge three most popular autonomous agents small AI Jupiter engineer and baby AGI I'm beating these three autonomous agents against each other in order to figure out which one is the best when it comes to writing code and building apps and I have some very happy news to sh... Read More

Questions & Answers

Q: Which autonomous agent performed the best in building the simple to-do app?

Small AI and Jupiter Engineer both had issues with generating a functional to-do app. However, Jupiter Engineer showed slight improvement by tweaking the prompt and asking clarifying questions, resulting in a partially functional app.

Q: Why did the author believe that Baby AGI had potential in building the Google Chrome extension?

Baby AGI showed promise in understanding the task, but it encountered a looping issue and couldn't complete the extension. The author speculates that with further improvements in the model, Baby AGI might perform the best among the agents due to its self-healing properties and ability to fix code and detect mistakes.

Q: What was the main reason for the failure of the agents in building the Chrome extension?

The complexity of the Chrome extension challenge highlighted the limitations of the autonomous agents. The lack of a carefully crafted prompt and the author's unfamiliarity with building Chrome extensions contributed to the agents' inability to generate a functional extension.

Q: How did the debugging feature of Small AI help in the development process?

Despite facing a rate limit issue with the gpt4 API key, Small AI's debugging feature proved useful. It allowed the author to make gpt4 API calls for debugging purposes, although it didn't pinpoint any apparent errors or bugs.

Summary & Key Takeaways

• The author challenges Small AI, Jupiter Engineer, and Baby AGI to build a simple to-do app using HTML, CSS, and JavaScript. Small AI and Jupiter Engineer generated apps that had bugs and didn't function properly.

• The author then challenges the agents to build a Google Chrome extension that summarizes job ads and suggests interview questions. Small AI struggled with debugging and produced faulty code, while Jupiter Engineer showed better understanding but still had errors. Baby AGI displayed potential but failed to complete the task due to looping.