Key insights

• ⚙️ Large language models (LLMs) are limited to system 1 thinking, lacking rational problem-solving abilities.
• 🌲 Two methods to simulate rational thinking: tree of thought prompting and utilizing platforms like CRIAI and agent systems.
• 🧠 Building teams of smart AI agents to solve complex problems and making them more intelligent by giving them access to real world data.
• 🔒 Avoiding fees and protecting privacy by running models locally.
• 🔍 Exploration of surprising discoveries related to local models.
• 💻 Defining tasks for a team of agents and the process of their work using code.
• 🛠️ Adding tools to make the agents smarter with real-world data access.
• 🔬 Experimenting with local llama models for crew AI, noting the struggles and successes of different models.

Q&A

• What were the challenges mentioned in using local models for AI tasks?

  Challenges with local models, including RAM limitations and failed experiments with different local llama models for Crew AI, were discussed in the video. The best performing model was a regular llama 13 billion parameters model.

• What tools were discussed for improving newsletter content quality?

  The video overviewed initializing the Sur API tool with an API key and creating a custom Reddit scraper tool as methods for improving the quality of newsletter content.

• How can agents be made smarter using real-world data access?

  Agents can be made smarter by providing them with access to real-world data. This process involves adding tools to enhance the agents' capabilities, as discussed in the video.

• What was the process of setting up AI agents using Crew AI in VS Code?

  The video demonstrated setting up AI agents to refine a startup concept using Crew AI in VS Code. No coding was involved, only outlining specific roles, tasks, and goals for each AI agent within the system.

• How can models be run locally to avoid fees and protect privacy?

  Models can be run locally to avoid fees and protect privacy. However, there may be challenges like RAM limitations, as indicated in the video. Exploring and addressing these challenges is crucial when opting for local model execution.

• How can rational thinking be simulated in AI?

  Rational thinking in AI can be simulated through methods like tree of thought prompting and utilizing platforms like CRIAI and agent systems. Building teams of smart AI agents and providing them access to real-world data also contributes to making them more intelligent.

• What are System 1 and System 2 thinking?

  System 1 is fast, subconscious, and automatic thinking, while System 2 is slow, conscious, and deliberate. Large language models (LLMs) are currently limited to System 1 thinking, lacking rational problem-solving abilities.

• 00:01 The AI assistant's ability to think rationally like a human is limited, but there are methods to work around this and build teams of smart AI agents to solve complex problems.
• 03:08 Setting up AI agents to refine a startup concept using Crew AI in VS Code. Defining roles, tasks, and goals for each agent. No coding involved, just outlining specific roles and tasks for the AI agents.
• 06:34 A demonstration of assigning tasks to different agents within a team using code. The process defines how these agents work together, and tools can be added to make the agents smarter. The example involves defining tasks for a team of agents and modifying the team to include new tools.
• 09:48 The video discusses initializing a custom API tool for scraping and improving the quality of newsletter content by creating a custom Reddit scraper tool.
• 13:02 A user shares their experience with running browser tool class agents and testing Gemini Pro's free API key, highlighting variations in outputs, issues with GPT-4, high costs for running the script multiple times, and challenges with local models due to RAM limitations.
• 15:58 Experimented with different local llama models for crew AI, but most models failed to understand the task. Only the regular llama 13 billion parameters model incorporated subreddit data, albeit not perfectly.