Summary
Highlights
The video introduces the rapidly expanding ecosystem of Large Language Models (LLMs), starting with OpenAI's ChatGPT, developed in 2022. It highlights that while ChatGPT is an "Original Gangster" and feature-rich, many other LLM-like applications have emerged from big tech companies (Google Gemini, Meta Llama, Microsoft Copilot) and startups (Anthropic Claude, xAI Grok, DeepSeek, Mistral). The speaker recommends using leaderboards like 'Chatbot Arena' and 'seal leaderboard' to track model performance and strength, emphasizing that paid subscriptions generally offer access to larger, more powerful models.
The core interaction with an LLM involves text input and text output, broken down into "tokens." The video demonstrates how a text query is converted into a sequence of tokens using the TikTokenizer app. It explains that conversations are built by alternating turns (user and model) in a "token stream," which forms the "context window" or working memory of the model. Starting a new chat wipes this window, resetting the context and saving computational resources, as longer context windows can be more expensive and distracting for the model.
The speaker explains that an LLM's knowledge comes from a two-stage training process: pre-training (compressing a vast amount of internet data into a probabilistic "zip file" of neural network parameters) and post-training (fine-tuning the model to act as an assistant based on human-curated conversations). This means the model's knowledge has a "knowledge cutoff" (when pre-training occurred), making it unaware of recent events. By default, LLMs are self-contained and lack external tools like calculators or web browsers unless specifically integrated. The speaker illustrates this with examples of asking ChatGPT for caffeine content and medicine information, highlighting the need to verify information due to its probabilistic nature.
The video emphasizes the importance of being mindful about which LLM model is being used. OpenAI offers different tiers (free, Plus, Pro) with varying access to models like GPT-4o Mini (smaller, less capable) and the flagship GPT-4o. Paying users typically get access to more advanced models with better performance, creativity, and knowledge. Other providers like Anthropic (Claude) and Google (Gemini) also have tiered access. The speaker suggests that for professional use or demanding tasks, investing in a top-tier model can be beneficial, and often uses multiple LLMs (his "LLM Council") to get diverse perspectives, such as for travel advice.
LLMs can employ "thinking models," which are trained with reinforcement learning to develop problem-solving strategies, similar to a human's inner monologue. These models explore different ideas, backtrack, and revisit assumptions, leading to higher accuracy, especially in complex tasks like math and coding. The speaker provides an example where a standard GPT-4o failed to debug a programming problem, while a "thinking model" (like OpenAI's O1 Pro mode) successfully identified the core issue after a period of "thought." Other LLMs like Claude, Gemini, and Grok also offer models with these enhanced reasoning capabilities, often indicated by specific labels or a "think" toggle.
The video then moves to "tool use," starting with internet search. This feature allows LLMs to perform web searches, retrieve information from web pages, and integrate it into the conversation's context window. This is crucial for obtaining recent information that isn't part of the model's pre-trained knowledge. The speaker demonstrates this by asking for the release date of a TV show, which models like ChatGPT and Perplexity can answer by searching the web. However, not all LLMs integrate this feature equally, with some automatically performing searches, others requiring explicit activation, and some (like older Claude/Gemini versions) lacking the capability altogether. The speaker uses Perplexity.ai for a variety of search queries, whenever the answer is likely to be found in top web search results (e.g., market open hours, movie filming locations, product features, trending news summaries).
Deep Research is a recent, advanced feature (currently requiring a $200/month ChatGPT Pro subscription) that combines internet search and "thinking" processes over an extended period. The model can spend tens of minutes conducting multiple internet searches, reading papers, and synthesizing information to produce a comprehensive report. The speaker illustrates this by asking for research on a longevity supplement (Ca-AKG). ChatGPT, Perplexity (with "Deep research" mode), and Grok (with "Deep search") are shown performing this task, generating detailed reports with citations. While impressive, the speaker cautions that results should be treated as a "first draft" and verified, as hallucinations can still occur. Examples include comparing browsers for privacy and researching life extension in mice.
LLMs can be provided with concrete documents via file upload, acting as an extended context window. The speaker demonstrates uploading a research paper to Claude and asking for a summary. This allows for interactive document reading, where questions can be asked directly about the loaded content. The speaker also describes using this functionality to read books, such as "The Wealth of Nations," by copy-pasting chapters into the LLM and asking for summaries or clarifications. This method significantly enhances understanding, particularly for complex, old, or interdisciplinary texts, making reading more accessible.
The next powerful tool discussed is the Python interpreter (or similar programming language integration), which allows LLMs to write and execute code. Instead of directly answering complex calculations, the LLM generates a program, runs it, and then uses the program's output to formulate its response. The speaker shows how ChatGPT uses the Python interpreter for complex multiplication, while other LLMs (like Grok) might try to do it in their "head" and get it wrong. ChatGPT's Advanced Data Analysis feature extends this, allowing it to act as a "junior data analyst" by plotting data. The speaker demonstrates creating a plot of OpenAI's valuation over time, highlighting that while powerful, users must scrutinize the generated code and results, as the model can still make implicit assumptions or hallucinate information.
Cloud artifacts, unique to Claude, allow the LLM to generate and deploy custom applications directly in the browser. The speaker demonstrates Claude writing a flashcard app to test vocabulary from a given text, showing how it generates React code. While not a daily user, the speaker finds artifact generation particularly useful for creating conceptual diagrams. By providing a chapter from "The Wealth of Nations," Claude can generate a Mermaid diagram illustrating the key arguments and their relationships, aiding visual thinkers in understanding complex information.
For professional coding, the speaker bypasses web-based LLM interfaces in favor of dedicated IDEs with LLM integration, such as Cursor. This standalone application works directly with files on the user's system and leverages LLMs (like Claude 3.7 Sonnet via API) to assist with coding tasks. The speaker demonstrates cursor building a simple Tic-Tac-Toe React app from scratch, where the LLM (called "composer" in Cursor) generates boilerplate code, CSS, and JavaScript with minimal human input. The concept of "Vibe Coding" is introduced, where users give high-level commands, and the AI autonomously implements them. The speaker shows Cursor adding confetti and sound effects to the Tic-Tac-Toe game, highlighting the efficiency and power of LLM-assisted development, while also noting that developers can always fall back to traditional coding for debugging or complex adjustments.
The video transitions to multimodal interactions, starting with voice. The speaker explains the difference between "fake audio" (speech-to-text and text-to-speech conversions) and "true audio" (where the LLM processes audio chunks natively). While mobile apps typically offer easy speech-to-text (microphone icon), desktop users might need third-party apps like Super Whisper. "True audio" (often called "advanced voice mode" in ChatGPT) allows the LLM to directly hear and speak audio, offering more natural and nuanced interaction. The speaker demonstrates true audio with ChatGPT's advanced voice mode, including changing voices (Yoda, pirate) and storytelling, though noting its occasional reluctance to perform certain requests. Grok also offers an advanced voice mode with various "unhinged" personalities.
Google's NotebookLM is introduced for generating custom podcasts. Users can upload various sources (text, web pages, PDFs), and NotebookLM creates an audio podcast synthesizing the information. The speaker uses it to create podcasts on niche topics or complex papers. Moving to images, the video explains how images can be tokenized similarly to text and audio, allowing LLMs to process them. The speaker shows using image input to analyze a nutrition label, interpret blood test results, transcribe mathematical expressions, and understand a meme. For image output, OpenAI's DALL-E 3 is highlighted, capable of generating stylistic images from text prompts. The speaker uses image generation for creating social media content or icons.
The video then covers video input, available in ChatGPT's mobile app (Advanced Voice mode). The speaker demonstrates pointing the phone camera at various objects (acoustic foam panels, books, a CO2 monitor, a map) and asking the LLM to identify and discuss them. The model provides accurate descriptions and relevant information, making it a natural way for non-power users (like parents) to interact. While seemingly real-time video processing, the speaker notes it might involve processing images from the video stream. Finally, the video briefly touches upon video generation, showcasing several AI models (e.g., Sora, Kuaiku, Auror) generating high-quality video clips from text prompts, acknowledging its rapid evolution and creative applications.
The video concludes by discussing quality-of-life features. ChatGPT's "Memory" feature allows it to remember user preferences and facts across conversations, leading to more personalized interactions over time. Users can manage or edit these memories. "Custom Instructions" allow users to set global preferences for how ChatGPT should behave (e.g., tone, educational emphasis). Finally, "Custom GPTs" enable users to create specialized LLM agents by providing detailed, few-shot prompts. The speaker demonstrates a Korean vocabulary extractor and a detailed Korean translator, highlighting how custom GPTs can automate repetitive tasks by pre-configuring the model with specific instructions and examples, making them more accurate and efficient than generic tools. These are particularly useful for language learning and other niche tasks. The speaker encourages experimentation with these features to tailor LLMs to individual needs and workflows.