Summary
The video showcases the recent update V3 of Deepseek, a groundbreaking LLM that impressed viewers with its coding abilities. The demonstration included creating a modern landing page with HTML, CSS, and JS, generating thousands of tokens from a Sonnet and showcasing high-quality output. Throughout the video, the speaker explored reasoning models, prompt modifications, and API providers, providing valuable insights into the latest capabilities and performance of Deepseek. Overall, the video offered a comprehensive overview of Deepseek's functionalities and the impact of its V3 update on model responses.
Deepseek V3 Update
Deepseek had a minor update to V3, one of the biggest updates to an LLM seen so far. Impressive at coding, oneshot a whole website from a single prompt with properly working links.
Creation of Modern Landing Page
Created a modern landing page using HTML, CSS, JS in a single file, generating almost 20,000 tokens from Sonnet 3.7. Output shown on high settings.
Python Script and HTML Rendering
Discussed creating an HTML script of a ball bouncing inside a spinning tessc using a Python script. Explored rendering HTML inside the Deepseek app.
Reasoning Models Testing
Tested reasoning models with prompts like the classic trolley problem, comparing responses with user input before making decisions. Explored reasoning capabilities with models like Gemini 1.5.
Modified Prompts and Responses
Reviewed modified prompts like the Monty Hall problem, noting variations in responses. Explored prompt modifications and their impact on model responses.
Deepseek Capabilities and Performance
Analyzed Deepseek capabilities with the V3 update, discussing model performance and capabilities. Explored API providers, particularly open router, and its output token flexibility.
Deepseek API and Usage
Explained the API features of Deepseek, including usage, pricing, and documentation. Compared hosted version with open router API, highlighting differences and token limits.
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