In the era of driven artificial intelligence, the demand for high-quality data by large language models (LLMs) like GPT-3 and BERT is increasing. However, manually sorting this data from the web is not only time-consuming and labor-intensive but also often difficult to scale. This poses significant challenges for developers, especially when large amounts of data are required. Traditional web crawlers and data scraping tools have limited capabilities in extracting structured data; although they can collect webpage data, they often cannot format it in a style suitable for LLM processing.

The newly launched 1.5 flash model update of Gemini has significantly improved response speed by 50%, especially noticeable in the Pro Plan, benefiting all user tiers. This optimization is evident on both web and mobile platforms, enhancing user interaction experience, particularly when handling complex tasks. The competitiveness of Gemini in the AI assistant domain has thus increased, with a new round of updates scheduled for release on September 4, introducing more enhanced features and continuously optimizing services. This update reflects the rapid iteration of AI technology and fierce competition, allowing users to benefit and enjoy an improved experience.

Groq has recently launched a lightning-fast LLM engine on its website, allowing developers to perform quick queries and task execution on large language models directly.This engine utilizes Meta's open-source Llama3-8b-8192 LLM, supports other models by default, and its speed is stunning. According to test results, Groq's engine can process 1256.54 tokens per second, far surpassing the speed of GPU chips from companies like Nvidia. This has garnered widespread attention from both developers and