Core Insights - The article discusses a unique competition where AI models are tested in real-time trading of cryptocurrencies, aiming to determine which model can generate the highest returns without human intervention [1][2]. Group 1: AI Trading Competition - The competition involves six AI models, each with a capital of $10,000, trading major cryptocurrencies like BTC, ETH, and others [1]. - The event has generated significant interest, surpassing traditional stock trading discussions among participants [1][2]. - The performance of the models is evaluated based on their ability to analyze market data and sentiment, akin to human traders [2]. Group 2: Performance of AI Models - After six days, the leading model, DeepSeek Chat v3.1, initially achieved a return of nearly 40%, but has since stabilized around 10% due to market fluctuations [3]. - The most well-known model, GPT-5, has suffered a loss of 68.9%, indicating a poor performance compared to its peers [4]. - Qwen3 Max has outperformed DeepSeek Chat v3.1 with a return of 13.41% by employing a more aggressive trading strategy [7]. Group 3: Insights on AI Models - DeepSeek's strong performance may be attributed to its quantitative background, although initial tests showed mixed results for various models [7]. - The competition highlights the unpredictability of the market and the need for models to adapt to changing conditions [9]. - Observing the trading strategies and decisions of the models provides valuable insights beyond just the final returns [11]. Group 4: AI in Stock Trading - The article emphasizes the importance of selecting the right AI model for stock trading, as many retail investors are beginning to rely on AI tools for investment decisions [12]. - The development of financial AI models has evolved significantly, with notable examples like BloombergGPT, which faced challenges due to its high costs and closed systems [14]. - Despite the potential of AI in trading, many users report dissatisfaction with the outputs, indicating a need for better data quality and model customization [15][18]. Group 5: Challenges and Limitations of AI - AI models often struggle with understanding complex market dynamics and may produce similar strategies, limiting their effectiveness against larger, more sophisticated quantitative firms [16]. - The article warns that relying solely on AI without a solid understanding of investment principles can lead to significant losses [19][23]. - AI's limitations in predicting "black swan" events and its reliance on historical data highlight the need for human oversight in investment decisions [24][26].

Core Viewpoint - The article discusses an experiment called "Alpha Arena" conducted by a financial AI lab, where six AI models trade in real markets with real money, highlighting their performance and strategies in stock and cryptocurrency trading [2][11]. Group 1: AI Model Performance - As of October 21, 2023, DeepSeek leads with a balance of over $12,000, followed by Claude at $11,800, and Grok4 at approximately $11,500. GPT5 has decreased to $6,600, while Qwen3Max is at over $9,200, and Gemini2.5 Pro is at around $6,170 [2]. - DeepSeek's significant growth is attributed to a 36% increase over the weekend, likely due to accurate predictions regarding international conditions [4]. Group 2: Trading Strategies - The founder of DeepSeek believes that both DeepSeek and Grok have a better understanding of the market's microstructure [6]. - DeepSeek's weekend gains are largely due to shorting Bitcoin, while Grok4 maximized its positions, and Qwen only took long positions on Bitcoin, resulting in losses during Bitcoin's decline [8]. - The initial test on October 11 saw Grok4 leading with a starting amount of $200 before the real competition began with a starting amount of $10,000 [8]. Group 3: Experiment Timeline - The first phase of the experiment is set to conclude on November 3, 2023, at which point the results will be evaluated [11].

Core Viewpoint - The article discusses a method to solve the issue of aspect ratio control in images generated by Nano Banana, allowing users to modify existing images to fit desired proportions [2][4][12]. Group 1: Problem Identification - Users of Nano Banana face two main issues: low resolution of generated images and uncontrollable aspect ratios, making it difficult to use images in production [2][4]. - The output image's aspect ratio is determined by one of the input images, leading to inconsistency when multiple images are used [4][12]. Group 2: Proposed Solution - The solution involves using a reference image to control the aspect ratio of the generated images, allowing for modifications to both new and existing images [4][8]. - Users need two images: the original generated image and a reference image that defines the desired aspect ratio [6][16]. Group 3: Implementation Steps - The process requires inputting a specific prompt to instruct Nano Banana to redraw the content of the original image onto the reference image while maintaining the aspect ratio [13][15]. - The order of images is crucial: the image to be modified should be first, followed by the reference image to avoid errors [16]. Group 4: Additional Insights - The article mentions that using the Gemini2.5 Pro model in the Gemini APP yields better results compared to AI Studio when calling Nano Banana [15]. - A link is provided for users to download various aspect ratio templates for convenience [18].