双“雷”暴击！Trae 被曝资源黑洞、Claude背刺超级付费党，开发者们被“刀”惨了

Core Viewpoint - The article highlights the growing popularity of AI programming applications like Trae, which emphasize "automated execution, multi-model invocation, and contextual memory." However, it also points out significant issues such as resource consumption, performance lag, and high inference costs that affect both developers and users [1]. Group 1: Resource Consumption Issues - Trae has been reported to excessively consume resources, with a comparison showing it uses 33 processes and approximately 5.7 GB of memory, significantly higher than Visual Studio Code's 9 processes and 0.9 GB memory usage [2][3]. - After an update to version 2.0.2, Trae's process count was reduced to about 13, and memory usage decreased to approximately 2.5 GB, indicating some improvements but still highlighting the initial high resource consumption [2][4]. - The telemetry system in Trae captures extensive user interaction data, with a single batch of data reaching up to 53,606 bytes, and around 500 calls occurring in a short period, resulting in a total data transfer of 26 MB within approximately 7 minutes [4][9]. Group 2: Cost Management and User Experience - The high operational costs and resource consumption of AI programming tools are common industry challenges, prompting companies like Anthropic to impose usage limits on their paid users of Claude Code, effective from August 28 [16][18]. - Anthropic's new usage limits are designed to manage the demand for Claude Code, which has seen unprecedented levels of usage, particularly among heavy users of the $200 monthly Max plan [19][20]. - The article notes that while high-tier subscription plans are becoming more expensive, many companies still offer free or lower-cost options to attract non-heavy users [23][24]. Group 3: User Feedback and Market Dynamics - Developers have expressed dissatisfaction with Trae's performance, citing issues like lag and high memory usage, which reflect underlying resource allocation and system design problems [15]. - The article discusses the segmentation of high-paying users into two categories: those seeking to explore new technologies and those who believe these tools will provide a return on investment through increased efficiency [21]. - The increasing costs of AI subscription services are expected to continue rising, as companies balance computational costs with user experience, indicating a potential shift in market pricing dynamics [24].