Articles

The article provides a detailed comparative review of OpenAI's newly released image generation models, GPT Image 1.5 and Banana2. The author designed 12 test scenarios, ranging from multi-text generation and Chinese comprehension to world-knowledge application, multi-image fusion, image editing, style transfer, and video storyboard generation, to comprehensively examine the actual capabilities of both models. The evaluation results show that although GPT Image 1.5 has improved in some aspects (e.g., generation speed), its performance in instruction following, the accuracy of Chinese text generation, understanding of world knowledge, and the precision of image editing, especially compared to Banana2, proved unsatisfactory. The article points out that GPT Image 1.5 has significant weaknesses in processing Chinese and complex instructions, and speculates that this might be the reason why OpenAI did not hold a dedicated launch event for it.

ByteDance has officially released its next-generation audio-video creation model, Seedance 1.5 Pro. The model's core highlight is its support for joint (multimodal) audio-video generation, capable of handling diverse tasks from text-to-audio-video synthesis to image-guided audio-video generation. This article details the 1.5 Pro version's efforts to enhance visual impact and elevate the performance ceiling of motion effects, specifically emphasizing its breakthroughs in precise audiovisual synchronization (supporting multiple languages and dialects), cinematic camera movement control with dynamic tension, and enhanced semantic understanding with narrative coherence. Through various scenario demonstrations—such as film and television art creation, high-difficulty scene camera movements, stylized performances, and immersive sound effect generation—the article showcases the model's potential applications in domains like film and television, short dramas, advertising, and animation. It also highlights the model's leading performance in comprehensive evaluations and delves into its multimodal joint generation architecture, covering technical aspects such as its multimodal joint architecture, multi-stage data pipeline, fine-grained post-training optimization, and efficient inference acceleration. Finally, the article notes that there is still room for improvement in areas such as physical stability for high-difficulty movements, multi-character dialogue, and singing, while also outlining future development directions.

Starting with the 1983 paper "The Irony of Automation" and integrating the current development of AI Agents, this article explores the potential challenges AI automation poses to human society and individuals. Core arguments include: the 'use-it-or-lose-it' phenomenon, leading to professional atrophy where humans' skills degrade under automated systems; the memory retrieval dilemma, where infrequently used knowledge becomes slower to access; the practice paradox (limited training efficacy and reduced real-world opportunities); monitoring fatigue (humans' struggle to maintain long-term vigilance over low-error systems); status issues (the psychological impact of experts being demoted to overseers); and the poor monitoring design of current AI Agent interfaces. The article also introduces the 'training paradox', where 'the more successful the automation, the higher the training investment required,' and the new requirement to 'supervise AI with leadership.' The author emphasizes that the advancement of AI Agent capabilities has not altered inherent human cognitive structural limitations, reminding people to be wary of its potential negative effects even while enjoying the convenience of automation.

This article introduces a set of prompts optimized for the Google Gemini 3 Flash model, addressing the common issue of generic designs when large models generate front-end interfaces. The author notes that Gemini 3 Flash excels in speed and cost compared to the Pro version, and its coding capabilities are outstanding. To tackle the problem of large models generating "webpages with monotonous blue-purple tones and a generic AI aesthetic," these prompts simulate the roles of a "Product Manager," "Designer," and "Front-end Engineer." They meticulously define a complete workflow and specifications, from requirements analysis, design inspiration sampling, interactive visual solutions, to front-end code implementation. The "Inspiration Source Pool" in the designer section is particularly innovative, aiming to guide the model to generate diverse, high-quality website designs. This set of prompts can generate various styles of front-end website code in about 30 seconds, greatly enhancing the model's practicality and providing users with an effective tool for rapid front-end design generation.

This article delves into the shift of US internet hotspots from AI websites to prediction markets (e.g., PolyMarket), detailing their operation mechanisms, user participation methods, and significant potential for future growth. Following this, the article introduces how domestic AI models (秘塔AI) are benchmarking against Google Nano Banana Pro in image slideshow generation, highlighting their free, user-friendly nature, and integrated explanation features. Additionally, the weekly features inspiring stories like walking around the world, six-armed robots, hand-crank washing machines, and other tech developments, recommending a series of technical articles and open-source tools. Finally, the article explores the impact of large language models like Claude Opus 4.5 on the employment of technical professionals and the profound value of physics education, sharing perspectives on Chinese expression habits and the contraction of the SaaS industry.