The latest survey from the Game Developer Collective reveals the complex attitudes of game developers towards the application of generative AI. 30% of respondents expect AI to reduce development costs in the coming year, while 8% anticipate an increase in costs. Regarding quality, 17% of developers believe AI will enhance game quality, whereas 35% express concern over declining quality. Generative AI has sparked controversy in the gaming industry, with opponents arguing that it may cause greater environmental harm and lead to a decrease in product quality. In terms of application, 49% of developers have adopted it.

Anthropic has announced the launch of a new 'Prompt Caching' feature in its Claude series of large language models, aimed at significantly reducing AI costs for enterprises and enhancing performance. This feature allows users to store and reuse specific context information without incurring additional costs or delays. In public testing, the Claude 3.5 Sonnet and Claude 3 Haiku models will implement this feature, expected to achieve up to 90% cost reduction and double the response speed in certain scenarios. This feature is particularly suited for use cases that require...

On June 25th, OpenAI's announcement of supply cuts caused considerable disruption to developers and businesses. In response to this move by OpenAI, the AI large model company Zero One Everything, founded by Dr. Kai-Fu Lee, quickly reacted by launching the 'Yi API 80% Discount Alternative Plan.' This service is aimed at OpenAI users, designed to help them seamlessly transition to the Yi series large models, providing a cost-effective alternative.

Researchers from Nanjing University and Megvii Research Institute have collaborated to bring a revolution to visual large models. Their proposed unsupervised paradigm, SeVa, successfully addresses the preference alignment issue of visual language models, and the entire process does not require the involvement of humans or GPT-4, significantly reducing alignment costs.

The saying that large models exhibit certain capabilities only when data, parameter counts, and computing power are large is widespread in the tech community. Competing on model size is unsustainable both commercially and environmentally. The era of seeking sheer size has passed. Although the two paths for large models diverge significantly, their situations are similar. The more promising commercial direction to B2B is currently hindered by high labor and capital costs behind the customizable narrative, preventing business expansion and slow incremental growth. To provide long-term and sustainable services...