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The plan's advancement has triggered dissatisfaction among developers. According to VentureBeat, OpenAI's current API deprecation involves the GPT-4.5 Preview version, which is an intermediary model launched between GPT-4 and GPT-5. Multiple developers have stated that this sudden change has disrupted their development plans, forcing significant adjustments to applications already built based on this API. A developer who wished to remain anonymous commented: We understand the necessity of technological iteration, but the lack of sufficient buffer time in the schedule has brought additional pressure to development teams.

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According to the latest prediction from Gartner, by 2028, 80% of generative AI business applications will be developed on existing data management platforms. This shift is expected to reduce development complexity and cut delivery time in half. Currently, the development of generative AI business applications mainly relies on combining large language models (LLMs) with corporate internal data, as well as evolving technologies such as vector search, metadata management, prompt design, and embedding techniques. However, without a unified management approach, companies may...

Recently, OpenAI announced the launch of its latest o3-pro AI model, which is designed to provide more reliable and accurate responses for enterprises. As an advanced version of the o series inference models, o3-pro supports more software tool integrations and is particularly suitable for businesses and developers requiring high precision and accuracy. However, the response speed of o3-pro has slowed down compared to previous models. OpenAI stated that due to o3-pro's enhanced capabilities with richer tools, it needs extra time when handling complex problems.

The global leader in open-source solutions, Red Hat, recently announced the launch of the revolutionary open-source project LLM-D, specifically addressing the urgent needs for large-scale inference in generative AI. The project, founded by industry giants such as CoreWeave, Google Cloud, IBM Research, and NVIDIA, aims to meet the most stringent production service-level goals through breakthrough technologies that enable cloud-based large language model inferencing.

Recently, the DeepSeek team released a technical paper about their latest model, DeepSeek-V3, focusing on the challenges of scaling in large AI model training and related considerations of hardware architecture. This 14-page paper not only summarizes the experiences and lessons learned during the development of V3 by DeepSeek but also provides deep insights for future hardware design. Notably, CEO of DeepSeek, Liang Wenfeng, also participated in writing the paper.