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Home/Models & Labs
Models & Labs

Gemini API Introduces Webhooks for Long-Running Jobs

Google AI Blog·May 4, 2026·high confidence

Why it matters

  • →Webhooks reduce the need for inefficient polling, saving resources and time.
  • →Secure and reliable communication is ensured with signed requests and retries.
  • →Developers can manage complex workflows more efficiently with real-time notifications.
Gemini API Introduces Webhooks for Long-Running Jobs
©Google AI Blog

Google has enhanced its Gemini API by introducing event-driven Webhooks, aimed at improving efficiency for long-running tasks. This new feature allows developers to receive immediate notifications when a job is completed, replacing the need for continuous polling. The Webhooks implementation is secure, adhering to the Standard Webhooks specification, and includes features like signed requests and automatic retries. This update is designed to support complex workflows, making it easier for developers to manage tasks such as deep research and batch processing.

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Google unveils major AI advancements at Cloud Next '26© Google AI Blog
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Google unveils major AI advancements at Cloud Next '26

Google's Cloud Next '26 event showcased significant advancements in AI, emphasizing the 'agentic era' with the launch of the Gemini Enterprise Agent Platform and eighth-generation TPUs. These innovations aim to enhance business operations and energy efficiency in data centers. The introduction of Gemma 4, an open model for advanced reasoning, and Deep Research Max, which automates high-level research tasks, marks a leap in AI capabilities. Additionally, Google Vids now offers free video generation, democratizing access to professional-quality content creation. These developments highlight Google's commitment to integrating AI into diverse sectors, from education to enterprise solutions.

Google AI Blog·May 4, 2026

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llama.cpp b9018 release expands platform support

The b9018 release of llama.cpp continues its trend of broadening platform compatibility, now supporting a wide array of systems including macOS, Linux, Windows, and Android. Notably, it introduces Vulkan support on Ubuntu and Windows, and adds ROCm 7.2 for AMD GPUs, which is a significant step for users seeking alternatives to NVIDIA's CUDA. This release doesn't bring new models or quantization methods, but it solidifies llama.cpp's position as a versatile inference runtime across diverse hardware configurations. Users can now leverage these enhancements to optimize performance on their specific setups.

llama.cpp Releases·May 5, 2026
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llama.cpp b9019 Release Enhances Model Flexibility

The b9019 release of llama.cpp brings notable changes by relocating functions like load_hparams and load_tensors to be defined per model, enhancing the flexibility for developers. This structural shift is complemented by the introduction of build_graph and refined switch case logic, which collectively improve the system's modularity. These updates facilitate easier adaptation to various hardware setups, including macOS, Linux, and Windows environments. Although no new model architectures are introduced, the release sets a foundation for more efficient development and deployment, particularly with support for configurations like KleidiAI on Apple Silicon and ROCm 7.2 on AMD GPUs.

llama.cpp Releases·May 5, 2026
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llama.cpp b9025 Release Expands Platform Support

The latest b9025 release of llama.cpp continues its trend of broadening platform compatibility, now supporting a wide array of systems including macOS, Linux, Windows, and Android. Notably, it introduces Vulkan support on Ubuntu and Windows, and adds ROCm 7.2 for Ubuntu, enhancing GPU performance options. This release doesn't introduce new models but focuses on making llama.cpp a versatile tool across different hardware configurations. By expanding its reach, llama.cpp is positioning itself as a go-to runtime for diverse computing environments, ensuring developers can leverage its capabilities regardless of their platform choice.

llama.cpp Releases·May 5, 2026