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The Decoder：DeepMind 希望更早推出 Gemini 4，重点转向可信智能体

据 The Decoder 报道，DeepMind 负责人 Koray Kavukcuoglu 希望在年底前更早推出 Gemini 4。报道表示，该模型已进入后训练阶段，并在内部编码工具 Antigravity 中使用；他强调可信智能体的重要性。产品时间表仍以官方后续公告为准。

Use open weight models as your AI coding agent with Amazon Bedrock

Pair OpenCode, an open-source terminal-native AI coding agent, with open weight models on Amazon Bedrock to get a secure, flexible, pay-per-use coding assistant. Learn how to configure multi-model workflows, match the right model to each task, and keep your data in your own AWS account with no infrastructure to manage.

Claude Opus 5.5 is now available on AWS

Claude Opus 5.5, Anthropic's most capable Opus model for agentic coding, knowledge work, and long-running tasks, is now available on Amazon Bedrock and Claude Platform on AWS. This post covers what's new in Opus 5.5, practical guidance, and how to start building with the model on Amazon Bedrock.

xAI’s Grok 4.6 is now available in Amazon Bedrock

xAI's Grok 4.6 is now available in Amazon Bedrock: a frontier model for long-running agents, coding, and knowledge work, with a 500K token context window and four reasoning effort levels. It runs on both the bedrock-mantle and bedrock-runtime endpoints, with Converse API and cross-Region inference support.

Introducing Kimi K3 on Amazon Bedrock

Kimi K3 from Moonshot AI is now available on Amazon Bedrock, giving you a powerful new open-weight option for coding and knowledge work. It offers native vision, a 1-million-token context window, and explicit prompt caching to reduce latency and input costs.

v0.34.2

What's Changed Added first-run setup when running ollama , with options to sign in or continue locally. Setup completion is shared with the desktop app on macOS and Windows. Added ollama://apps to open the desktop app’s Apps page directly on macOS and Windows. Fixed excessive memory growth during long generations with MLX speculative decoding. Updated llama.cpp. Full Changelog : v0.34.1...v0.34.2

v0.34.2-rc2: mlxrunner: Release freed KV buffers during speculative decode

The decode loop releases MLX's pool of freed buffers every 256 generated tokens, which is also how often the KV cache grows and drops its previous, smaller buffers. The check fires only when the token count lands exactly on a multiple of 256. Speculative decoding emits several tokens per round, so most rounds step over the boundary and the pool is never released. Each growth at a long context leaves several GB of buffers that no later allocation can reuse, so the runner's footprint keeps climbing over a long generation until the system runs out of memory. We now release the pool whenever a round crosses a multiple of 256 tokens, which is what

AWS Weekly Roundup: Claude Fable 5.1 on AWS, Amazon Linux 2027 preview, AWS Certified AI Business Strategist, and more (September 7, 2026)

Last week, Claude Fable 5.1 became available on AWS. According to Anthropic, Claude Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows. Claude Fable 5.1 is built for long-running, high-stakes work that runs for hours and spans many applications. It can own more of a software project on its […]

Give Your Coding Agents a Memory You Own

Training a coding model to paint watercolours with TRL and OpenEnv

Release v5.16.1

Release v5.16.1 This is a special release as we include GLM! (and a few small fixes) GLM-5.3-Flash GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply r

Release: v5.15.0

Release v5.15.0 New Model additions Meta Muse Glimmer Muse Glimmer, released today, is Meta’s new multimodal model, especially designed for agentic use cases. Distilled from Muse to 30B parameters, and released under the Apache 2.0 license, it can be deployed to local setups for privacy-aware applications such as coding, document analysis, personal assistants, Claw- or Hermes-like setups. Muse Glimmer is a dense 30B parameter model consisting of: 2B ViT-style encoder for vision (Perception Encoder) 28B parameter text decoder We're covering it in the following blogpost: http://hf.co/blog/muse-glimmer GraniteMoeSWA & GraniteSWA Links: Documenta

Patch release: v5.14.1

Patch release v5.14.1 This patch solves a few issues which appeared when integrating Inkling model, most notably an issue affecting models using EncoderDecoderCache during assisted generation. It also fixes an issue that could appear during prefill with StaticCache and sdpa without padding for Inkling which uses a position_bias. It contains the following commits: Fix sdpa prefill with position_bias ( #47359 ) by @Cyrilvallez Fix assisted decoding for models with EncoderDecoder cache & OlmoHybrid ( #47361 ) by @Cyrilvallez [FP8] Bump kernels version ( #47344 ) by @vasqu Fix deepgemm on multiple devices ( #47323 ) by @IlyasMoutawwakil

Release v5.14.0

Release v5.14.0 New Model additions Inkling (fresh from Thinking Machines): 975B total, 41B active Add Inkling model #47347 by @molbap @Cyrilvallez @eustlb and @zucchini-nlp Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI- powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-foll

Release v5.13.0

Release v5.13.0 New Model additions KimiK 2.5, 2.6, and 2.7 This release includes the architecture for Kimi 2.5 which is used by 2.5-2.7: Kimi K2.5 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration. The model was proposed in Kimi K2.5: Visual Agentic Intelligence and further improved in [Kimi K2.6: Advancing Open-Source Coding](Kimi K2.5: Visual Agentic Intelligence). Kimi K2.5 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming langua

Open source maintainership in the age of AI

AI has really changed the game around software development. More people are leveraging AI than ever to contribute patches to projects they use. To me, this is a good thing as more folks will contribute patches rather than fork or not fix them. The main problem is that AI has made generating code fast but there has been very little improvement in maintaining code bases. In this post, we will highlight the ways the Kubernetes community is adapting to the world of AI assisted coding. The first step of this journey was to develop an AI policy. This seems mundane and bureaucratic but there were many PRs that derailed into discussions around AI usa