Claude Opus 5 Now Available on AWS Bedrock, with ZDR On by Default for Enterprises
Anthropic's Claude Opus 5, which we covered in our previous release bulletin, became available on AWS Bedrock and Claude Platform on AWS as of July 24. According to the official announcement on the AWS Machine Learning Blog, Bedrock offers **zero data retention (ZDR) by default**, emphasizing a configuration that lets you use the model within your own AWS account while still satisfying regional data residency and enterprise governance requirements.
It runs on Bedrock's next-generation inference engine, and the fact that operators cannot access inference data is also highlighted as a plus for enterprise deployments. The model ID at launch is `global.anthropic.claude-opus-5`. It can also be called via the `AnthropicBedrockMantle` client in the Anthropic SDK.
Additionally, Opus 5 supports adding and removing tools mid-conversation (`tool_addition` / `tool_removal`) via `role: "system"` messages, so there's no need to resend the tools array every time. This looks like a meaningful spec for trimming the fiddly overhead of agent implementations.
> According to Anthropic, in high-risk domains Opus 5 may fall back to Opus 4.8, and fallback behavior can be configured via the API. During the transition period, designs will likely run both models side by side.
Available regions include US East (Virginia), Australia East (Melbourne), and Europe (Ireland, Stockholm), among others.
The Decoder Analysis: Opus 5 Benchmark Details, 4x GPT-5.6 Sol on ARC-AGI-3
Matthias Bastian at The Decoder has broken down Opus 5's benchmarks. The key points:
Top scores in coding and knowledge work**, with per-token pricing at half of Fable 5's
% on ARC-AGI-3**, a benchmark for emergent problem-solving — roughly 4x GPT-5.6 Sol
Anthropic describes it as "performance that comes close to Fable 5 at Opus-tier pricing," but independent benchmarks are still catching up. Worth noting: there remain areas where Fable 5 comes out ahead depending on the context.
Cognition Acquires Poke as AI "Personality" Becomes a Differentiator
Cognition, the AI coding company, has acquired Poke, a conversational AI that texts you like a friend. According to TechCrunch, the valuation is in the "low nine figures (low hundreds of millions of dollars)." Cognition plans to bring Poke's conversational style and interaction model into its own coding agent, Devin.
Over the past few months, **AI assistants' interpersonal manner — their personality — has been emerging as a competitive axis** alongside raw LLM performance. The Poke acquisition epitomizes this trend, hinting at a direction where Devin doesn't just write code but handles all of its interactions with users naturally. Cognition appears intent on shifting Devin's image from "tool" to "coworker" going forward.
> The premise that a "pleasant personality" can differentiate a product holds up in both the coding and conversational domains. How much of it makes its way into Devin is the next thing to watch.
Statistically-Lossless Quantization "SLQ": Accuracy Maintained at 3.3 Bits, Up to 3.6x Faster Inference
A paper (IST DASLab, arXiv:2605.02404) under discussion on r/LocalLLaMA proposes a new middle ground for LLM quantization: **Statistically-Lossless Quantization**. It fills the gap between lossy methods like GPTQ/AWQ and fully lossless but slow approaches, and its distinctive contribution is introducing three definitions of "losslessness":
Task-lossless**: zero-shot accuracy stays within the range of natural sampling error
Distribution-lossless**: the next-token distribution is practically indistinguishable from the original model's (introducing a metric called EAR)
A gamma-squared variance law: theoretically shows that symmetric quantization noise grows larger than asymmetric
The proposed method, **SLQ**, is a layer-wise non-uniform quantization scheme that achieves task-losslessness at 3.3 bits per parameter or below, and distribution-losslessness at 5–6 bits. Optimized kernels are also reported to deliver 1.7–3.6x faster inference compared to FP16. It has been drawing attention since Red Hat AI mentioned it recently.
CachyLLama: Persistent KV Cache Cuts "Context Reprocessing" for Local Agents
**CachyLLama**, also making the rounds on r/LocalLLaMA, is a fork of llama.cpp. It implements SSD-backed persistent KV checkpoints and a system prompt cache, so when the beginning of a request matches a previously processed prefix, the recomputation can be skipped.
It directly addresses the problem of prompt re-evaluation costs becoming a bottleneck in agent harnesses that send long system prompts, tool definitions, and conversation history with every request.
On the author's benchmark (7840U/780M):
,243 tokens: cold 9.3s → warm 0.41s
,409 tokens: cold 43.3s → warm 0.57s
,700 tokens: cold 143.1s → warm 0.99s
Generation speed itself doesn't improve, but the perceived latency of the agent loop reportedly improves dramatically. It also supports hybrid architectures like Qwen 3.5/3.6, Gemma 4, and GLM-4.7, which should make it a compelling option for anyone running agents locally on CPU.
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