#Agent

We’re sharing how GLM-5.3 helped build and optimize the inference infrastructure serving GLM-5.3-Flash. The system went from its first successful run to production readiness in less than two weeks, with end-to-end throughput tripling relative to the initial baseline. The key was dense feedback: local correctness tests, execution traces, microbenchmarks, and end-to-end measurements that enabled targeted hypothesis testing rather than reliance on aggregate performance metrics alone. https://z.ai/blog/glm-built-its-inference-infrastructure

So OpenAI will now publicly disclose model misalignment even before it fully understands or fixes the behavior. So its institutionalizing public disclosure of model failures instead of waiting for occasional system cards or bundled research reports. They will prioritize cases that reveal new failure mechanisms, show known problems getting worse, or undermine assumptions about existing safeguards.

Today we rolled out Astra to every engineer at Databricks (N=~3500). Some notes that may be helpful to others: 1. Astra unambiguously out performs our previous highest-end models (Opus 5, Sol 5.6) on highly complex tasks, especially those related to high level system design or long range horizontal tasks. 2. Engineers given Astra increased overall coding spend by around 60% compared to baseline. 3. It is not clear Astra meaningfully improves on medium/low complexity coding tasks compared to earlier models. We suspect those tasks are mostly saturated (i.e. perfectly executed) by existing models. 4. We learned above by piloting Astra with around 200 users to gain signal on both quality and cost. We use Unity Gateway to do cohort-based experiments for all new models. 5. We give engineers a sub-budget specific to Astra to encourage them to use Astra selectively on complex tasks while preferring lower cost models for everyday tasks. Our engineers are able to mix-and-match tools and models within their overall budget envelope (we also allow for increased budgets through various mechanisms). These budgets are defined in Unity Gateway and regularly revisited. Note: We do not have robust comparisons of Astra-vs-Fable because we have net yet rolled out Fable widely due to data retention policies.

A new Perplexity Search SDK cookbook is live. The recipe fans out focused searches, filters results to official docs, extracts relevant passages, and writes a source-linked brief for your coding agent. Get started: https://pplx.ai/pplx-srch-sdk

Today we're launching Rene. A multiplayer-first iMessage agent you text like a friend. It has a browser, writes code, goes shopping, ships sites, makes slides and images. Not much it can’t do. It's been in my texts for four months. It found me a new office, preps me for every meeting, and polls the team for dinner options. No app or signup. Link below👇

Does your Claude model really need Claude Code…? 🤔 We evaluate 7 models on Claude Code, Codex, and Pi. Three surprising findings emerge: 1️⃣Harness choice has little effect on task success rate, but can significantly affect the cost 2️⃣A simple harness can be competitive 3️⃣The native harness isn’t always the best. Millions of people are using coding agents, but the impact of harness choice remains unclear. (1/n) More details in the thread. 🧵

你现在还可以在对话中制作演示文稿、文档和设计。 在 Claude Docs 中起草一页纸文档，用 Claude Slides 将其转为演示文稿，并在 Claude Design 中制作匹配的视觉稿，全部都在同一个地方完成。

Claude Cowork and chat are merging into one Claude. Ask a quick question or hand over a report, and Claude takes it from there, even after you close your laptop. If something's unclear, Claude asks—you keep the final say. Rolling out to Pro and Max over the next few weeks.

你现在还可以在对话中制作演示文稿、文档和设计。 在 Claude Docs 中起草一页纸文档，用 Claude Slides 将其转为演示文稿，并在 Claude Design 中制作匹配的视觉稿，全部都在同一个地方完成。

Claude Cowork and chat are merging into one Claude. Ask a quick question or hand over a report, and Claude takes it from there, even after you close your laptop. If something's unclear, Claude asks—you keep the final say. Rolling out to Pro and Max over the next few weeks.