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# 🤖 AI Agents Weekly: GLM-5.3-Flash, Hy4 Preview, Qwen3.8-Flash, Claude's Built-In Browser, Terminal-Bench-Science, Jalapeño, Skild S1, and More
### GLM-5.3-Flash, Hy4 Preview, Qwen3.8-Flash, Claude's Built-In Browser, Terminal-Bench-Science, Jalapeño, Skild S1, and More
Aug 29, 2026
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In today’s issue:
  * GLM-5.3-Flash ships under MIT
  * Tencent opens Hy4 preview weights
  * Qwen previews the Qwen4 architecture
  * Claude gets its own browser
  * Terminal-Bench-Science scores agents on science
  * OpenAI reports first Jalapeño results
  * Skild S1 learns from one video
  * Headlong keeps agents always thinking
  * X launches Chat Agents
  * MCP publishes its next roadmap
  * AI4AI-Bench tests recursive self-improvement
  * Agents close 81.7% of the speedrun gap
  * Repo-wide migrations survive 5.4% of runs


And all the top AI dev news, papers, and tools.
* * *
## Top Stories
### GLM-5.3-Flash Ships Under MIT
[![GLM-5.3-Flash benchmarks](https://substackcdn.com/image/fetch/$s_!_fWt!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd3906313-9eed-4aea-8d81-cde8399c2f6a_2048x1277.jpeg)](https://substackcdn.com/image/fetch/$s_!_fWt!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd3906313-9eed-4aea-8d81-cde8399c2f6a_2048x1277.jpeg)
Z.ai released GLM-5.3-Flash, a natively multimodal 320B-A18B model with a 1M-token context window, published under the MIT license. It was previously previewed as Ox Alpha.
  * **Agentic benchmarks:** 84.3 on Terminal Bench 2.1, 63.4 on DeepSWE v1.1, 48.8 on AutomationBench v1.0.6, 55.3 on HLE with tools, and 1773 on GDPVal-AA v2, ahead of GLM-5.2 on every one.
  * **Coding performance:** On Z.ai Code Bench v1.0, run through Claude Code, GLM-5.3-Flash beats GLM-5.2 at every effort level and at max effort comes within half a point of Claude Opus 4.8 at 29.0 against 29.5.
  * **Priced to run in a loop:** $0.15 per 1M input tokens, $0.50 per 1M output, and $0.03 for cached input, which makes long agent trajectories cheap to iterate on.
  * **Hybrid attention carries the efficiency:** Linear attention captures local dependencies while sparse attention retrieves global context through a lightweight indexer, cutting attention compute 3.0x and KV cache 4.4x against GLM-5.3. Against GLM-4.5 it nearly halves both activated parameters (18B against 32B) and layers (45 against 92).
  * **Served on Chinese silicon:** Z.ai ran the model anonymously as ox-alpha on OpenCode and OpenRouter before release and served all of that traffic on Chinese AI chips, reporting 3x better end-to-end serving performance than its own earlier baseline on the same hardware.


**[Blog](https://z.ai/blog/glm-5.3-flash)** | **[Weights](https://huggingface.co/zai-org/GLM-5.3-Flash)** | **[Docs](https://docs.z.ai/guides/vlm/glm-5.3-flash)**
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