# 🤖 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

> Source: <https://nlp.elvissaravia.com/p/ai-agents-weekly-glm-53-flash-hy4>
> Published: 2026-08-29 15:33:13+00:00

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

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.