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06:00

DeepSeek Harness: Everything Is a Plugin on the Cordis Kernel

DeepSeek Harness 速通：一切皆插件，Agent 在运行中插拔自己

DeepSeek launched DeepSeek Harness right after V4 Pro, built around the idea that everything is a plugin. The core, Cordis, only handles plugin loading, unloading, and dependency management, allowing hot-swapping during agent runs; an 88-page paper describes temporal and spatial composability. UI, tools, skills, storage, and the agent loop are all exposed as plugins, with 100+ first-party plugins and a community plugin marketplace. Setup is a single `npx @deepseek-ai/dsh web` command, and the harness is not locked to DeepSeek models—custom providers, base URLs, protocols, and model lists are supported. The post walks through four modes: Standard, PTC (packing multiple tool round-trips into one run_code), Minimal, and Create, where the agent can inspect its own Cordis environment, build a plugin, and attach it mid-run. Sessions are append-only event logs, making failure traces observable, auditable, and reproducible. It also flags steep price increases for V4 Pro (cache hits up 12x, peak output ¥27) and admits the UX is rough for non-developers. Useful for engineers interested in agent-harness design and composability.

x.com · 7 min · Agent Architecture · Agent Infrastructure · Ai Tooling · Plugin System
06:00

GLM-5.3: Post-Training Scaling Boosts Coding and Cyber

GLM-5.3：只靠后训练扩展，代码与网络攻防能力同步提升

Z.ai announces GLM-5.3, claiming all gains come from post-training scaling on the same base model as GLM-5.2. The release post details environment-synthesis pipelines, reward verification, and the slime RL stack. Public coding/agent benchmarks improve markedly: Terminal-Bench 3.0 rises from 4.6 to 28.3, DeepSWE v1.1 from 46.2 to 66.9. The standout claim is an "emergent cyber capability": ExploitBench jumps from 24.4 to 54.4, and real-world testing across 269 projects found 2,436 vulnerabilities, the oldest from 1981, now tracked in a public disclosure ledger. On the systems side, slime adds local storage caching, 1e-7 training-rollout logprob alignment, and workload-aware scheduling, yielding 2.3x throughput for long-horizon coding RL. The API removes thinking disabled and introduces reasoning_effort low/high/max. Weights arrive in two weeks after safety hardening. All scores are self-reported, and closed models still lead on several cyber and coding suites.

z.ai · 22 min · Agent Engineering · AI Engineering · AI Security · LLM · Open Source