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@Hesamation · 2026-08-15
DeepSeek Harness is now the fastest growing GitHub repo, passing 100K stars in under 48 hours, even faster than OpenClaw.
very positive community reaction:
> unusually well designed architecture with tools, session log, agent loop, subagents, all being replaceable plugins
> UI
DeepSeek Harness 现在是增长最快的 GitHub 仓库，不到 48 小时破 10 万星，比 OpenClaw 还快。社区反响非常正面：> 架构设计出奇地好——工具、会话日志、agent 循环、子 agent 全都是可替换的插件 > UI……（正文被接口截断）
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@eliebakouch · 2026-08-13
amazing release. it's a web UI with multiple harnesses inside it, you can spawn claude code and codex agent through their SDK
"deepseek harness" supports different "modes" by default (which are harnesses): code mode with programmatic tool calling (in typescript), bash+edit
非常出色的发布。它是一个内嵌多个 harness 的 Web UI，你可以通过 SDK 生成(spawn)Claude Code 和 Codex agent。DeepSeek Harness 默认支持多种‘模式’（本质就是不同的 harness)：带程序化工具调用(TypeScript)的 code mode、bash+edit……（正文被接口截断）
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@jenzhuscott · 2026-08-13
DeepSeek Harness v0.1's Cordis-powered design is probably one of the cleanest expressions of agent architecture I've seen: every capability - model adapters, tool registries, session logs, sandboxes, orchestration loops, and even the UI a is a 1st class plugin w typed services,
DeepSeek Harness v0.1 基于 Cordis 的设计，可能是我见过的 agent 架构中最干净的表达之一：每一项能力——模型适配器、工具注册表、会话日志、沙箱、编排循环、甚至 UI——都是带类型化服务(typed services)的一等公民插件……（正文被接口截断）
Reddit
u/stealthispost · 2026-08-15
**Anchored Standard** (xiaobright/dsh-anchored-standard) does a clever hybrid: 1. First model request: presents V4 Pro with essentially the same environment as Minimal... 2. As soon as it makes its first real tool call/reply, it unlocks the full Standard toolset... The author found that the tool schema on that first request appears to be the decisive variable.
Anchored Standard(xiaobright/dsh-anchored-standard)做了一个聪明的混合方案：1. 第一次模型请求时，给 V4 Pro 呈现与 Minimal 基本相同的环境……2. 一旦发生第一次真实工具调用/回复，就解锁完整的 Standard 工具集……作者发现，第一次请求携带的工具 schema 似乎是决定性变量。
Reddit
u/for4f · 2026-08-15
the anchored standard result is the one that got me. 98/99 with the full toolset handed back after the first call kills the 'fewer tools = fewer mistakes' explanation, which was the obvious alternative. so it really does come down to what the model sees at init. kinda fits the RL story though. train a model inside one specific scaffold and it'll behave best inside that scaffold, first message included.
最让我震撼的就是 anchored standard 的结果。第一次调用之后就把完整工具集还回去，居然还能拿到 98/99 分，这直接干掉了‘工具越少=错误越少’这个最顺手的解释。所以关键真的就在于模型初始化时看到了什么。这也符合 RL（强化学习）的逻辑：模型在某个特定脚手架里训练出来，它就会在那个脚手架里表现最好，第一条消息也不例外。
Reddit
u/somerussianbear · 2026-08-16
I saw the dsh-anchored-standard plugin yesterday, the popularity of this thing and what it promised, and decided to give it a try on porting the approach to a Pi extension, cause I really like that harness and wouldn't like to have a harness just for DeepSeek. It works, I managed to replicate the whole thing. All thinking uses "We ...", as flagged by xiaobright as being the trace of a superior version of DeepSeek v4 Pro.
我昨天看到了 dsh-anchored-standard 插件，看它的热度和承诺的效果，决定把这套思路移植成 Pi 扩展试试，因为我很喜欢那个 harness，不想为 DeepSeek 单独养一个。它有效，我完整复现了整件事。所有思考都以‘We ...’开头——正如 xiaobright 标记的那样，这是某个更强版本 DeepSeek V4 Pro 的痕迹。
