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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 現在是成長最快的 GitHub 儲存庫，不到 48 小時破 10 萬星，比 OpenClaw 還快。社群反應非常正面：> 架構設計出奇地好——工具、session 日誌、agent 迴圈、子 agent 全都是可替換的外掛 > UI……（內文被 API 截斷）
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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
非常出色的發布。它是一個內嵌多個 harness 的 Web UI，你可以透過 SDK 生成(spawn)Claude Code 和 Codex agent。DeepSeek Harness 預設支援多種‘模式’（本質就是不同的 harness)：帶程式化工具呼叫(TypeScript)的 code mode、bash+edit……（內文被 API 截斷）
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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 基於 Cordis 的設計，可能是我看過最乾淨的 agent 架構表達之一：每一項能力——模型轉接器、工具註冊表、session 日誌、沙箱、編排迴圈、甚至 UI——都是帶型別化服務(typed services)的一等公民外掛……（內文被 API 截斷）
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)做了一個聰明的混合方案：1. 第一次模型請求時，給 V4 Pro 呈現與 Minimal 基本相同的環境……2. 一旦發生第一次真實工具呼叫/回覆，就解鎖完整的 Standard 工具集……作者發現，第一次請求所帶的工具 schema 似乎是決定性變數。
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.
最讓我震撼的就是 anchored standard 的結果。第一次呼叫之後就把完整工具集還回去，居然還能拿到 98/99 分，這直接推翻了‘工具越少=錯誤越少’這個最順手的解釋。所以關鍵真的在於模型初始化時看到了什麼。這也符合 RL（強化學習）的邏輯：模型在某個特定鷹架裡訓練出來，它就會在那個鷹架裡表現最好，第一則訊息也不例外。
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 dsh-anchored-standard 外掛，看它的熱度和承諾的效果，決定把這套思路移植成 Pi 擴充套件試試，因為我很喜歡那個 harness，不想為 DeepSeek 單獨養一個。它有效，我完整重現了整件事。所有思考都以‘We ...’開頭——正如 xiaobright 標記的，這是某個更強版本 DeepSeek V4 Pro 的痕跡。
