agent-mcp: Turning Agent CLIs Into One Work Poolagent-mcp：把 Agent CLI 收成一个工作池
Opening more terminals is easy; staying in control is hard. The main agent only splits and merges — dispatch, timeouts, and resume go to the control plane, and each CLI gets matched to the task.
多开终端不难，难的是可控。主 Agent 只拆解汇合；派发、超时、续接交给控制面，CLI 按任务匹配。
EduEvidence: No Evidence, No New Study Designeduevidence：没有证据，就不设计新研究
Retrieved snippets are not evidence. A nine-stage protocol + evidence graph that outputs ADOPT, PILOT, REJECT, or INSUFFICIENT EVIDENCE.
检索片段不是证据。九阶段协议 + 证据图，给出 ADOPT、试点、驳回或证据不足。
Keep reading the full archive below.
d-token: Before Context Leaves Your Machined-token：上下文离开本机之前
Compress redundancy, make routing explicit, and leave a receipt — before the request goes out. One real request sent 33,705 fewer tokens.
在请求发出前压缩冗余、写清路由、留下回执。一次真实请求少送 33,705 token。
agent-mcp: Turning Agent CLIs Into One Work Poolagent-mcp：把 Agent CLI 收成一个工作池
Opening more terminals is easy; staying in control is hard. The main agent only splits and merges — dispatch, timeouts, and resume go to the control plane, and each CLI gets matched to the task.
多开终端不难，难的是可控。主 Agent 只拆解汇合；派发、超时、续接交给控制面，CLI 按任务匹配。
EduEvidence: No Evidence, No New Study Designeduevidence：没有证据，就不设计新研究
Retrieved snippets are not evidence. A nine-stage protocol + evidence graph that outputs ADOPT, PILOT, REJECT, or INSUFFICIENT EVIDENCE.
检索片段不是证据。九阶段协议 + 证据图，给出 ADOPT、试点、驳回或证据不足。
ompweb: A Local Workbench for ompompweb：给 omp 一张本地工作台
No rewriting the agent — just read the local session files. Session tree, terminal, MCP, and worktrees, all on one desk.
不重写 Agent，只读本地会话文件。会话树、终端、MCP、worktree 都摊在一张桌子上。
Write Agent Skills Like Reusable Lesson Plans把 Agent Skill 当成可复用教案来写
The biggest time sink in student AI projects isn't a weak model — it's re-explaining the same workflow every time. Writing workflows as Agent Skills is like writing experiment steps into a reusable lesson plan.
学生做 AI 项目时最容易浪费时间的，不是模型不够强，而是每次都要重新解释同一套流程。把工作流写成 Agent Skill，就像把实验步骤写进可复用的教案。
From Desktop App to Claude Skill: Distilling a Design System's Reusable Logic从桌面应用到 Claude Skill：如何提炼设计系统的可复用逻辑
Using Open Design as an example: how to break down an app with 149 design systems, 110 templates, and 11 craft specs into a clean Claude Code Skill. Core ideas: three-axis composition, six-part assembly, advisor-style interaction.
以 Open Design 为例，讲解如何将一个包含 149 个设计系统、110 个模板、11 套工艺规范的应用，拆解为一个结构清晰的 Claude Code Skill。核心思路：三轴组合、六段拼装、顾问式交互。
Ship a Full-Stack AI App in 3 Days with Vibe Coding用 Vibe Coding 三天搞定一个全栈 AI 应用
From idea to launch: building scholar-ai. How AI-assisted coding helped ship a FastAPI backend and a React frontend fast, plus real-world lessons from implementing RAG.
从想法到上线，记录 scholar-ai 的开发过程。如何利用 AI 辅助编码，快速搭建 FastAPI 后端和 React 前端，以及 RAG 检索增强生成的实际落地经验。
SwiftUI vs. AppKit: Knowing When to SwitchSwiftUI 与 AppKit 的取舍
Lessons and pitfalls from building macOS tools. The conveniences and limits of SwiftUI, when to go back to AppKit, and how to do on-device inference with Core ML.
在开发 macOS 工具时遇到的坑和经验。SwiftUI 的便利与局限，什么时候该回归 AppKit，以及如何利用 Core ML 做本地推理。
File Format Conversion on AndroidAndroid 文件格式转换方案
How to do quality file format conversion on Android. The on-device vs. cloud API tradeoff, plus UI practice with Jetpack Compose.
如何在 Android 上实现高质量的文件格式转换。本地处理 vs 云端 API 的权衡，以及 Jetpack Compose 的 UI 实践。
On-Device AI Models in Practice本地 AI 模型实战
Exploring what local AI models can do on mobile. TFLite image processing, offline translation model integration, and performance optimization.
探索在移动端运行本地 AI 模型的可能性。TFLite 图片处理、离线翻译模型的集成，以及性能优化技巧。
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