Today's brief

morluto/rea

Reverse engineer anything with agents, from app behavior down to native binaries. English · 简体中文 · 日本語 · 한국어 · العربية REA: Reverse Engineer Anything One MCP for reverse engineering across binaries, applications, and runtime behavior. See a feature you like. Understand how it works, down to the binary level. Quick start · Current status · Investigation model · Tool catalog · Roadmap · How it works npx rea-agents setup Join the Reverse Engineering Community Discord · Q&A · Show and Tell See a feature in an app that you want in your own product? Ask your agent to investigate it with REA. It can inspect the app without its source code, explain how the feature works, show the evidence, and build a version for your project. REA connects your agent to tools for inspecting native binaries, JavaScript and Electron apps, .NET assemblies, and websites. You can also use the same tools from your terminal. Analysis runs locally, and results include the evidence and limitations behind each conclusion. Setup registers REA with your agent and installs matching workflow instructions. Native analysis can use an existing Hopper or Ghidra installation; setup can optionally install Hopper with approval. Static JavaScript analysis needs neither engine. Quick start Run setup (recommended) Set up REA with your agent: npx rea-agents setup Choose which supported agents should use REA, then review the exact paths and changes before approving. Existing REA registrations are selected by default; newly detected agents are available to select, but detection alone does not select them. Setup adds MCP access and REA's guided workflow for selected agents. Hopper is a separate optional choice with its own consent. Setup can also record an existing Ghidra installation. Setup shows its changes before applying them and backs up existing configuration. See Installation and setup for require…[truncated]

AnyPS5 是一款可将 PS5 可执行文件自动移植到 Linux 与 Windows 的开源工具，通过自定义重链接器与系统库实现原生运行，无需模拟器，目前已验证可运行部分 2D 游戏。但对产品设计师与 AI 设计师而言，这属于底层游戏逆向工程，与日常设计或创意工作流基本无关。

ArtCraft 是一款面向艺术家和设计师的交互式 AI 图像与视频创作 IDE，支持 2D/3D 合成、角色摆姿、场景分镜及多模型自选。它将提示词工程转化为可视化创作流程，让 AI 生图从随机抽卡变成可控、可重复的制作工序。

开源了一个 Claude Code Skill，内置 29 种编辑级图表模板，输出纯 HTML+SVG。它能自动抓取品牌网站的配色与字体，60 秒内统一图表风格，让 AI 直接画出可用在文章或演示中的高质量架构图与流程图，彻底告别 Mermaid 的粗糙圆角框。

Context Mode 是一款 MCP 服务器，通过沙盒隔离工具输出（减少 98% 上下文占用），并用 SQLite + BM25 实现会话事件的持久化与检索。当对话压缩时，它能确保 AI Agent 不丢失文件编辑、任务进度等关键状态，同时支持 17 个平台的路由。

TypeScript 教育家 Matt Pocock 开源了他自用的 Claude agent skills 仓库，提供一套小型、可组合、模型无关的工程化技能集合，附带一键安装脚本。不同于完全放任的 vibe coding，这套技能旨在修复 Claude Code 与 Codex 等工具的常见失效模式，让开发者保持对真实工程流程的掌控。

GitHub Trending 自动抓取了 Flutter 官方仓库条目，正文只是项目标准 Readme 的通用介绍，不含任何版本更新、功能发布或技术细节。对于寻找可用工具、组件或设计参考的目标读者而言，这条内容没有提供任何新的可操作信息。

GitHub Trending 上的 TensorFlow 仓库条目，内容仅为该机器学习框架的常规 README 介绍与安装说明，无任何新功能发布或设计相关更新。

市面上多数 AI PPT 工具只输出图片或不可编辑的幻灯片，PPT Master 直接生成原生 .pptx，保留矢量形状与动画，还能读取用户自有模板并自动配音。对需要高频产出提案或评审材料的设计师而言，可直接接入工作流，省去重复排版。

该条目仅为 GitHub Trending 对 PyTorch 主仓库的自动抓取，正文内容是项目的基础 README 摘录，涉及张量计算与动态神经网络等常规介绍，未包含任何新功能、版本更新或设计相关动态。

这只是一条 GitHub Trending 的算法推荐，附带了 Transformers 库的通用介绍，未涉及任何新模型发布、API 变更或设计工程相关的工作流更新，对设计师而言是零信息增量的噪音。

Anthropic 在 GitHub 开源 11 个面向知识工作者的插件，兼容 Claude Cowork 与 Claude Code，覆盖生产力、销售、客服等场景。每个插件捆绑 skills、connectors 与 slash commands，企业可按自身工具与流程深度定制，让 Claude 变成具备专业工作流的岗位专家。

multica-ai/andrej-karpathy-skills

A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls. Karpathy-Inspired Claude Code Guidelines Check out my new project Multica — an open-source platform for running and managing coding agents with reusable skills. Follow me on X: https://x.com/jiayuan_jy A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls. English | 简体中文 The Problems From Andrej's post: "The models make wrong assumptions on your behalf and just run along with them without checking. They don't manage their confusion, don't seek clarifications, don't surface inconsistencies, don't present tradeoffs, don't push back when they should." "They really like to overcomplicate code and APIs, bloat abstractions, don't clean up dead code... implement a bloated construction over 1000 lines when 100 would do." "They still sometimes change/remove comments and code they don't sufficiently understand as side effects, even if orthogonal to the task." The Solution Four principles in one file that directly address these issues: Principle Addresses Think Before Coding Wrong assumptions, hidden confusion, missing tradeoffs Simplicity First Overcomplication, bloated abstractions Surgical Changes Orthogonal edits, touching code you shouldn't Goal-Driven Execution Leverage through tests-first, verifiable success criteria The Four Principles in Detail 1. Think Before Coding Don't assume. Don't hide confusion. Surface tradeoffs. LLMs often pick an interpretation silently and run with it. This principle forces explicit reasoning: State assumptions explicitly — If uncertain, ask rather than guess Present multiple interpretations — Don't pick silently when ambiguity exists Push back when warranted — If a simpler a…[truncated]