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26 articlesFilter articles Projects & practice
Ideas to revisit
Projects & practice
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Impeccable:把 AI 前端设计要求变成可检查的工作流
Impeccable 把 PRODUCT.md、DESIGN.md、61 条确定性规则和浏览器组件 Review 组合成 AI Coding 的前端设计验收流程。
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Muse Gadgets 把 ESP32 和 Raspberry Pi 接进 Muse
Muse Gadgets 用 ESP32 与 Linux Device SDK 把屏幕、按钮、传感器、执行器、文件和命令接成 Muse 的设备端点。
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EmDash 1.0 separates plugin catalogs from publishing
EmDash 1.0 separates author identity, signed releases, plugin discovery, and runtime permissions. Installers verify records independently.
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OpenShell enforces agent permissions outside the workload
NVIDIA OpenShell places sandbox, network, credential, and policy controls outside the agent workload, with a Supervisor enforcing permission boundaries.
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OpenShell:把 Agent 权限检查放到运行时外层
NVIDIA OpenShell 把 sandbox、网络、凭证和 policy 控制放在 Agent workload 外,由 Supervisor 执行真实权限边界。
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MentraOS runs Miniapps on phones for multiple glasses
MentraOS runs Miniapps in a phone runtime, sharing a connection to manage input, output, hardware capabilities, and permissions across smart glasses.
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MentraOS:Miniapp 跑在手机上,同一运行时适配多款眼镜
MentraOS 把 Miniapp 放在手机 Runtime,通过共享连接管理多款智能眼镜的输入输出、设备能力和权限。
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Company Brain separates team memory permissions
Company Brain scopes team-agent memory by room and user permissions, with personal authorization, traceable sources, and separate tool-write access.
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Company Brain:团队 Agent 的记忆权限该怎么拆
拆解 Company Brain 的团队记忆与权限设计,重点看个人授权、共享知识、可追溯来源和团队 Agent 的权限边界。
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Jev-Code-Reviewer prioritizes large PRs as P0/P1/P2
Jev-Code-Reviewer prioritizes PR changes, expands P0 by default, and retains the original diff and fallback paths so reviewers see key changes first.
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Jev-Code-Reviewer:大 PR 先分 P0/P1/P2
Jev-Code-Reviewer 给 PR 改动做注意力排序,默认展开 P0,同时保留原始 diff 和失败回退路径,帮助人先看关键改动。
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GitHub Security Lab makes fuzzing an agent feedback loop
GitHub Security Lab Fuzzing Taskflow uses coverage gaps to guide model decisions while deterministic tools execute, measure, and persist results.
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GitHub Security Lab 把 Fuzzing 做成 Agent 反馈环
拆解 GitHub Security Lab 的 Fuzzing Taskflow,模型根据 coverage 缺口决定下一轮,确定性工具继续负责执行、测量与持久化。
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Link CLI lets agents pay without real card numbers
Stripe Link CLI separates purchase intent from payment permission. Agents request a purchase and receive one-time credentials only after user approval.
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Link CLI 让 Agent 付款,但不拿真实卡号
Stripe Link CLI 让 Agent 先提交购买请求,用户确认后再获得本次支付需要的一次性凭证,把购买意图和支付权限分开。
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LobsterAI: send tasks by phone, run them on your desktop
LobsterAI connects phone messages to a local desktop agent. The phone handles tasks and approvals while the computer works with files, terminals, and browsers.
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json-render constrains UI with a component catalog
Vercel Labs json-render defines components, actions, and data bindings before generating schema-constrained JSON that can be checked and replayed.
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json-render:先锁组件目录,再让 AI 生成界面
Vercel Labs 的 json-render 先定义组件、动作和数据绑定目录,再让模型生成符合 schema 的 JSON。生成式 UI 的边界因此可以验证、记录和回放。
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A Minecraft agent separates planning from fast decisions
This Minecraft agent uses Astra for slow planning, JEV for frequent action selection, and Mineflayer for execution, with logs and scripts to verify runs.
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Minecraft Agent 把规划和即时决策拆成两层
这个 Minecraft Agent 让 Astra 负责慢规划、JEV 负责高频动作选择、Mineflayer 负责确定性执行,并用日志和验证脚本保留运行证据。
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OpenCloak: replace personal data before sending it to AI
OpenCloak replaces names, emails, and other personal data with stable stand-ins locally, then restores them when the model responds.
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OpenCloak:发给 AI 之前,先在本地把个人信息换成假值
OpenCloak 在本地把姓名、邮箱等敏感字段替换成稳定假值,模型响应回来后再恢复,减少个人信息直接发送给 AI 服务。
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Splash: a local LLM inference stack built for each model
Splash combines model packages, kernels, memory planning, and compatible local APIs into a model-specific inference stack for coding agents.
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Splash:把本地 LLM 推理栈按模型重做
Splash 把模型包、Kernel、内存计划和本地兼容 API 组合成按模型定制的推理栈,面向 Coding Agent 的本机模型运行。
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Foreman: an independent supervisor for coding agents
How Foreman separates a coding agent from supervision, using runtime evidence, Jev judgments, and deterministic policy to continue, verify, stop, or escalate.
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Foreman:给 Coding Agent 加一个独立监工
拆解 Foreman 如何把 Coding Agent 的执行循环与独立监督分开,用运行证据、Jev 判断和确定性 policy 决定继续、验证、停止或交给人工。
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Skills Manager: sync one skill to multiple coding agents
Skills Manager distributes a local skill library to Claude Code, Codex, Cursor, and Gemini CLI while managing version and configuration consistency.
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Skills Manager:把一份 Skill 同步给多个 Coding Agent
介绍 Skills Manager 如何用本地 Skill Library 统一分发到 Claude Code、Codex、Cursor 和 Gemini CLI,并处理版本与配置一致性。
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HyperFrames makes video a buildable HTML project
How HyperFrames gives agent-generated video an inspectable workflow of HTML, animation, assets, lint, preview, and rendering.
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HyperFrames:把视频做成可构建的 HTML 工程
介绍 HyperFrames 如何把 Agent 生成视频拆成 HTML、动画、素材、lint、preview 和 render 可检查流程,让视频生产进入工程循环。
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RTK: filter shell output before it reaches a coding agent
RTK filters test, Git, search, and build noise before command output enters model context, applying context engineering at the tool-output boundary.
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RTK:Coding Agent 不该把整段 Shell 输出塞进上下文
介绍 RTK 如何在命令输出进入模型上下文前过滤测试、Git、搜索和构建噪声,把 Context Engineering 做到工具输出边界。
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OpenCompany: version-controlled agent configuration
OpenCompany uses company.toml, agent teams, and human sign-off to put organizational structure and agent configuration into version-controlled files.
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OpenCompany:把公司写成可版本控制的 Agent 配置
OpenCompany 用 company.toml、Agent Team 和 Human Sign-off 把组织结构与 Agent 配置写成可版本控制文件,并明确保留人工签字点。
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Munim Computer Use lets agents act on desktop controls
Munim Computer Use reads the accessibility tree and operates desktop controls by element ID, reducing reliance on fragile screenshot-coordinate automation.
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Munim Computer Use:让 Agent 按控件操作桌面
介绍 Munim Computer Use 如何先读取可访问性树,再按元素 ID 操作桌面控件,减少依赖截图坐标的脆弱自动化。
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jcode moves coding-agent bottlenecks into the harness
How jcode handles multi-agent resources, state synchronization, and verification in its harness, beyond the capabilities of the model alone.
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jcode:把 Coding Agent 的瓶颈从模型转到 Harness
介绍 jcode 如何把多 Agent 的资源、状态同步和验证放到 Harness 层,说明并行 Coding Agent 的瓶颈不只是模型能力。
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GitHub Agentic Workflows:把 Agent 装进 GitHub Actions
介绍 GitHub Agentic Workflows 如何把需要判断的自动化放进 GitHub Actions,同时保留权限、输出、审批和安全边界。
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GitHub Agentic Workflows: agents inside GitHub Actions
How GitHub Agentic Workflows puts judgment-based automation inside GitHub Actions while retaining permissions, controlled outputs, approval, and security.
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AI Engineering Harness: verifiable delivery for AI code
A multi-agent engineering workflow uses isolated worktrees, CI, cold-start review, evidence gates, and layered context to control delivery of AI-generated code.
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AI Engineering Harness:把 AI 代码放进可验证的工程闭环
介绍一个多 Agent 工程工作流,如何用独立 worktree、CI、冷启动审查、证据门和分层上下文控制 AI 生成代码的交付。
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Free Vision Skill: visual evidence for text agents
VEP/1 turns images into short evidence packets. Provider fallback, caching, and credential storage manage cost and reliability for text agents.
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Free Vision Skill:让文本 Agent 按需读取视觉证据
通过 VEP/1 把图片转换成短证据包,并用 Provider fallback、缓存和凭据存储控制文本 Agent 的视觉输入成本与可靠性。
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MiniMax image and video generation for Hermes Agent
A Hermes MiniMax plugin with image-01, Hailuo async video tasks, CN/global endpoint fallback, end-to-end tests, and a PyPI release.
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用 MiniMax 为 Hermes Agent 接入图片与视频生成
记录 Hermes 的 MiniMax 图片与视频插件实现,包括 image-01、Hailuo 异步任务、CN 与 global endpoint fallback、端到端测试和 PyPI 发布。
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StepFun image generation for Hermes Agent
The Hermes StepFun image plugin: directory layout, model routing, API calls, billing endpoints, version fixes, and publishing.
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用 StepFun 为 Hermes Agent 接入图片生成
记录 Hermes 图片生成插件接入 StepFun 的目录结构、模型路由、API 调用、计费 endpoint 差异、版本修复和发布流程。
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Building WishLive: A Multi-Agent Runtime
A 48-hour hackathon build of an event-driven multi-agent runtime with registry, workflow, execution, negotiation, settlement, and observability layers.
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Building a Full-Stack YouTube Clone with Next.js 15
A repository-backed walkthrough of NewTube: Next.js 15 structure, tRPC and Drizzle data flow, Mux video pipeline, auth, creator tools, and deployment tradeoffs.
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