Writing
Notes on systems I am building, checking, and learning from.
Browse 86 English articles and 93 Chinese articles. English translations link back to their original versions.Filter articles All articles
Should an agent keep going or start a fresh attempt?
Use When Agents Slow Down, Reflexion, Hyperband, and agent evaluation to compare continued revision with independent attempts under reproducible budgets.
Read this noteAgent 该继续跑,还是重新开一轮?读懂预算分配、记忆与评测
结合 When Agents Slow Down、Reflexion、Hyperband 和 Agent 评测,说明怎样比较连续修改与独立尝试,并设计可复现的预算分配实验。
Read this noteA starting point
Selected writing
- 01 What Tau solves by splitting an AI agent into three layers Tau separates model adapters, the agent core, and the application so tools and frontends can work together without depending on each other. Read this note
- 02 Connecting an agent loop to a frontend with events Follow tool execution and repeated model calls, separate streamed deltas from final messages, and explore frontend state with a model-free Python demo. Read this note
- 03 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. Read this note
Ideas to revisit
Long reads and practical guides
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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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Meta 的 AI 数学论文开始标注模型参与
Meta 公布 6 篇与 Muse Spark 协作的数学论文。更值得关注的是贡献来源、第二组复核、并行结果和形式化验证怎样进入公开论文。
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Google 让 Gboard 的联邦学习隐私可以被外部审计
Google 把 TEE、访问策略、远程证明、透明日志和可复现构建接成联邦学习的外部验证链,并已用于 Gboard 下一词预测。
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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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Agents API browser auth keeps passwords out of model input
How Agents API separates site approval, login credentials, and screenshots, and why network access and site approval cannot replace action-level confirmation.
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OpenAI 给 Agents API 加了浏览器,密码不走模型上下文
拆解 Agents API 的网站访问审批、独立登录通道与截图输出边界:为什么能联网不等于已授权,站点批准也不能代替高影响动作确认。
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Instant agent steering and recoverable tool calls
A close reading of Codex instant_interrupt: ending model output, continuing background tools, and preserving state when users change requirements mid-run.
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Agent 中途改需求:即时转向、抢占与可恢复工具调用
拆解 Codex instant_interrupt:用户在 Agent 运行途中补充要求时,模型输出如何收口,后台工具如何继续,以及状态怎样避免重复或丢失。
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Cloudflare redesigns its CLI for agents
Cloudflare launches cf in open beta, using Wrangler usage data to explain default JSON, command search, and typed configuration.
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Cloudflare 把 CLI 重新做给 Agent
Cloudflare 发布 cf open beta,用 Wrangler 的实际使用数据解释默认 JSON、命令搜索和类型化配置。
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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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Keep review evidence separate from compacted agent context
Independent Codex Guardian history shows why generation context and review evidence need separate records, with provenance through resume and rollback.
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主 Agent 压缩上下文后,审查证据为什么要独立保存
从 Codex Guardian 历史独立保存出发,解释生成上下文与审查证据为什么不能共用一份压缩摘要,以及 resume、rollback 应如何保留来源。
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Cloudflare data puts agent traffic on the website budget
Cloudflare and Fastly network data reveal repeated crawling, origin costs, access-policy questions, and the returns websites get from machine traffic.
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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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Why old CLAUDE.md rules can hinder a model upgrade
Use prompt audits, instruction layers, and migration evals to retain real requirements, reassess model workarounds, and enforce hard limits in code.
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模型升级后,旧 CLAUDE.md 为什么可能拖后腿
拆解 Prompt Audit、指令分层和迁移 Eval:模型能力变化后,哪些规则应保留,哪些模型补丁要重新验收,哪些限制必须下沉到 Hook 或策略层。
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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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Microsoft connects AI-built tools to app delivery
After Copilot Code builds an internal tool, Managed Runtime handles Git, dev environments, Entra ID, connectors, previews, deployment, and governance.
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Microsoft 把 AI 内部工具的发布链也接上了
Copilot Code 生成内部工具后,Managed Runtime 接管 Git、受控开发环境、Entra ID、连接器、预览、部署和企业治理。
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Agent 已获网络权限,为什么连接途中还要继续检查
从 Codex 网络策略更新出发,解释 HTTP 重定向、长响应与 WebSocket 为什么需要可撤销许可,以及工具权限和网络权限为什么必须分层。
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Why agent network permissions need ongoing checks
Codex network policy changes explain revocable permits for redirects, long responses, and WebSockets, and why tool and network permissions need separate layers.
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GitHub Agentic Autofix starts retaining repair experience
GitHub Agentic Autofix reads and writes Copilot Memory. How security repair knowledge crosses agent workflows, and why verification, scope, and expiry matter.
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GitHub Agentic Autofix 开始积累修复记忆
GitHub Agentic Autofix 会读取并写入 Copilot Memory。本文拆解安全修复经验如何跨 Agent 复用,以及验证、范围和过期为什么重要。
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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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Agent markets: 85% of the loss comes before negotiation
Anthropic's book market experiment locates most efficiency loss in preference representation. A look at model choice, preference evals, and market rules.
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Anthropic Agent 市场:85% 的效率损失发生在谈判前
Anthropic 的 Agent 市场实验显示,主要效率损失发生在偏好表示和匹配阶段。本文拆解模型选择、偏好 Eval 与市场规则。
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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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Claude takes a candidate to the lab, not a new CRISPR
About 950 Claude agents helped identify ART. Human wet-lab experiments confirmed short RNA expression, but its main biological function remains unknown.
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Claude 把候选推进湿实验,但这不是新 CRISPR
Anthropic 用约 950 个 Claude Agent 从大规模逆转录酶候选中筛出 ART,并由人类湿实验验证短 RNA 表达,但 ART 的主要功能仍未知。
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Financial markets from zero: stocks, Bitcoin, and risk
A beginner learning path through stocks, ETFs, indexes, Bitcoin, candlesticks, position sizing, paper trading, backtesting, and tax basics.
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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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Debugging agents with traces, spans, and OpenTelemetry
The GitHub Copilot OpenTelemetry update explains agent traces, spans, metrics, events, content capture boundaries, and a telemetry adapter for Lora PI Kit.
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Agent 有日志还查不清?读懂 Trace、Span 与 OpenTelemetry
从 GitHub Copilot 的 OpenTelemetry 更新出发,解释 Agent Trace、Span、Metrics、Events、内容采集边界,以及 Lora PI Kit 如何接入标准遥测。
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GPT-6 prompt caching becomes an agent state layer
GPT-6 adds cache breakpoints, diagnostics, prewarming, and mutable configuration. Long-running agents need stable shared prefixes and cache-hit monitoring.
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GPT-6 Prompt Cache 变成了 Agent 的状态层
GPT-6 增加 cache breakpoint、诊断、预热和可变配置。长跑 Agent 需要主动保持共享前缀稳定,并监控缓存命中率。
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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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Deploying agent backends with Python Workers GA
Python Workers GA supports web frameworks and agent libraries. WebAssembly package compatibility remains the main migration boundary.
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Cloudflare Python Workers GA 后,Agent 后端怎么部署
Python Workers 已进入 GA,FastAPI、Django、Flask 以及 openai、langchain、mcp 可以直接运行在 Workers。真正的迁移边界仍是 WebAssembly 包兼容性。
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Why pinning a Git SHA can still run different code
Plugin4Shell exposes a supply-chain gap: a pinned SHA records the expected version, but installers must also verify the actual HEAD after checkout.
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固定 Git SHA 为什么还可能跑到别的代码
Plugin4Shell 暴露了一个供应链边界:固定 SHA 只写下期望版本,安装器还必须在 checkout 后验证真实 HEAD。本文结合 Git 文档、本地无害复现和 GitHub 分支限制解释机制。
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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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When to split a task across agents: check dependencies
How task dependencies shape multi-agent results, with SAIGE experiments, ablations, and OpenAI guidance for deciding when to delegate.
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一个任务什么时候该拆成多个 Agent?先看依赖关系
Rethinking Multi-Agent Collaboration 说明多 Agent 的收益取决于任务依赖结构。本文结合 SAIGE 主实验、消融实验和 OpenAI 多 Agent 工程建议,整理可执行的委派判断。
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Qwen-Image 2.1: native RGBA and commercial licensing
Qwen-Image 2.1 offers native RGBA, unified generation and editing, up to 10 reference images, and 2K output. Commercial use needs a separate license.
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Qwen-Image 2.1 原生透明图很适合做资产,但商用要另取许可
Qwen-Image 2.1 支持原生 RGBA、统一生成编辑、最多 10 张参考图和 2K 输出,但仓库许可证默认只授权非商业研究与评测。
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How to score a coding agent that finishes 80% but fails
Android Bench 2.0 keeps pass rate as a hard gate and adds completion rate to diagnose progress and unfinished acceptance requirements.
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Coding Agent 做完 80% 还失败,Eval 怎么记
Android Bench 2.0 保留 Pass rate 作为硬门禁,再用 Completion rate 解释长任务已经完成多少,以及问题卡在哪一类验收。
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FDE growth: 519 overseas jobs, 28 in China, 6 projects
A study of 519 overseas job listings, 28 roles at major Chinese firms, and 6 projects examines FDE responsibilities, growth, costs, and scalability.
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FDE 为什么突然火了:519 条海外 JD、28 条国内大厂岗位和 6 个真实项目
用 519 条海外公开 JD、28 条国内大厂岗位和 6 个真实项目,拆解 FDE 的职责、增长、国内落地、成本与规模化条件。
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Jev gives agents typed decisions instead of generated text
TypeSafe Jev uses Choice, Score, and Noul for constrained agent decisions while code retains control of valid actions and side effects.
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Agent 里不是每个判断都需要生成文本:Jev 把决策交给代码
TypeSafe Jev 用 Choice、Score 和 Noul 处理 Agent 的受约束决策,让模型负责模糊判断,代码继续掌握合法动作和副作用。
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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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Why you cannot just rerun an agent after a crash
WorkflowAgent, step boundaries, and idempotency keys explain safe recovery after crashes, timeouts, and human pauses, with a proposed Lora PI Kit adaptation.
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Agent 跑到一半进程挂了,为什么不能直接重跑?
用 WorkflowAgent、Step 边界和幂等键解释 Agent 在崩溃、超时和人工暂停后怎样安全恢复,并给出 Lora PI Kit 的迁移思路。
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Anthropic opens internal processes to embedded evaluators
Anthropic and Accenture extend evaluation beyond model outputs to training, deployment, monitoring, and incident processes, with independence unresolved.
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Anthropic 让第三方 Eval 进入内部,模型评测开始看过程
Anthropic 与 Accenture 的 embedded evaluator 计划把评测范围从模型输出扩展到训练、部署、监控和事故流程,也暴露出独立性边界。
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Where agent credentials belong: host, vault, and broker
Where keys belong when coding agents call GitHub, databases, and cloud APIs, and how vaults and credential brokers define access boundaries.
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Agent 的生产密钥该放哪里?Host Boundary、Vault 与 Broker
解释 Coding Agent 调用 GitHub、数据库和云 API 时,应用密钥、执行器密钥和第三方凭据该放在哪里,以及 Vault 与 Credential Broker 的边界。
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skill-up: evaluate real stateful multi-turn sessions
Alibaba skill-up resumes real sessions turn by turn, letting skill evaluations test context continuity, intermediate state, and per-turn assertions.
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多轮 Skill Eval 不该把对话压成一包:skill-up 开始续接真实 Session
Alibaba skill-up 的 stateful 多轮模式逐轮恢复真实 Session,让 Agent Skill Eval 能检查上下文连续性、中间状态和逐轮断言。
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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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Why more agent tools can mean worse tool selection
How Tool Search loads definitions on demand, affects prompt caching, and differs from MCP connections and ARD resource discovery.
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Agent 工具越接越多,为什么反而更容易选错?读懂 Tool Search、MCP 与运行时发现
解释 Tool Search 如何按需加载工具定义,为什么它会影响 Prompt Cache,以及 MCP、Tool Search 和 ARD 分别解决什么问题。
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Claude merges Chat and Cowork to reduce mode choices
Claude combines Chat and Cowork in one conversation, routes capabilities automatically, and keeps task state and permissions visible.
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Claude 把 Chat 和 Cowork 合并后,Agent 产品开始减少模式选择
Claude 把 Chat 与 Cowork 合并到同一会话,把能力路由交给系统,同时保留任务状态和权限确认,给 Agent 产品一个具体 UX 边界。
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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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Gemini Live: turnComplete no longer means the task is done
Gemini Live Extended Thinking separates conversation turns from background task lifecycles. Clients need interaction_status to track actual completion.
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Gemini Live 把推理放到后台:turnComplete 不再等于结束
Gemini Live Extended Thinking 把对话回合与后台任务生命周期拆开,客户端需要用 interaction_status 管理真正的完成状态。
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Turn production agent failures into regression tests
Use Kitaru Replay, Evaluator, and Regression Suite to preserve production failures as repeatable agent tests, with tool policies and CI gates.
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Agent 线上出过的错,怎么自动变成以后每次改动都要重跑的测试?
结合 Kitaru 的 Replay、Evaluator 和 Regression Suite,说明怎样把线上失败固定成可重放的 Agent 回归测试,并用工具策略与评测结果接入 CI。
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长任务为什么不能只靠一条越来越长的对话
结合 OpenAI Agents API 和 Anthropic Managed Agents,解释 Session、Context Window、Compaction 与持久状态怎样支撑跨上下文窗口的长任务 Agent。
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Why long tasks need more than one growing conversation
How OpenAI Agents API and Anthropic Managed Agents use sessions, context windows, compaction, and persistent state for long-running agents.
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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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What Tau solves by splitting an AI agent into three layers
Tau separates model adapters, the agent core, and the application so tools and frontends can work together without depending on each other.
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Connecting an agent loop to a frontend with events
Follow tool execution and repeated model calls, separate streamed deltas from final messages, and explore frontend state with a model-free Python demo.
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AWS agents propose; deterministic code validates
How AWS separates agent proposals, rule checks, human approval, and execution, with code enforcing boundaries and idempotency.
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A reliable agent might leave the code alone today
GitHub Agentic Workflows jsweep checks whether a change is worthwhile and treats noop as valid, instead of forcing automation to produce a diff.
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一个靠谱的 Agent,今天可能不改代码
从 GitHub Agentic Workflows 的 jsweep 看 Agent 如何先判断有没有值得修改的目标,把 noop 作为正式结果,而不是为了自动化强行制造改动。
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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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Herdr turns the terminal into an agent runtime
How Herdr manages existing coding-agent terminals with persistent sessions, blocked states, cross-machine control, and agent coordination.
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Herdr 不替换 Agent,它把终端做成运行时
拆解 Herdr 如何接管现有 Coding Agent 所在终端,提供持久会话、阻塞状态、跨机器控制和 Agent 协作,而不重写底层 Agent。
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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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Google packages Skills and MCP in one plugin
How the Google Cloud Developer Plugin packages Skills, MCP configuration, and rules, and what this means for distribution, versions, and permissions.
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Google 把 Skill 和 MCP 打成一个插件
拆解 Google Cloud Developer Plugin 如何把 Skill、MCP 配置和规则按 Agent Plugins 规范打包,暴露跨 Coding Agent 的分发、版本与权限问题。
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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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Agentic search missed an incident. Test the eval too
The Anthropic incident review shows why automated monitors and LLM graders need coverage, independence, and tests for their own failure modes.
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Agentic Search 漏掉第四起事故,Eval 也要被测试
从 Anthropic Agentic Search 事故复盘看自动监控和 LLM grader 的漏检问题,说明 Agent Eval 也需要覆盖率、独立性和失效模式测试。
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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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OpenAI agent workdays and shifting builder bottlenecks
OpenAI organization-level data suggests that more coding-agent use shifts bottlenecks to task decomposition, concurrency, harnesses, evaluation, and selection.
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OpenAI 研究组织跑到 3.1 Agent 工作日后,Builder 的瓶颈开始转移
OpenAI 的组织级数据说明 Coding Agent 使用规模上升后,瓶颈会转向任务拆分、并发调度、Harness、Eval 和结果选择。
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Agent demos are easy. The harness is the hard part
Strands harness-sdk, Harness MCP Server, and HydraFusion show why agent engineering needs control over context, permissions, state, validation, and recovery.
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Agent Demo 不难,难的是 Harness
从 Strands harness-sdk、Harness MCP Server 和 HydraFusion 看 Agent 工程为什么从模型调用转向上下文、权限、状态、验证与恢复控制。
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Coding agent security is now a supply-chain problem
Coding agents read and execute skills, MCP tools, dependencies, and repository instructions. These need software supply-chain controls.
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AI Coding Agent 的安全问题已经进了供应链
说明 Coding Agent 会读取并执行 Skill、MCP、依赖和仓库指令,因此这些组件应按软件供应链治理,而不只是做 Prompt 安全。
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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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Vercel Eve Agent Framework
A source-backed review of Vercel Eve’s filesystem-first agent model, durable execution, tools, sandboxes, approvals, deployment, and Vercel platform tradeoffs.
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Loop Engineering Harness: controlling agent iterations
Break down planning, execution, evaluation, state management, and stopping rules to keep multi-step agent tasks controlled and traceable.
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Loop Engineering Harness:怎样控制 Agent 的迭代循环
把 Harness 作为一种工程模式,拆解计划、执行、评估、状态管理和终止判断,说明 Agent 多轮任务怎样保持可控和可追溯。
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Sandbox isolation: boundaries, implementations, and practice
How sandboxes limit untrusted code access to files, processes, networks, and resources, comparing VMs, containers, user-space kernels, and syscall filters.
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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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