You can launch 100 coding agents. Who keeps them moving?
Six rules for running the software development lifecycle across a fleet of coding agents, November 2026.Over the last 30 days, coding agents...
Topic
#AI
133 篇笔记
Agents Are Going Async
Imagine a chief of staff who disappears every time you hand them a big project and only comes back once it's done. You'd probably fire them ...
Computer Use 主流方案与工具调研
🖥️ Computer Use 主流方案与工具调研（2026-11） [!summary] 一句话结论 Computer Use 已经分成四层：模型层（Claude / GPT / Gemini / UI-TARS / Fara / Qwen-CUA）、Agent Harness 层（Cowork / Agent TARS / Agent S / Browser Use）、环境层（E2B / O…
Enterprise Evals 101
Imagine your worst nightmare: your agent passes every demo, then a customer asks something you never tested, and it fails in public.In April...
The components of agent memory
For Claude to learn from experience, it has to decide what’s worth remembering before it knows what’s next. Here’s how “dreaming” helps it organize memory and correct mistakes.
Designing an Always-On Agent
Chatbots wait for a message. An always-on agent is single-threaded, proactive and situationally aware. Here's how we design them, and what models still lack.
Software Factory Engineer: Job Description
An emerging engineering role for the transition from AI-assisted development to L4 Autonomous Software Factories.The RoleWe are looking for ...
ThoughtWorks 技术雷达 Vol.35 摘要
ThoughtWorks 技术雷达第 35 期（2026 年 11 月）中文摘要：四大主题、117 个条目与团队行动建议。
Agent State Machines: Building Deterministic Scaffolding for Agents
If you're not using some type of agent state machine, you're completely at the mercy of the nondeterministic nature of models. Are you reall...
Concurrent agents on one file system
I think there's a lot of confusion out there about what a file system really is. There's lot of places where we can assign blame.People's fa...
The most lucrative AI business to start in 2027
The most lucrative skill has always been capturing attention.It’s obvious why.If your goal is making more money, it doesn’t matter how good ...
Critical thinking when agents do the work
Back in the day, critical thinking was taught as approaching new info with humble curiosity and a healthy dose of skepticism - take in diffe...
The era of personal software
📝 摘要 作者以自己构建的健康数据追踪应用为例，讨论 LLM 时代「个人软件（personal software）」的兴起：当构建成本足够低，软件将逐渐商品化，越来越多人会为自身需求量身定制工具。他完整记录了实现链路——Apple Health 数据经 Health Auto Export 每小时导出 JSON，通过 端点写入 $5 的 PlanetScale Postgres；数据库 sche…
Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights
We find that frontier LLMs are surprisingly good at natively reading and writing neural network weights, well past what we’d expect from humans or conventional algorithms.
腾讯 2026 AI Agent 企业解决方案及落地实践手册
腾讯、腾讯云与 WorkBuddy 联合发布的 78 页《2026 AI Agent 企业解决方案及落地实践手册》精读：七类提效场景与 WorkBuddy / ClawPro / Agent Runtime / TokenHub 四件套，三阶段跃迁与三代办公，研发运维、SaaS、内容生产、AI 社交陪伴、游戏 NPC、智能客服与生态共建的方案和案例。
bliki: LLM Voice
The uncanny valley of LLMs' prose
Building an AI-native engineering org
Last week Will Larson (@Lethain) asked how leaders are evaluating engineering managers for AI skills. My reply, a list of questions to think...
vLLM Semantic Router Releases d3-ultra: A 27B Open Multimodal Decision Model
vLLM Semantic Router released d3-ultra, a 26.09B Apache-2.0 multimodal decision model scoring 87.2 on its own SR-Text benchmark.
コードを読まずにマージするために必要なこと
📝 摘要 本文讨论「no look merge」——开发者完全不读代码就合并 AI 产出的 PR——究竟需要哪些前提。作者认为，仅把质量确认自动化（测试验证行为 + AI 做设计与安全评审、修正后重新验证、存在未执行验证或未解决指摘就不合并）并不足以移除人工审批，真正的障碍在于组织共识：如果漏检后开发负责人仍追究个人责任，那实际上等同于要求人继续读代码。文章引用 2021 年的 Ship / S…
The new set of problems for engineers to solve
A lot of software engineers are going through what can only be described as an identity crisis. The job has changed a lot. Some are depresse...
多 Agent 协作终极解：实测 YC 开源新作 Agent Relay，让 Claude Code 和 Codex 直接对话
这周，美国创业孵化器 Y Combinator 2026 年秋季营（YC Fall 2026）的创业公司 Agent Workforce 发布了开源项目 Agent Relay。公司的两位联合创始人分别是 CEO Will Washburn 和 CTO Khaliq Gant，其...
How I work with Agents
... as of October 2026.Use the latest and greatest models. Everything else is potentially a waste of time. If a model is wrong 10% of the ti...
What FDE means at OpenAI
What forward deployed engineering means at OpenAI
用 Claude 做动画，全网最全攻略：4 条路线、11 种风格、30+ 工具和案例、全部素材来源（💗建议收藏⭐）
一条 3 分钟的梵高动画：名画在金画框里一笔一笔画出来，旋涡会动，信封会飞，拍卖槌会落下。没有下载一张图，没有打开剪辑软件，我自己也没写一行代码。画面全是 Claude 写程序“画”出来的。做完这条，我把市面上用 Claude 做动画的玩法全部扒了一遍：官方工具、开源项目、海外爆...
Agent Compute Engine deep dive | Google Cloud Blog
Traditional compute primitives are not built to run hundreds of millions of these agents at planet scale, which led us to build Agent Compute Engine.
Lemo-Opuscar 史诗级更新：8种新风格+新改编玩法，我把风格库变成了风格制作工具箱
Hello 大家好，我是lemomo。这次 Lemo-Opuscar 迎来了上线以来最大的一次更新，不光有新的风格，还有新的玩法，整个项目定位从【视频风格库】变成【搭建自己风格库的工具】。新增【剪辑与纪实】8 种风格，目前从最早的39种已经更新到了59种。未来会继续更新，我的目标...
LLMs are making formal software verification feasible at a much larger scale
Claude formally proved security properties in the compiled code of 123 open-source libraries. Along the way, it found bugs that had eluded detection for years.
How To Use Decision Models for Evals
Co-authored with Antaripa Saha.Decision models have exploded onto the scene recently, starting with TypeSafe’s Jev, followed by Perplexity’s...
Inspect AI 调研（索引）
调研笔记已归档到知识库： 👉 20-Areas/AI/Agentic/Harness/Inspect AI 调研 一句话结论：Inspect AI 是 UK AISI 出品的 LLM 评测框架（MIT，0.3.282），核心抽象是 ，把工具调用、Agent、沙箱、日志回看做成一等公民，配 的 200+ 预置 benchmark 可直接横向扫模型。
How Uber connects its AI agents to models and tools
In this blog we will see how Uber puts two gateways around its AI agents, and how every call carries the person who asked.
形式化验证 LLM 生成的代码（FATTW 2026, Lucas Cordeiro）
FATTW 2026 会议演讲（与 ASE 2026 联合举办）：LLM 负责猜测（不变量、契约、harness），证明引擎负责裁决（ESBMC/k-induction/Vampire）。以 310,531 个 C 程序基准、Test-Comp/SV-COMP 竞赛成绩和 AWS/NVIDIA/vLLM 生产代码漏洞为证据，论证『模型建议 + 证明引擎确认』的设计原则。
AI Agents Are Having Their Kubernetes Moment
Ten years ago, we saw infrastructure responsibilities moving out of microservices and into Kubernetes, service meshes, and proxies. Applicat...
A deep dive into OpenSEO
Explore OpenSEO's architecture: SEO data APIs, Cloudflare Workers, its audit crawler, MCP tools, AI agents, storage, and self-hosting trade-offs.
How to be more ambitious: A tutorial for humans with models
I work on Claude. It's an interesting experience: Every day I learn about yet another feat accomplished with or by Claude that I hadn't considered, didn&
Redesigning source control at Anthropic
As our merges per day grew more than tenfold, source control became our biggest development bottleneck. Here, we share how we migrated to an in-house version control system in five months.
「万字总结」- 我偷来了一份成为 Agent 工程师的学习线路
我之前面试了一家做 AI + web3 业内比较厉害的公司，但是没有通过吧，面试之后面试官给我了一份《Agent工程师入门指南》。我把它整理了一下，分享出来给大家提供参考，这是一份很详细很详细的、关于如何成为 Agent 工程师的文档，并且补充上我自己的学习心得当时给我的答复是 ...
来自 every 的自我批评，不应该让每个员工一个 Agent
Every 是一家做 AI 应用和内容的公司，最近把自己用了半年的企业 Agent 公开，同时批评自己之前的做法：受 OpenClaw 的影响，Every 为员工每人一个 agent，但在实践过程中，发现了在「维护」层面产生了诸多的问题，导致这种方法不可持续。它的联合创始人兼 C...
入职大厂两个月，我的 AI 开发心得
背景起因是和组里老板进行了几次 one-on-one。他对我目前使用 AI 工具、搭建个人 Harness，以及 AI Coding 的工作流很感兴趣。主要也是因为，我现在的需求做得又快又好，已经是一些项目的主 O 了。因此，我也借这个机会，整理了一下自己的日常 AI 工作流。目...
How to Cut Your Agent Bill by 90% With Jev Engineering and Kimi K3
How to find the decisions hiding inside your agents, write questions a system one model answers well, route on calibrated confidence and han...
The 5 levels of AI agents. From a single prompt to a production agent (Complete course)
There are five words people throw around about agents right now. Context engineering, loop engineering, Jev engineering, harness engineering...
Jev 模型调研 — TypeSafe AI 的 System One 模型
Jev 模型调研 — TypeSafe AI 的 System One 模型 [!info] 一句话概括 Jev 是 TypeSafe AI 于 2026-09-14 发布的首个 System One 模型。它不生成文本，而是输入非结构化状态、输出带校准概率的类型化决策——可以理解为"一个具有前沿智能的函数调用"。 1. 背景 - 公司：TypeSafe AI，隐身（stealth）两年后公开…
The Evaporation of Software Engineering (and the Rise of the Agentic Builder)
If you’re like me, you likely thought software engineering was a safe and secure job …In fact, I never thought I’d have to worry about a job...
How to become a Robotics Engineer in 6 months (RESOURCES)
Robotics is the least crowded high-value skill in tech right nowThe problem is that almost nobody knows where to start, because the field lo...
Building the missing layer between AI spend and ROI
Author: René SultanAI telemetry tells us which models our agents use and what they cost, but not what we are paying them to do. We built a s...
From rg to zg: Local Search Beyond Keywords
Summary: The information humans and agents need is often scattered across large numbers of local files, making it difficult to locate accura...
Your Inference Server is Secretly a Learner: Open-Sourcing Reef for Continual Self-Improving Agents
1. Before all the “RSI” hype becomes realBefore today's excitement around RSI is fully realized, we want to open-source Reef, an infrastruct...
Memory Is a Compiler
tl;dr: Memory preserves the past in a form meant to be useful in the future, then recompiles what that past means as new evidence and new qu...
There's no reason for software to be slow anymore
📝 摘要 Dan Luu 论证 AI 编码智能体（coding agents）已将软件性能优化的成本降低了多个数量级——过去需要专业性能工程师数天/数周完成的工作，现在普通人输入几句话即可完成。文章通过多个案例展示这一趋势：FRE 正则引擎的 AOT 编译器切换为 ripgrep 带来 7% 提速；Azul 游戏 AI 借助 LLM 实现多线程和 10-20 项手工难以完成的优化，远超人工编写的…
AI agents meant to replace Meta workers made “large-scale, disruptive actions”
Report shows Meta's challenges replacing people with AI agents.
AI Chip Architectures
A look at AI Chip Architectures. NVIDIA, AMD, TPUs, Trainium, Groq, Cerebras.
Harbor Framework 深度调研
Harbor Framework 深度调研 一句话概括：Harbor 是一个开源框架，用于在容器化沙箱环境中评估和优化 AI Agent 及 LLM——从跑 benchmark、生成 RL rollout 到 prompt 优化，一站式解决。 |---| | GitHub | harbor-framework/harbor | | Stars | 4,500 | | License | Apach…
Moats in the age of floods
As the labs break revenue records and absorb more capabilities, there's a growing narrative that the great flood of intelligence they bring ...
Recursive Self-Improvement
Agents can already edit their tools, skills, and harness. Recursive self-improvement still needs a system that can raise the verifier without capturing it.
Codex as a platform: build on the open agent harness
Build Codex into the products and workflows your users already know.
Domain-Driven Design matters more when AI writes your code
The domain model and Ubiquitous Language matter more than ever when an AI agent writes your code.
An open source rival to Claude Managed Agents just launched
TrueFoundry's open source TrueForge challenges Claude Managed Agents with vendor-neutral model choice, claiming 50% lower agent costs and no lock-in.
Fragments: August 18
fragments 18 Aug 2026
From context to dreams: architecting memory for AI agents
Have you ever felt that every conversation you have with an LLM across sessions feels like starting over from scratch? LLMs have a problem: ...
Designing Loops for Production-Grade Work
What became standard practice after vibe-coding a production-grade BPE tokenizer trainer without reading a single line of code.
Optimizing for cost and intelligence
Balance cost and intelligence on the Claude Platform, with measured results for prompt caching, effort, model choice, budgets, and multi-model strategies.
Patterns and problems in multiagent systems
We ran experiments on swarms of Claude agents and found coordination failures, collusion, and sabotage. Here, we share what they mean for AI safety.
The AI Engineering Skills Map
I am delighted to present The AI Engineering Skills Map. AI allows us to build software very differently today than in 2022, and everyone wi...
Why model routing must be in the harness
Inference spend is rising as agents are deployed more widely, take on more complex tasks, and run longer autonomously. Enterprises are start...
"Issue tracking is dead"; How the pull request became the last chokepoint in the SDLC bottleneck
CodeRabbit's new Agentic Change Management layer reimagines code review as AI agents outpace human teams' ability to plan and prioritize work.
TDD inside the agent loop - theater or actual value?
Notes from my Thoughtworks colleagues on AI-assisted software delivery
7 rules for self-improving agent loops every AI engineer should know
By @Saboo_Shubham_ and @secchi_eliaWhen coding agents write the code, defining what's good is the real engineering job.Coding agents can now...
The human is the loop
After a few weeks away from AI, I see my usage wasn't always productive or healthy.
How to Build a Shared AI Harness for Your Team
I am going to show you how to turn the models, agents, skills, and automations living across your team's tools and workflows into one coordi...
Your Agent Remembers Everything and Understands Nothing
Agent memory is where analytics was for years, returning what you asked for and nothing more. Then analytics started surfacing which number ...
DeepSeek 刚支持了 OpenAI Responses API，但你可能还不知道它是什么
2026 年 7 月 31 日，DeepSeek 悄悄发布了 V4-Flash-0731，附带一行更新日志：原生支持 OpenAI Responses API。消息不大，但背后藏着一条值得认真讲的故事线。一套 API 格式，是怎么从聊天框时代的产物，一步一步演化成今天 agent...
Five AI rivals just backed a shared plugin standard. Here's why it matters for developers.
OpenAI, AWS, Cursor, GitHub and Microsoft back Vercel's new Agent Plugins standard for packaging AI skills and MCP servers. Here's what it means for developers.
Profiling LLM Inference with SGLang and Torch Profiler
This blog is my attempt to learn how to profile an llm served using sglang and its in built torch profiler integration that comes out of box...
Why managed agents are the next big thing in agent building
Today we’re launching Managed Deep Agents - the easiest way to build, run and deploy production agents. We have a full release blog and docs...
所有想做 FDE 的人，都该读懂 Palantir 这套方法
大模型进入企业以后，最难的一步是把一家公司的数据、规则和权力关系，变成 AI 能够理解并安全执行的系统。2026 年 5 月，OpenAI 成立了一家专门负责企业部署的新公司。它准备投入超过 40 亿美元，并计划通过收购 Tomoro，一次带入约 150 名 Forward De...
Deep Agents vs LangChain vs LangGraph
Deep Agents, LangChain, and LangGraph each offer distinct approaches to building agents. In this post, we cover the key distinctions between...
LLM Inference: Past, Now, and Future — Where Does the Value Move?
LLM Inference — Past, Now, and Future: Where Does the Value Move?For the past two years, LLM inference has been one of the most contested la...
LLM Inference: Past, Now, and Future — Where Does the Value Move?
LLM Inference — Past, Now, and Future: Where Does the Value Move?For the past two years, LLM inference has been one of the most contested la...
Why Agents Started Dreaming (And What is Dreaming)?
A long-running agent gets really worse at remembering facts over time.As the context window fills up during the conversation, and especially...
The Agent Development Lifecycle has arrived on Cloudflare
Agents can write code faster than teams can review, deploy, and maintain it. Today we’re introducing the Agent Development Lifecycle and the Cloudflare primitives that underpin it.
前OpenAI推理负责人JT与Gemini pretrain负责人Rohan: Continue learning的未来
📝 摘要 本文是红杉资本对 Core Automation 联创 Jerry Tworek（前 OpenAI o1 reasoning 负责人）和 Rohan Anil（前 Google Gemini pretrain 负责人）的访谈。两人共同阐述了 Transformer 架构的根本局限——训练停止后无法真正持续学习——以及 Core Automation 寻找"Transformer 替代者…
最佳实践：从Multi-Agent到Agent Team
2w字长文，Agent Team 的最佳实践。
How Stripe Built Kai on Deep Agents in 1 Week
Learn how Stripe built Kai, a company-wide AI agent on LangChain, LangGraph, and Deep Agents, reaching 5,000 users in roughly 4 weeks.
Evaluating code review agents with ReviewBench
There are more code review agents now, and we’ve been building one internally. Code review is hard to evaluate. There aren’t many benchmarks...
A practical guide to building agents
A comprehensive guide to designing, orchestrating, and deploying AI agents—covering use cases, model selection, tool design, guardrails, and multi-agent patterns.
Codex 进阶指南：作为 Multi-Agent 编排控制平面
大多数人用 Codex 的方式，是开一个会话、交代一件事、等它做完。这种用法只动用了它很小一部分能力。Codex 真正提供的是一组完整的调度原语：发现有哪些项目和会话、创建新会话、从已有会话分叉、给别的会话派活、等它们回来、观察它们的进展、纠正跑偏的那个、把任务搬到另一台机器、最...
Agency: Secure, scalable sandboxes for agents
This is part of our series on how we're AI-pilling Sierra.In our last few posts, we talked about what happens when every employee has a sing...
多模型路由进入 Agent 会话之后
模型路由在单轮问答中可以按难度分配成本，但进入 Agent 多轮会话后，还要处理上下文、工具状态、缓存和隐式状态。本文拆解会话级路由的智能与工程瓶颈，以及固定强模型、隔离子任务和有状态路由三条现实路线。
The harness is all you need (mostly)
A practical GitHub Copilot workflow for prototyping, planning, implementing, and reviewing software - without chasing every new AI tool.By @...
The Orchestrator's Tax
Use subagents to protect the orchestrator's working memory, not just to save time or run tasks in parallel.
22580: From GPT2 to Kimi3, Explained
Twenty-two thousand five hundred and eighty. That’s how many GPT-2 (2019) models fit inside KimiK3 (2026). We scaled up by a factor of 22,58...
Kimi K3 Is Here: Efficient Day-0 Support on vLLM
vLLM delivers day-0 Kimi K3 serving with hybrid KDA prefix caching, DSpark speculative decoding, production-scale disaggregation, and optimized kernels across N
Practical multi-agent orchestration in Codex
GPT-5.6 Sol gets especially interesting when it has a team to work with. Codex's new Multi-Agent V2 tools give Sol and Terra a natural way t...
The new rules of context engineering for Claude 5 models
I’ve written previously about how to best prompt the newest generation of Claude 5 models and work with them iteratively to discover what yo...
大模型是专利期十个月的制药生意
这几天流传的 DeepSeek 梁文锋讲话里有一个有意思的数据点，正好和我最近的思考交汇，因此写下一些关于整个大模型产业的一些预测，供以后证伪修正思考。他说，DeepSeek 的 API 定价标准是：买一批设备回来，十个月收回成本。也就是十个月赚来的利润可以覆盖硬件上的投入。顺带...
The State of Agent Wikis
In April 2026, Andrej Karpathy published a GitHub Gist describing a pattern he called the LLM Wiki.In the months since, four different teams...
Controlling Reasoning Effort in LLMs
How LLMs Learn Low-, Medium-, and High-Effort Reasoning Modes
数据研发Multi-Agent架构的Harness工程实践
📝 摘要 本文基于阿里云数据研发团队的 Multi-Agent 实践，系统阐述了 Harness Engineering（智能体脚手架工程）的核心理念与落地方法。文章指出 AI 工程经历了 Prompt Engineering → Context Engineering → Harness Engineering 三代演进，并提出核心公式 Agent = Model + Harness：模型决定…
A Swarm of Agents for Multi-Angle Analysis: Building a Team of Experts from LLMs
Not about speed. About making several agents with different viewpoints argue over one decision and reach a conclusion better than any of the...
Career advice in the age of AI
AI models get better at anything you can write a loss function for, and school is mostly loss functions: well-defined problems graded agains...
Reshaping the economics of software: Building a future-ready core with Al/works™
Shift from AI experimentation to execution. Discover how Thoughtworks AI/works™ reshapes the economics of software delivery.
DSpark 深度解读——置信度调度投机解码与半自回归生成
DSpark：置信度调度投机解码与半自回归生成 DeepSpec 开源仓库: deepseek-ai/DeepSpec — 包含 Eagle3、DFlash、DSpark 的算法驱动训练代码与 checkpoints DSpark 是 DeepSeek-AI 提出的投机解码框架，核心创新： - 半自回归生成：并行 backbone + 轻量 sequential head，兼得并行首 token…
Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction
📝 摘要 Google Research 发布了一种将 Multi-Token Prediction (MTP) 增层改造（retrofit）到已冻结的 Gemini Nano v3 生产模型上的方法，在不修改基座模型权重的前提下，大幅加速 Pixel 设备上的端侧 LLM 推理。该方法已在 Pixel 9 和 10 系列实机部署，使 AI Notification Summaries、Proo…
vLLM + LMCache: A Starter Guide, No GPU Required
Get started easily: a single MacBook is all you need to develop vLLM + LMCacheFor New Contributors · Covering Frontend / L1 Eviction / L2 Storage / Observability If you ever skipped LMCache because you didn’t have a GPU on hand, this guide was written for you. LMCache’s multi-platform framework has already decoupled the GPU […]
How to survive AI mass replacement (& escape wage slavery)
Unfortunately, all jobs will be gone in like the next 5 seconds.At least that’s what it feels like if you take loud people on social media a...
15 AI Agent Design Patterns Every Engineer Must Know
Every team building AI agents hits the same wall.You start with one prompt and a few tools.It works. Then requirements grow. More edge cases...
简直就是教科书级别的 AI 设计规范。
Vercel 的设计系统规范，真的是我见过的教科书级样例。今天认真学习他们的 DESIGN.md，一亮一暗两套主题，彻底把颜色、字体、间距、组件、动效、文案规则，全部描述成了一套可以执行的设计方法。感觉以后我所有产品的 design.md 都可以照这个路子来写了。具体在这里：ve...
Designing distributed AI inference: Core concepts and scaling dimensions
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