与「domain」相关的搜索结果

mattpocock/skills v1.2 is out! We're now the 19th most-starred repo of all time. 13.5m downloads on skills​.sh. Thanks for your support! Here's what's new: - Docs: the community's biggest ask. Every skill documented, with explanations of the main flows + troubleshooting - Claude Plugin: install via Claude's official marketplace - Codex Support: full Codex support via agents/openai.yaml files Updated Skills: - /grilling now asks you questions in rounds, not one-by-one - /prototype now uses HTML instead of a TUI for building logic prototypes - easier to share and far richer - /writing-for-agents renamed from /writing-great-skills, use it for ANYTHING your agents read (AGENTS.md, system prompts, docs) New Skills: - /wizard: tired of provisioning infra? Get your agent to build you a TUI to walk you through it - /to-questionnaire: hit a grilling question you can't answer? Turn the session into a doc you can walk through on a call with a colleague - /wait-what: no idea what the model said? Refocus it in your domain language and simplify with ASD-STE100 Full changelog + docs below. Video soon!

@ericoolen_eric @mitsuhiko 而且我一直觉得 GAV 是好文明，Golang 是带 domain name 的 FQN，所以你不会莫名其妙因为一个经典名称被一个神人抢注，而遭遇 SEO 滑铁卢。如果是纯抢注不干活还好 .. 就怕蠢人一直在干活。

Big news: Qwen3.8-Max by @Alibaba_Qwen just landed at #4# on the Frontend Code Arena leaderboard with a score of 1,668! With 1,668 points, Qwen3.8-Max is trailing only Claude Opus 5 (Max) with 1,705 pts and Kimi K3 (Max) with 1,676 pts, on par with Claude Opus 5 (High) with 1669 pts. It also ranks high across all domains: #2# in Consumer Product #3# in Brand & Marketing, Reference-based design, Gaming, and Content Creation Tools #4# in Data & Analytics #5# Simulations Dig into the thread for more details and to see how it also performs on real-world tasks in the Text Arena. Congrats to @Alibaba_Qwen on this huge release!

开源项目 LoopX：超长程 Agent 自主运行 200+ hours，状态不漂移。 我的技术主张是：LLM 上下文有限，长程 Agent 需要外置状态，通过完备的状态管理、监督和规划，让 Agent 无人干预时跑得稳、持续有产出；有人干预时跑得更好，能吸收反馈继续演进。 两条真实 trajectory 分别跨越 220.7 / 272.9 小时，跨多轮执行、等待、人工决策、writeback 与 resume 后，整个 loop 仍能找回目标、证据和下一步。 目前 LoopX 已有 3 个 showcase：auto PR issue fix、AutoML experiment 和 auto coredump fix。 以 OpenViking 开源仓库的 PR issue fix 为例，Agent 不只是循环写代码。它需要持续理解 issue 的不同状态，判断何时开发、何时等待、何时请求 review，处理 CI、冲突和上游变化，并连续交付多个 PR。 这对应 LoopX 的 domain state 管理：领域系统决定真实状态，LoopX 负责把状态投影成下一步可执行的工作。 与此同时，Agent 还可以在干活过程中实现能力自进化。当它发现现有系统缺少某项能力时，可以提出 feature、完成开发与验证、发布新的离线或在线版本，再使用新能力继续原来的任务。 长程 Agent 天然适合自进化，“完成工作”和“升级完成工作的系统”可以在同一条长程轨迹中发生。 LoopX 把这些信息外置成结构化控制面： • Goal / Vision：目标是什么，什么不能被局部优化牺牲 • Todo / Gate：当前执行的 frontier，以及必须留给人的关键判断 • Identity / Authority：谁能 claim、writeback、approve • Evidence / Receipt：每次推进留下什么可回读证据 • Quota / Scheduler：何时继续执行，何时安静等待 • Handoff / Recovery：换模型、换会话、换 host 后如何恢复 你也可以把它理解为一块专门给 Agent 设计的可执行 Kanban。 普通看板只展示“谁在做什么”；LoopX 的状态会直接约束和驱动下一次 bounded turn，让看板本身成为执行系统的一部分。 这套系统最强的地方是通用性。它不只可以修 PR，还可以做 auto research、长期实验、自媒体运营、复杂 feature 开发和办公任务。 Agent 不再只是一次性的回答机器，而可以围绕一个人的 vision，长期工作、等待、吸收反馈、积累证据并持续演进。 LoopX 从一开始 build in public：状态协议、CLI、控制面实现和真实运行轨迹都进入了开源仓库；它也已经和 OpenViking、NoKV 等 agent infra 项目形成了开源合作伙伴关系。 我希望 LoopX 最终能放大每个人的 vision 和想象力。只要你有自己的目标、想法或技术主张，就能拥有一个全天候继续工作和探索的 Agent 系统，帮助你把愿景一点点变成现实。 欢迎试用、提 issue、贡献代码，或者用一个真实的 multi-day task 跑 LoopX。

We're starting to leave the territory where you'd test an LLM by e.g. "create an svg of pelican on a bicycle". As one idea to generalize it, I was interested what Opus 5 would do if I gave it the first paragraph of the Lord of the Rings, a 1M token budget (~$10) and asked for three js render of it. Opus went off for ~2 hours and wrote 5500 lines of code that (procedurally) rendered the story. It's kind of janky but fun. But it's a bit mindboggling that the LLM has to place and orchestrate various polygon assets in (x,y,z) coordinates and write code that animates it all, and that it even does anything at all. I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom but LLMs have all the stamina and patience in the world, so it's an example where we go from "no one would ever do this" to "sure, why not, it's ~free". There might be a lot more. But I'm excited about creating hyper custom worlds that you can imagine dropping players into, e.g. here to participate in the LoTR story as a spectator NPC, or one of the characters, or etc. Something like an ephemeral GTA of X on demand. Last thought is that the domain of worlds/games exposes a weakness in LLMs: they can't easily audit their work because they aren't able to efficiently and natively perceive videos or play games within them. Here, Opus 5 had to very slowly and painstakingly take screenshots at different points, and it messed up a few times and created a bunch of jank. An example of raw capability (multimodal, gameplay) that I think is still quite lacking.

A recent YC startup spent 250k on a domain. I was slightly shocked. But when I asked how much they raised after YC, the answer was 6.5m. So 1/26 of their round. This is the time we live in; one shocking number counterbalances the other.

给关注了半年的agent team 交份作业。 这是一篇2w字长文，也是一份 Agent Team 的最佳实践。 01 一个 Agent，是怎么变成多个 Agent 的为什么真实工作会把 Task Agent 推向 Long-running Agent；为什么一个越来越好用的 Agent 最后又必须分化；多个 Agent 出现以后，瓶颈为什么会转移到 Human。 02 先让“谁负责什么”离开 Human 的脑子Agent 怎样获得稳定身份、Domain 和 Scope；Human 与 Agent 怎样查询当前 Team 的责任结构；真实协作又怎样逐渐沉淀为 Organization 与 Collaboration。 03 让 Agent 自己开始协作Agent Message 带来了什么变化；一个 Agent 怎样把工作直接交给另一个 Agent；为什么 Agent 沟通不是一次把一切说完。 04 Message 负责沟通，Topic 负责收口当工作跨越多个 Agent、多个 Turn 和多天以后，怎样保留唯一的当前版本；Responsible、Participant、Artifact 和 Needs You 分别解决什么问题。 05 Overview：让一支持续变化的 Agent Team 变得可治理当 Human 不再阅读每个 Agent 的全部过程，怎样从更高层观察 Team；怎样发现等待、积压和瓶颈候选；为什么治理关注的是工作流动，而不是让每个 Agent 看起来都很忙。 06 Agent Team 怎样进入真实的外部关系为什么把 Agent 接入 Slack、飞书还远远不够；Agent 对外以后，身份、角色、信任、权限和现实后果为什么必须被分别治理。 07 CodexLoom 在织什么Multiple Agents 与 Agent Team 的真正分水岭是什么；Human 在 Team 中的新位置是什么；为什么需要稳定的 Agent 和动态的 Team。

The future of identity is being built on-chain — and names matter. HUMANOIDE is more than a word. It is a globally recognized namespace already understood across Spanish, Portuguese, and French -speaking communities representing 700M+ people. As AI, robotics, and digital identity converge, culturally native names become valuable infrastructure. A strong Web3 namespace starts with global recognition. Enter .HUMANOIDE An extension of namespace begins with pairing it to an app! Imagine an app that becomes the gateway to the humanoid era. has a unique advantage: a memorable, language-native brand that can connect instantly across Europe, Latin America, parts of Africa, and beyond — without translation. Potential applications include: • Humanoid robot ecosystems
• Consumer AI companion apps
• Healthcare and eldercare robotics
• Education and robotics training
• Smart home and service robots As humanoid robotics moves from innovation to mainstream adoption, the platforms that define how people access and interact with these technologies will shape the next generation of AI experiences. Welcome to PITN aka Power in the Numbers. The future won’t just be about robots. It will be about the applications, identities, and ecosystems that bring humanoid technology into everyday life. #web3# #web3community# #domain# #Humanoide# #Humanoid# #AI# #Robotics# #Robotica# #ArtificialIntelligence# #app# #AppDevelopment# #FutureTech# #DigitalAssets# #Innovation# #welcome# #worldwidehumanoids#

The biggest mistake in humanoid robotics isn't engineering. It's thinking the robot is the entire business. History says otherwise. The companies that become indispensable don't always build the machine—they build the ecosystem everyone else eventually depends on. For the past six years, PITN has been preparing for a world where humanoid robotics becomes a multilingual global economy, not just a collection of impressive machines. While headlines chased the next robot demo, we quietly assembled a strategic portfolio of 500+ robotics and AI digital assets across 30 of the world's most influential languages. Not because domains are the future. Because digital identity is. Every emerging industry reaches a tipping point where attention shifts from Can we build it? to How will the world find, trust, and connect with it? That's when gateways matter. Maybe the robotics industry hasn't reached that realization yet. Just because evolution is slow doesn’t mean you need to be! Maybe it's closer than anyone thinks. Either way, we chose to build for tomorrow instead of competing for today's headlines. The future won't speak one language. Neither will humanoid robotics. Embrace the future. #HumanoidRobotics# #ArtificialIntelligence# #Robotics# #FutureTech# #DigitalAssets# #Innovation#

On July 14, the Department of the Navy released its Strategy to Weaponize Data and AI: a roadmap to an AI-first Fleet that can "out-learn and out-fight any adversary." Within a week, our team answered with working code. The Navy's new strategy frames data and AI as warfighting assets on par with weapons and munitions, and it prizes one thing above all: turning information into decisions, fast. That is exactly the problem our GURU architecture was built for. So when two Navy SBIR topics called for AI-driven maritime tracking and adaptive sensor management, we pointed at the sea what we had already proven in orbit. The video below shows both prototypes, back to back. First, GURU MarineGuard: 787 real vessels from public NOAA data, replayed through a cascade of learned models that forecast each ship's movement, flag deviations from its learned pattern of life, and hand analysts a ranked review queue instead of an unfiltered flood. The full stack runs on a laptop. Second, an adaptive sensor resource manager add-on to MarineGuard: it measures each radar's marginal contribution to each track, projects the consequence of releasing a sensor task before proposing it, and then waits for the operator. Advisory by design. Both inherit their DNA from OrbitGuard, our system watching 14,710 space objects at the SDA TAP Lab with 94 to 96 percent maneuver-detection accuracy. Same architecture, new domain, days not years. These are prototypes, and we say so on screen. The trajectories are real. The sensor numbers are deliberately notional. No score is a threat call. Showing your assumptions is a capability, not a caveat, and national-security guidance now demands exactly that: AI that is reliable, robust, steerable, and controllable under rigorous test and evaluation. Our doctrine was written for that bar. Learned models accelerate and rank. Validated references confirm and decide. Humans stay in command. One architecture. Space, maritime, autonomous engineering, regulated nuclear autonomy. This is simply the latest sign of what this team fields, fast, where mistakes are not allowed. Sailors, engineers, program folks: what mission should GURU learn next? #DefenseTech# #ArtificialIntelligence# #MaritimeDomainAwareness#