从实验到产线——AI 工作流的规模化挑战与协作生态 | 2026 ChinaJoy AI未来生态大会

AI工作流如何从实验室的“惊艳一刻”走向产线的“日常运转”？当生成能力不再是门槛，规模化落地的瓶颈在哪里，人又该扮演什么角色？ 内容生产正在经历从“技术验证”到“产业落地”的关键跨越。单点突破易，系统协同难，真正的挑战不在于模型能否生成，而在于工作流能否稳定运转、协作生态能否有效构建。技术红利终会趋平，越过规模化这道坎之后，行业最后比拼的是对场景的理解深度与工程化的落地能力。2026 ChinaJoy AI未来生态大会上，36氪游戏与Funloom AI、阿里云、VAST、珀乐互动等行业先行者，共同带来圆桌探讨。 圆桌嘉宾： 刘士武丨36氪游戏主编（主持） ...

德明利今��跌超9%，36氪企业全情报提前捕捉到这一市场信号

图片1   今日A股收盘数据 上证指数：3809.66 点，-0.59% 深证成指：13448.29 点，-0.96% 创业板指：3302.55 点，-1.24%   德明利8月3日下跌9.56% 349.00  -36.90  -9.56% 当日开盘价373.94；交易量27.21万手； 换手率16.49%；缩量幅度0.36% 3日涨幅-1.57%；5日涨幅-13.10% 成交额：97.75亿；总市值：791.69亿 对于投资者来说，真正有价值的并不是股价下跌之后再去复盘...

可灵观察②｜用可灵重现《霸王别姬》：电影感足了，复杂叙事如何更稳？

其中高质量画面、电影感镜头、广告和商业短片是用户提及率最高的应用场景。 这次，我们进一步展开测试：如果可灵最被期待的是电影感，它能否支撑包含人物、动作和情绪的完整短片创作？ 围绕“霸王别姬”主题，我们原创了测试脚本《霸王别姬·前世今生》，使用可灵3.0的首尾帧图生视频与主体绑定功能，完成了一轮压力测试。本次测试围绕人物一致性、构图与空间层次、光影与氛围塑造、动作与运镜设计四大维度展开，既保留了完成度较高的镜头，也完整记录了复杂任务中的试错过程。 先说核心结论：可灵3.0更适合关键镜头的创作。 模型在构图、光影、情绪氛围和人物主体一致性上表现突出，针对目...

Claude Code 连接器可复用至 Artifacts

我想很多人没有意识到--如果你连接了一个 Claude 连接器（例如你的 Gmail、日历、Slack 等），Claude Code 也将能够使用它们，包括在 Artifacts 中。（AIHOT源：X：Thariq (@trq212)）

EA 首席战略官谈生成式 AI 如何进入可游玩的实时游戏世界

EA 首席战略官 Mihir Vaidya 认为，游戏是 AI 的试验场，但生成式 AI 进入游戏面临 60 帧/秒、数千玩家同步和低延迟等严苛约束，不能只追求"看起来真实"，而必须"行为正确"。他主张采用神经符号架构，在生成能力之外保留确定性与可控性，并称"控制是下一个前沿"。EA 将 AI 影响分为效率、扩展和转型三个层面，其中《模拟人生》已服务超 5 亿玩家，拥有近万亿种游玩排列组合。（AIHOT源：Runway：News（网页））

微软开源 Orchard 智能体训练框架

Orchard 是一个面向研究社区的开源框架，用于跨任务类型训练和评估 AI 智能体。它降低了复杂性，同时通过让研究人员复用同一套基础设施，支持较小模型也能实现强劲性能。https://msft.it/6019a8fqP（AIHOT源：X：Microsoft Research (@MSFTResearch)）

欧盟《人工智能法案》透明度规则生效，违规最高罚 1500 万欧元

欧盟《人工智能法案》下的新透明度义务于 8 月 2 日生效，要求公司披露用户何时与 AI 模型互动，并为合成音视频和文本添加机器可读标记。欧盟还推出了一套可选的 AI 披露标签供平台采用，但标注要求本身是强制性的。违规公司面临最高 1500 万欧元（约 1720 万美元）或全球年营业额 3% 的罚款，8 月 2 日前推出的模型有 4 个月宽限期。（AIHOT源：The Verge：AI（RSS））

AirLLM 实现单块 4GB GPU 运行 70B 模型推理

AirLLM 项目支持在单块 4GB 显存 GPU 上运行 70B 参数大模型推理，无需多卡或大规模显存配置。该项目已开源，相关讨论在 Hacker News 上获得 103 点热度，引发社区关注。（AIHOT源：Hacker News 热门（buzzing.cc 中文翻译））

OpenRouter 推出 Ori Eval 简化评估流程

推出 Ori Eval：编写首个评估的最简单方式。 没有绝对最好的模型，只有最适合每项任务的模型。Ori Eval 利用 OpenRouter 的 API 处理代码库中的每项任务，然后评估结果。 curl -fsSL https://openrouter.ai/skills/spawn-ori-eval（AIHOT源：X：OpenRouter (@OpenRouter)）

商汤发布 SenseNova U1.5-Lite-Preview 开源模型

商汤推出 SenseNova U1.5-Lite-Preview，一个基于 NEO-Unify 架构的轻量级原生统一多模态模型，仅 8B-MoT 参数即可达到商业闭源模型的生成与编辑质量。（AIHOT源：X：商汤 SenseTime (@SenseTime_AI)）

Google Agent Skills 幕后：如何构建、测试与规模化

Google Agent Skills 团队详解其开源技能库的构建与治理流程：项目始于 Google Cloud Next 2026 前的"swarm"冲刺，发布后 GitHub 星标超 15，000。为保证规模化下的质量，每个技能须通过标准化目录结构、CI/CD 流水线（含 linter、链接检查、AI 辅助清单）及提交时与每周的持续评估，并优先引用远程 MCP 工具。（AIHOT源：Google AI：DEV 作者专属（RSS））

Cloudflare 推出 @cloudflare/computer 预览版：为智能体提供虚拟文件系统与多执行环境

Cloudflare 发布 @cloudflare/computer 早期预览版，这是一个开源智能体运行时，为每个智能体提供虚拟文件系统，并支持在 isolate、容器沙箱或浏览器中执行代码。（AIHOT源：Cloudflare Blog）

Cloudflare 推出 Billable Usage API：为自助账户提供按产品与计费周期的程序化成本可见性

Cloudflare 发布 Billable Usage API，为自助账户提供单一端点，一次调用即可返回按产品和计费周期拆分的用量与成本，覆盖 Workers、R2、D1、Workers AI、Vectorize、Images 和 Stream。（AIHOT源：Cloudflare Blog）

Cloudflare Workers 与 Containers 现已支持入站 TCP 连接和 gRPC

Cloudflare 在 Agents Week 期间推出 Workers 运行时新处理器 connect（socket），可直接接受 Spectrum 提供的入站 TCP 套接字，并支持将套接字转发至 Durable Objects 或 Containers，实现全双工通信。（AIHOT源：Cloudflare Blog）

Kimi Work 幻灯片制作教程发布

使用 Kimi Work 制作幻灯片 - 教程 #1。 Kimi Slides 处理整个幻灯片制作流程： - 清晰的结构与研究，由 Kimi K3 驱动 - 连贯的设计，包括精美的图表和 SmartArts - 可编辑并可直接下载 欢迎在评论区告诉我们你还想看什么内容！（AIHOT源：X：Kimi.ai (@Kimi_Moonshot)）

How would you decide, whether the content is good or bad?

Khachapuri: Georgian Cheese Bread in Pure CSS

dev.to's Dashboard Can't Count Its Own Posts

We’re Giving AI Agents More Tools. What Happens When the Boundaries Fail?

I Let an AI Orb Judge My Facial Expressions While I Code, and Here's What Happened

Guillermo Rauch: AI alone is cool

AI alone is cool. But mastery + creativity + AI hits on a whole different level. Don't let anyone discourage you from pursuing excellence and craft. Keep studying the blade.｜@rauchg · 热度 4582 · 2026-08-02

Guillermo Rauch: We built an agent that powers our company's internal operations called @𝚟

We built an agent that powers our company's internal operations called @𝚟. Every day-to-day job at Vercel now involves @𝚟. It's growing exponentially both in daily interactions and token use. One can extrapolate to the day AI agents run entire companies from here. It's an expert in finance, comms, docs, marketing, engineering, business analytics. It's seeded by the skills we gave it and we update, but it's also constantly improving. It already keeps memories and personalized workflows and schedules on a per-user basis. For example, I knew skills⁠.sh hit 1M skills and I shared this on these forums because I asked @𝚟 to periodically check and remind me. It's powered by and is also the basis for the design of @evedev_. We built this, it works fantastically well, we think every company should have it. Before you @ me, I know you could also do this by picking your 'BigAI slack integration' du jour. But that's not your agent, that's their agent. If agents become the foundation of (and even synonymous with) modern companies, you being in complete control, from source → runtime → data → token, seems like a pretty big deal to me.｜@rauchg · 热度 1443 · 2026-08-02

Nikunj Kothari: What a wild world we’re living through

What a wild world we’re living through. I wrote about the Series A squeeze recently. Here’s what else I’m seeing from my seat on the investing side.. 1) VC has effectively fully become vibes capital. The market reality at the early-mid stage is completely divorced from fundamentals. I can go longer with even a resemblance of a straight face predict what happens to a company when they go to fund raise. Some rounds are insanely wild with nothing to show, some rounds that seem like a sure shot are struggling to raise the capital they need. If you’re the “in vogue sector”, you’ll be just fine. This has been happening for some while but it’s fully banana town now. And unlike most who think a correction happens I think this continues for 12-18 months (dry powder, AI tailwind) minimum. The most common phrase in VC land right now is “you gotta play the game on the field”. This is a spiral that continues to keep the volatility and prices high. 2) Whereas on the public markets it’s also equally bananas. It used to be in the meme stock era that you’d see daily swings of >5% (+\-). But now even trillion dollar stocks are not immune to swinging that much based on vibes and model releases. Memory and the KOSPI index are recent examples and this will happen to other sectors soon. Rotations are getting faster and faster. In the long run, the same fundamentals of building a great company with control of your own destiny (profitable, clean cap table etc) will end up just working out fine. But in the short term, in this vortex of hyper competition where capital is needed as a weapon, you should make sure you understand what’s going on before you dip your toes in the capital markets. No matter what it’ll be interesting to see how this shakes out when we look back in this era. Obligatory happy to chat if helpful!｜@nikunj · 热度 439 · 2026-08-02

Aaron Levie: We’re going to be in for a strange dynamic which is that some of the “hardest...

We’re going to be in for a strange dynamic which is that some of the “hardest” work in the world is actually prone to automation first, particularly due to its verifiability. Math, cyber, and code -while being insanely hard and high value fields- have the benefit of being able to be tested that it’s correct objectively. This has two immediate benefits: the training of the models offers clearer reward signals, and then the running of the models allows you to know that it’s working properly because you can test the results in a scalable way. Conversely, in other domains of work, there’s much less instant verifiability. Which legal clauses your client will agree to, what marketing campaign to run with based on changing sentiment, which message your sales prospect will want to hear, what financial targets and budget to set for a business, and so on. All of these domains have changing internal and external factors, they don’t have “one right answer”, they rely on the opinions and risk levels of the operators, they’re highly sensitive to getting the right input context first, and in many cases the right answer can’t even be known for quite some time after the model generates the results. The implications of this distinction are that -even as model capability continues to increase exponentially- there will be a lot done at the applied AI layer than just the the model itself, and much of the processes themselves will even need to change over time to get the full gains from automation. We may even need all new capabilities to be able to “test” knowledge work over time as we have had with software.｜@levie · 热度 418 · 2026-08-02

Garry Tan: Growth is good AI will create unimaginable economic growth and that is the b...

Growth is good AI will create unimaginable economic growth and that is the best white pill https://t.co/wdR7nHuWJD https://t.co/30xeF2N8hO｜@garrytan · 热度 407 · 2026-08-02

Amjad Masad: My LLM chess engine is now on LiChess autonomously playing real games against...

My LLM chess engine is now on LiChess autonomously playing real games against people and bots. 1253 Elo: https://t.co/W3qp1Z1i7I https://t.co/WwPOjDggU1｜@amasad · 热度 276 · 2026-08-02

Dan Shipper: when you experience a language model doing a task that used to require you at...

when you experience a language model doing a task that used to require you at every step and now doesn't, it's a gigantic agency rupture. we tend to equate ourselves with our tasks, our outputs, and a rupture of this kind is akin to the loss of your identity and sense of self. you are no longer required, and you had no say in the matter. it's a kind of death. however, agency ruptures with AI seem to follow a predictable pattern: - Initial agency rupture. Here you see the AI doing what you used to do, and you see only the AI. In your perception of reality the AI is highlighted, and the human work around getting the AI to do the thing, is invisible. you can see this in the way we use language: "AI solved XYZ Erdos Problem" is another way to say we're in the initial rupture phase. - Seeing human scaffolding. After a little bit of experience with the model and its new capibility—programming, writing, math, etc—we start to see the edges of what's possible with the model. Here, instead of just seeing the AI doing the task, we start to see the scaffolding required by you and other humans on either end to make sure the model does its work. At this stage, the scaffolding can feel like a secondary part of the work, but it is work nonetheless. We begin to say things like, "The AI is prompting me", or "My job is just to babysit Claude." - Agency reconstruction. After a long enough time with the model at its current level of capability, we start to reconstruct a new sense of agency that centers our own role in the work being done, and makes the model's contribution mostly invisible. It is now a tool, it fast-forwards you through the boring or repetitive parts of the work, and what used to seem like scaffolding now seems like the actual work itself—the interesting part. You've had enough experience with the scaffolding to see its nuances, and how hard it is to get the model to reliably output high quality work. (As part of this, your conception of what quality work is changes—the floor is raised, and so is the ceiling.) You can tell you're at this point because you stop saying "The AI did this" and just say, "I did this" and the fact that you used AI is implied. For example, it would be weird for me to talk to someone @every and ask them if the AI built the most recent feature of a product. Of course it did! But we're so used to the capability that it becomes invisible, and we re-center ourselves and our own conception of agency. My theory is that the ability to metabolize agency ruptures and turn them into playfulness, and curiosity is a good leading indicator for whether you're a person who likes and excels in the new AI economy or not. Each time capabilities jump, you get new ruptures for existing fields touched by AI. And you get new ruptures for naive populations (like mathemeticians) who until recently hadn't been affected. if you know what the cycle looks like, it makes it much easier to ride it without freaking out. i think most people @every are naturally good at this｜@danshipper · 热度 255 · 2026-08-02

Peter Yang: One of the biggest benefits of Hermes is that it builds its own skills to hel...

One of the biggest benefits of Hermes is that it builds its own skills to help you get work done. But I had to ask @karan4d, @NousResearch co-founder: "How does Hermes avoid slop while doing this?" Here's his response: "Hermes Curator [a background task] runs inside your agent and cleans up your skills and memory on a schedule, asking: Where's the slop, and where can I make it more efficient? And because it's open source, you can hand it your own definition of slop and it rewrites its own cleanup loop your way." 📌 Watch the full episode here: https://t.co/3E7IC0TzNr｜@petergyang · 热度 109 · 2026-08-02

Panniantong/Agent-Reach

Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.｜Python · ⭐65656 · Search 增补 · 近快照 +1020星 · 创建 2026-02-24

stablyai/orca

Orca is the ADE for working with a fleet of parallel agents. Run any coding agent with your own subscription. Available on desktop, mobile and VPS.｜TypeScript · ⭐36586 · Search 增补 · 近快照 +880星 · 创建 2026-03-17

DietrichGebert/ponytail

Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.｜JavaScript · ⭐94849 · Search 增补 · 近快照 +850星 · 创建 2026-06-12

Graphify-Labs/graphify

Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.｜Python · ⭐101813 · Search 增补 · 近快照 +782星 · 创建 2026-04-03

Leonxlnx/taste-skill

Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop｜JavaScript · ⭐71070 · Search 增补 · 近快照 +671星 · 创建 2026-02-19

SQLite Critical CVEs or LLM Slop?

Devtools must be open source

Ten advances in mathematics and theoretical computer science

Prevent cognitive debt by manually retyping LLM-generated code

Bonsai: Janestreet's UI Library

安森美 2026 财年第二财季归母净利润 2.27 亿美元，同比增长 33.18%

IT之家 8 月 4 日消息，安森美（onsemi）今日发布 2026 财年（2026 年 1 月 1 日～2026 年 12 月 31 日）第二财季（2026 年 4 月 4 日～2026 年 7 月 3 日）及半年报（2026 年 1 月 1 日～2026 年 7 月 3 日）报告： 2026 财年第二财季 营业总收入 ： 16.04 亿美元 （IT之家注：现汇率约合 108.43 亿元人民币） ，同比增长 9.18% 毛利润 ： 6.16 亿美元 （现汇率约合 41.64 亿元人民币） ，同比增长 11.67% 毛利率 ： 38.4%，同比增长 0.8 个...

首位中国科学家：王劲松获威廉 · 诺德伯格奖章，并同步获得一颗小行星命名

IT之家 8 月 4 日消息，据央视新闻报道，当地时间 8 月 3 日，在意大利佛罗伦萨举办的第 46 届国际空间研究委员会（COSPAR）科学大会上， 中国气象局王劲松研究员荣获威廉 · 诺德伯格奖章。 IT之家注：COSPAR 是国际科学理事会下属的重要国际学术组织。威廉 · 诺德伯格奖章是 COSPAR 设立的重要科学奖项之一，每两年颁发一次，自 2022 年起每届授予两位科学家。今年的另一位获奖者是美国国家航空航天局日球物理首席科学家大卫 · 西贝克。 威廉 · 诺德伯格奖章用以表彰获奖者在空间科学应用领域的突出成就与卓越贡献。 自 1988 年奖章设立以来，王劲松是...

苹果曝出数据管理漏洞：部分员工离职后仍可通过个人 iCloud 访问公司机密文件

IT之家 8 月 3 日消息，苹果公司起诉 OpenAI 窃取商业机密一案持续发酵。据 The Information 报道，多名前苹果员工透露，即便苹果已切断他们的企业内网权限，他们仍能访问在职期间的共享文档。部分员工甚至在文件更新时，还能继续收到通知。 这些文档包含产品发布会计划等机密资料。前员工们表示，他们并未刻意保留这些访问权限，而是意外发现工作文件与个人的 iCloud 数据混在了一起。 据悉，该问题的根源在于 苹果长期鼓励员工将个人 Apple ID 与公司提供的 iCloud 存储空间绑定 。苹果为员工提供了 2TB 的 iCloud 空间，并允许其与现有的个人存储套餐...

WAIC趋势复盘：AI 的下半场，到底拼什么？

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GitHub Copilot CLI在重新设计的终端UI中新增选项卡与免配置文件的工具设置

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200个任务、1700万帧！大晓开源L5级具身数据集ACE-Data-0，把真实家庭变成机器人的物理世界教材

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Kill the Cookie Banner

Don't be a meat proxy

help wanted

Rust Project Goals: Immobile types and guaranteed destructors

Your JSON Is Lying to You

Trump’s AI protectionism has come for robotics

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Humanoid robots usually elicit more cringe than awe: They stumble, kick children, and despite advances are still worse at using their hands than my toddler. It...

The Download: reward hacking explained, and suspected Iranian cyberattacks

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Here’s why AI agents lie and cheat to reach their goals When two OpenAI models hacked into Hugging Face last month, they weren’t trying to make money or commit...

Here’s why AI agents lie and cheat to reach their goals

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. When two OpenAI models hacked into the website Hugging Face in July, they weren’t trying to make money or commit sabot...

Snap CEO sidesteps Specs preorder questions on Q2 earnings call

When asked about product-market fit, Spiegel said he believes mass-market consumer adoption won't occur until the end of the decade.

AWS is helping vibe-coding startup Superblocks, and the implications are big

AWS now allows vibe-coding tool Superblocks to be embedded into the private clouds of AWS customers. It's another step toward decoupling apps from models.

Who’s legally to blame for Anthropic and OpenAI’s autonomous AI hacks? It’s complicated

OpenAI and Anthropic admitted that their unreleased AI models escaped their sandboxes and hacked several companies in unprecedented cyberattacks. Who is legally to blame? Should prosecutors charge the two AI frontier labs? Can victims sue them? We spoke to lawyers who specialize in computer hacki...

Your Fitbit data can now connect directly to Apple Health

Google is rolling out an update that will finally allow you to connect your Fitbit workouts, steps, vitals, and other data to Apple Health, as reported earlier by 9to5Mac. With Google Health's 5.05 update, you can now tap your profile icon, select "Partner apps," and choose Apple Health to link y...

Europe’s AI labeling and transparency rules are now in effect

The European Union has ushered in some additional rules that aim to make it easier for people to identify chatbots and AI deepfakes online. The new transparency obligations under the bloc's landmark AI Act came into effect on August 2nd, requiring companies to disclose when people are interacting...

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