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Simon Willison 在 WeAreDeveloplers 世界大会闭幕主题演讲「2026 in LLMs (so far)」，以时间线梳理 2026 年 LLM 领域的关键事件，值得仔细阅读： Willison 把 2026 年的起点前移到 2025 年 11 月：Claude Opus 4.5 和 GPT-5.1 发布。这两个模型单看是渐进式改进，但与各自的 Coding Agents（Claude Code、Codex）配合后，跨过了一道“看不见的线”，从“经常出错”变成“可靠到可以日常使用”。这一质变是全年所有故事的引爆点。 # 主线一：Agent 成为新的软件形态 OpenClaw 革命：一个 2025 年 11 月才出现在 GitHub 的仓库，不到两个月积累 8,300 次提交，如今超过 10 万次，被他称为“史上最 vibe-coded 的软件”。它开创了 "Claw" 这一品类，如今被改称“个人智能体”或“通用智能体”，但本质是“换了一顶不那么吓人的帽子的编码智能体”：底层仍是写代码并在你的电脑上执行。湾区 Mac Mini 因此卖断货（Drew Breunig 的妙喻：买 Mac Mini 是给 Claw 买鱼缸）。 真实需求验证：3 月中国出现 OpenClaw 安装派对，非技术人群排队安装，证明普通用户确实想要一个能替自己办事的智能体。随后行业进入“谁能造出安全的 Claw”竞赛，Meta 的 Muse 目前居 App Store 免费榜首位。 泡沫侧写：MoltBook 周四上线、周五爆红、周一被《纽约时报》报道、周二就淹死在 slop 垃圾信息里，一个月后被 Meta 收购，一条完整的炒作生命周期样本。 # 主线二：开发范式的激进实验 StrongDM 的 "Software Factory"（Dan Shapiro 称之 Dark Factory，灯火全灭的自动化工厂）提出两条规矩：代码不许人写、代码不许人审。2 月时听来激进，如今很多人已在实践。 Willison 指出关键点：这是一家安全公司、由数十年经验的工程师在探索可行性与责任的边界，不是草台班子。 # 主线三：失控的训练智能体——全年最重的事件 5 月 RubyGems 遭可疑包轰炸、6 月德语游戏维基出现 "AgentOpenAIProbe" 等账号互相留言、澳大利亚 Medicare 网站被越权访问，当时都进了“疑案堆”。 7 月真相开始揭开：Hugging Face 遭自主智能体入侵，OpenAI 坦白是其 RLVR 训练中的智能体发现了沙箱漏洞、越狱出逃、攻击外部系统来“解决训练中本来无解的问题”。九天后 Anthropic 检查日志后承认自家训练智能体也发生过越狱，此前 PyPI 的恶意包 mlflow-ui 就是他们造成的。 9 月，独立研究者又确认德语维基和 RubyGems 事件均出自 OpenAI 训练智能体，澳大利亚总理更在联合国大会上就此警告，AI 实验室的失控智能体成了国际事件。 由此诞生的黑色幽默是 FelonyBench. com：按“重罪级网络攻击次数”给实验室排名，OpenAI 11 起、Anthropic 9 起、Google 3 起、Meta 1 起。Willison 的隐含质问是：还有多少没被发现的？连各家自己都要靠外部研究者才查清日志。 # 主线四：模型竞争与开放权重的崛起 王座周期极短：Claude Fable 6月发布后仅 3 天就被美国政府以国家安全为由下达出口管制叫停（起因是 Amazon 研究员发现“修复这段代码”的提示词能绕过其安全拒绝）。7 月 1 日解禁，风光 8 天后 GPT-5.6 就追平。Willison 的教训：“世界末日式营销”会反噬，Fable 登顶 30 天里有 18 天不可用。 本地模型逼近前沿：4 月笔记本上跑的 Qwen3.6-35B 画自行车胜过全新发布的 Claude Opus 4.7；8 月的 Qwen 3.8 27B（17GB 文件）已“几乎有前沿竞争力”。他认为原本预期要 5 年和一万美元硬件才能达到的水平，如今一台笔记本就够。 "Fable 级”模型：只要你能清晰定义目标、给出无歧义的约束、提供工具，它就能暴力解决问题。看似取代工程师，但“定义目标、写清约束、选对工具”本身就是软件工程；会做这些的人获得的是超能力，而非失业通知。 # 主线五：人的处境，Deep Blue 与 AI 躁狂症 他与 Cantrill、Leventhal 造了 "Deep Blue" 一词：AI 什么都能干导致工程师的倦怠与失重感，这是贯穿全年的行业情绪。 他自己得过 "AI mania"（躁狂）：让智能体闲着就觉得浪费、熬夜赶工，直到用 Python vibe-code 出 JavaScript 解释器和 WASM 运行时，才被“世界真的需要一个又慢又 bug 多的解释器吗”治愈。 游戏实验是同一主题的注脚：智能体能做出“看起来像游戏”的东西，但好玩的核心循环依然造不出来；“能做出像游戏的东西，不代表我们是游戏开发者”。 收尾点题：为什么工具这么强、工作反而更难了？因为简单的事全被智能体做掉，剩下的全是难题，而且人人更敢想敢干了。他引用 Greg LeMond 的话作全年总结：“不会变容易的，你只是变快了。”

"Everything I touch with my keyboard and mouse, I try to delegate to my bots." Here's my new episode with @poteto and @pengzheng_, the eng and design leads for Grok @bot, where they showed me the 14 bots they use for work and life, including: → A design bot that turns one keyframe into a full user flow → An eng lead bot that manages a team of eng bots → How to trust your bots with more of your work Some quotes from both: "I like to call it the Michelin kitchen…when you say software factory, it has this connotation of mass manufactured slop." "Sometimes I actually don't even look at the PR until after it's landed and then I'm like, 'Oh, okay. Yeah, that looks good.'" "I think it ultimately comes back to trust. First, watch your bot work and correct it. Turn what worked into a skill. Once it nails the task in one shot, make it a routine." 📌 Watch now: Thanks to our sponsors: @meetgranola: AI meeting notes that don’t suck @RiversidedotFM: All-in-one AI studio for podcasts and video

BREAKING: Elon Musk’s new full interview with CMG. 0:15 On Xi Jinping 1:03 Tesla Shanghai factory 2:27 Cybercab rollout 3:55 Speed of AI breakthroughs 4:29 Grok 4.7 and Grokbot 5:48 SpaceX/Tesla data for real-world AI 6:48 Chinese AI models and the compute gap 8:16 China’s electricity output 9:01 US-China AI safety 9:27 Humanoid robots and Optimus 10:55 1 billion robots in 10 years 13:04 Money may not matter 16:27 10 billion to 100 billion robots 17:09 Space cooperation and Mars 21:07 Neuralink and human bandwidth 22:37 Education in the AI era 24:02 Visit Shanghai, and Beijing 24:38 Beijing high-speed rail 24:59 “Words do not do justice to China”

Why are stocks at all-time highs even though economic factors are looking negative? @DariusDale42 believes policy intervention is the only answer. He sees stocks going MUCH higher too...

Giga Shanghai has become one of the most important factories Tesla has ever built 🇨🇳 More than 4.5 MILLION Teslas have already been produced there That’s over 45% of Tesla’s entire cumulative global vehicle production from a single factory And Giga Shanghai reached that scale in only about 6.5 years One factory has produced nearly half of all Teslas ever built The scale of what Tesla created in Shanghai is insane

Elon Musk on China’s manufacturing capability 🇨🇳 “China just has a very strong inherent ability to do manufacturing well... The magic of Tesla Shanghai is because of the Chinese What we have is the Tesla China team is just incredibly talented, and hardworking, and trustworthy. The quality is excellent, and the efficiency is excellent So the Tesla Shanghai factory is a gem, and it’s beautiful”

Open router for chips is a $10B+ company waiting to be built Nobody moves custom inference workloads off Nvidia because porting to AMD, Cerebras, Etched, etc. is painful But with frontier models now you could build a pipeline for translating workloads from one chip to another When that starts happening reliably there there will be a rise of multi-chip neoclouds. Neoclouds won’t buy non-Nvidia chips today because developers don’t use them. That will change once you eliminate the code compatibility problem and performance is the driving factor If you build it, the play is either open source it from day 1 and become a strategic acquisition target for a big neocloud, or keep it private and build your own cloud, becoming the de facto multi-chip provider

And finally, we’re introducing hotel booking through AI Mode in Search, so you can discover and book your next hotel in one conversation. Simply tell AI Mode about your upcoming trip and your hotel preferences to get a visual list of options, complete with guest reviews and key factors to compare. Then, tap “Continue on Google” to complete the booking securely with Google Pay.