#编码

用 AirDrop 传专业设备拍的超大照片和视频（动辄 20GB）一直很痛苦。 更难受的是 iPhone 的“文件”和“相册”之间几乎不互通：在“文件”里解压一个大包，想全部存进相册，没有进度，还经常闪退，存一半都不知道存到哪了。 这两天啵啵自己把它解决了。她完全没写过代码，让 Dots 操控云端电脑写了个 App，拿到她的 Mac 上，用数据线直接装到了自己手机上。没有 Apple 开发者账号，整个 App 只有 66KB。 功能很简单：把超大的 ZIP 解压之后一张一张存进相册，能看到进度，中断了还能接着存，已经存过的会自动跳过。 刚才实测 953 个文件、15.32GB，4 分钟左右全部存完。 以前遇到这种事儿只能忍或者不断自己再试试，现在不会写代码的人也能花两天给自己做个工具了，今年 vibe coding 的发展可以说是真的震撼。

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我们已统一了 Grok、Cursor 和 X Premium 的订阅方案。 隆重推出 The X Subscription。

We're launching our leaderboard for FrontierCode 1.1, a private evaluation from Cognition that tests models on completing coding tasks in a way that would actually get merged by maintainers of real-world repositories @cognition announced FrontierCode in June 2026 and updated it to version 1.1 in July. Cognition built the dataset with open source maintainers who spent more than 40 hours per task. Each task requires the agent to make a change to a real open source repository, then checks both unit tests and qualitative tests for what maintainers would require to merge the change. We run FrontierCode 1.1 Main, the primary 100-task set of the benchmark's 150 tasks, using our Stirrup open source reference harness for apples-to-apples comparisons across models. All tasks are private to avoid contamination. Each attempt is graded with both blocking and non-blocking criteria. An attempt that fails any blocking check scores zero, and one that passes them all scores between 70% and 100%. Unlike other coding evaluations, FrontierCode has a more holistic view of success based on real usage. Models need to pass unit tests, but are also assessed on behaviors like making excessive changes and large diffs, modifying files outside the relevant scope, or adding unhelpful tests. We think this approach is valuable in the current context: models are now very capable at coding tasks, but their changes can still require substantial cleanup before a maintainer would merge them. Key takeaways: ➤ Leading models still fail more than 40% of the time: Claude Opus 5.5 (max with fallback) leads at a score of 52% after passing blockers in 58% of its attempts. Nine of the 100 tasks were not passed in any attempt by any of the models we evaluated for launch ➤ The top of the leaderboard is tightly packed: nine models from six labs score between 43% and 52%. MiMo-V2.6-Pro is the leading open weights model at 45%, followed by DeepSeek V4.1 Flash (max) and Kimi K3 (max) at 44% ➤ GPT-6.1 Sol (max) offers the strongest performance for its cost: At $0.81 per task it scores 49%, matching Claude Fable 5.1 (max with fallback). This means it achieves similar performance to leading Anthropic models, at less than a tenth of the cost per task Thank you to @silasalberti, @ybenpan, @cl571128, and the rest of the team at Cognition for building FrontierCode and collaborating with us to bring it to Artificial Analysis - we’re working to add FrontierCode to our coding agent leaderboard very soon.

introducing muse wonder our new frontier model. state-of-the-art at personal agents and professional agents. our largest and most capable model yet—rivals the big bois

the slop conversation is so one sided, it's not just that you can produce more code you can also observe, debug, and refactor more code even if the code is slop you have such good tools to deal with it. even if you write code manually, it's still 100x easier to maintain it

pstack now includes 2 skills i recommend everyone use or copy: /create-verification-skill https://github.com/cursor/plugins/blob/main/pstack/skills/create-verification-skill/SKILL.md /maintain-verification-skill https://github.com/cursor/plugins/blob/main/pstack/skills/maintain-verification-skill/SKILL.md if you don't already have one, /create-verification-skill creates a skill that teaches your agent how to run, control, and debug your app. it also creates something i call the feature map: a map of all the features in your app and how to get to it and use it from a user's pov. it allows agents to navigate and use the app just like a real user which greatly increases its ability to verify its own work. but of course, this feature map goes out of date very quickly. run /maintain-verification-skill as a daily automation with cursor cloud agents. this skill will check your app for changes and keep your feature map up to date. having a strong verification skill will end up becoming critical infra for your team and become the foundation of boosting everyone's productivity and quality and also enable more interesting automations to be built. install https://cursor.com/marketplace/cursor/pstack and you'll get auto updates for my skills. enjoy!

codebases have always contained slop. we have always been okay with this because code rarely needs to be perfect, outside of mission critical "hard" (eg embedded) software. swe is a unique form of engineering because you're often building something that constantly evolves and is almost organic - user tastes change, your mental models evolve, and features come and go. admittedly, i and many others who've been coding for a long time were initially (like back in 2024) skeptical that agents would write production code. "but what about all the slop that would enter our beautiful codebase?". well, even before, we had human slop, and there was a lot of it! if we're being truthful, most people call "code i didn't write" slop. the reality is that 99% of people (i include myself in that number) don't even know what Good Code looks like, let alone be capable of writing it. i would propose that Good Code is: • correct • bug free • fast and cost efficient to run it has always been exceedingly difficult and expensive to write truly Good Code, because for the longest time (maybe @bendlang will change that), code was not something you could cheaply and formally guarantee to have all 3 of these properties. this is why writing hard software is much more difficult and expensive, because depending on the use case, you may only get one chance and so you have to be right from the start. but the vast majority of software we use does not need to be Good Code to provide value to users. in fact, it is very likely that most software we use is not formally verifiable! we accepted some bugs in exchange for velocity and progress, so we rely on tests and o11y instead and have adopted a more incremental approach. this is a good thing! agents help us move faster, and if you're open to the idea, are capable of helping us write better code. it won't be Good Code (that is still expensive), but it is likely to be much better on average at scale. the new craft of swe is to figure out how to do this. it's your job now! if your productivity with and without agents is not significantly different, you're doing something wrong. but it's not too late to embrace it and use it as an opportunity to learn a whole new skill tree

codebases have always contained slop. we have always been okay with this because code rarely needs to be perfect, outside of mission critical "hard" (eg embedded) software. swe is a unique form of engineering because you're often building something that constantly evolves and is almost organic - user tastes change, your mental models evolve, and features come and go. admittedly, i and many others who've been coding for a long time were initially (like back in 2024) skeptical that agents would write production code. "but what about all the slop that would enter our beautiful codebase?". well, even before, we had human slop, and there was a lot of it! if we're being truthful, most people call "code i didn't write" slop. the reality is that 99% of people (i include myself in that number) don't even know what Good Code looks like, let alone be capable of writing it. i would propose that Good Code is: • correct • bug free • fast and cost efficient to run it has always been exceedingly difficult and expensive to write truly Good Code, because for the longest time (maybe @bendlang will change that), code was not something you could cheaply and formally guarantee to have all 3 of these properties. this is why writing hard software is much more difficult and expensive, because depending on the use case, you may only get one chance and so you have to be right from the start. but the vast majority of software we use does not need to be Good Code to provide value to users. in fact, it is very likely that most software we use is not formally verifiable! we accepted some bugs in exchange for velocity and progress, so we rely on tests and o11y instead and have adopted a more incremental approach. this is a good thing! agents help us move faster, and if you're open to the idea, are capable of helping us write better code. it won't be Good Code (that is still expensive), but it is likely to be much better on average at scale. the new craft of swe is to figure out how to do this. it's your job now! if your productivity with and without agents is not significantly different, you're doing something wrong. but it's not too late to embrace it and use it as an opportunity to learn a whole new skill tree

Asking Claude to review your code is a slot machine. Every review finds brand new bugs. Review again: more. Review a tenth time: it still finds something. It never runs out of bugs to find... so how do you decide your code is actually done?

Now in beta: composer predictions in Codex for Pro users. Codex can now suggest your next message based on your conversation and how you talk to it. One of the most loved new features we've ever tested internally.