April

后端程序员｜AI Builder｜用 AI 造工具，只发自己跑过的东西

[April @April1qing]

Codex这次更新，让我还上欠下的「知识债」。

我用 Space / Pages 存下工作中没弄懂的问题、原对话和学习提示词。有空点一下，就能接着学。

AI 帮我把事情做完，我也想把里面的知识弄懂。

#Codex #ChatGPT

[向阳乔木 @vista8]

写好文章，发布公众号、X、小红书，操作非常繁琐？

推荐新开发的 Obsidian 插件 Qiaomu Publish

请看功能演示，自媒体必备，开源地址见评论区

[April @April1qing]

TypeScript 编译器原本用 TS 写，后来微软用 Go 重写，现在 Vercel Labs 又把它移植回 TypeScript，编译成原生程序，说比 Go 版快 2 到 4 倍。他们拿 TS 的一万两千多个测试文件当答案，每一段都和 Go 版逐项对比。#TypeScript #AI编程
https://t.co/Ot9BXpNc8n

[Chris Tate @ctatedev]

New from Vercel Labs: tsc-ts

TypeScript was written in TypeScript
Then rewritten in Go

We ported it back to TypeScript
Compiled it with scriptc

And it's 2-4x faster than tsgo

[April @April1qing]

做 Amp 的 Thorsten Ball 问了个挺冲的问题：有了 agent，为什么还要 PR？这周 Anthropic 那篇评审文章走的是另一条路，PR 还在，只是交给分类器按风险决定要几个人批准。两种做法摆在一起看，挺有意思。#AI编程 #CodeReview
https://t.co/cHuNg61qAV

[Thorsten Ball @thorstenball]

Why are you still using pull requests?

Is it for code review?

If so, do you really think your or your colleagues catch bugs and mistakes that an Opus 5.5 or Fable or Astra won't find? You can literally spawn all three of them and have them check four times each.

(Also, don't tell me you don't have a "stamp plz" Slack channel or group chat somewhere.)

Is it for knowledge sharing?

Surely there's better ways for that now than the PR, no? We have models that can generate explainer videos, presentations, podcasts, games, quizzes, walkthroughs, ... for any piece of code it can reach.

Is CI the reason?

Well ackshually, CI means continuous integration and its original meaning is the *opposite* of having long-lived branches and PRs.

But, leaving that aside, your agents are hopefully already running the tests before they push something, and hopefully they can run as long as they want and don't block you so they can run the whole test suite and generate "irrefutable proof" and end-to-end demo videos and so on... And so they run the tests and confirm and then you push to a branch and everything runs again and then you merge and then it runs again? That doesn't make sense, does it?

Maybe you work in open source and you accept contributions, in which case I can't speak to the value of pull requests. It's been 2 years since my last OSS contribution. But from what I heard, the time of the PR is coming to an end in OSS too?

I honestly don't get why people still stick to PRs.

With agents you can now move 10x faster, you can finally do real continuous integration, and get more confidence in the changes the agent made.

Don't slow yourself down by having all the changes pile up in a PR.

[April @April1qing]

Politico 报道，Anthropic 私下给国会递了一份前沿 AI 安全法案草案。按草案，政府要叫停一个模型，得先证明它马上可能造成 10 亿美元以上损失或 50 人以上伤亡，还要法院批准。安全圈不太买账，Kelsey Piper 觉得 Anthropic 更怕的是被政府随手叫停。#Anthropic #AI监管
https://t.co/grCet7lkfn

[Kelsey Piper @KelseyTuoc]

One unfortunate conclusion I'm reaching from this is that Anthropic, while acknowledging they're doing something extremely dangerous, is more importantly wrt policy terrified of the executive branch having the ability to flexibly make them stop at the drop of a hat.

This isn't shocking given that they've been on the receiving end of the executive branch doing that in an unprincipled and stupid way! Anyone who has to deal with a bureaucracy becomes a libertarian, and the administration's treatment of Anthropic was genuinely outrageous.

But it means that they're interested in giving themselves more process-based protections when everyone else is interested in giving us more process-based protections against them. The AI safety movement wants the ability to shut down superintelligent model development. Anthropic wants to significantly curtail current authorities to do that so that it has to be narrowly tailored to a specific imminent threat in only a few possible categories. The AI safety movement wants the states to be able to pass stronger laws than it can get through federally. Anthropic wants preemption.

You also kind of have to wonder if the Global Affairs people and the "we are terrified that we're about to kick off RSI" people at Anthropic talk to each other.

Global affairs: So we've been working very diligently to make sure the Trump administration can't just blacklist Fable 6.5 since we didn't donate as much money to his ballroom as Greg Brockman did.

Technical staff: We think Fable 6.5 might be able to copy itself into an unmonitored data center and start doing experiments we can't observe.

Global affairs: Sounds crazy, hope you're wrong! Anyway, we're lobbying for no further state laws around safety, since it's really expensive to fight ill-conceived laws in 50 state legislatures at once.

Technical staff: Money may or may not still have any meaning in 36 months.

Global affairs (giggling nervously): It's that San Francisco air, man. Everyone gets like that if they move there. Anyway, obviously if the government makes a narrowly tailored finding that an AI poses an imminent risk of causing more than 50 deaths-

Technical staff: We give it a 10 percent chance it'll cause 8 billion.

[April @April1qing]

@easybakeowein welcome to Shanghai! if jet lag lets you, walk the Bund at night then cross to Yu Garden early the next morning before the tour groups. and get Alipay set up with your card, it makes everything easier. enjoy the trip!

[April @April1qing]

@realjustinkim for cafes like that, wander the old lanes around Wukang Rd and Anfu Rd in the former French Concession. tons of tiny spots in old houses. Columbia Circle is nice too. go on a weekday morning, weekends get packed. enjoy the trip!

[April @April1qing]

@clarkeskru 19h is plenty! Get the transit visa-free stamp at Pudong, then Maglev + metro into town (~1h). Yu Garden early before crowds, xiaolongbao for lunch, the Bund at dusk when the lights come on. Leave 3h+ to get back. Happy to send my full layover route if you want it

[Tencent AI @TencentAI_News]

A 3B active model beats Opus 5 at memory. We open-sourced the full stack — model, engine, and app.

Hy Memory handles chat, work, and code memory in one layer, and it's first on all four benchmarks we ran, leading MemoryArena by 10.2 points while cutting memory volume 51.8% and recall tokens 39.3%.

The model isn't tied to our engine. Drop Hy-Memory-30B-A3B into Mem0, MemOS, or EverMemOS and it still comes out on top, even on the one system it never trained on. It's already running in our own products, where one assistant's multi-hop recall went from 50% to 90%. It also unifies memory across Codex, Cursor, WorkBuddy, and more.

Repo: https://t.co/M7PkOJVyE5
Weights: https://t.co/U04mNgmVuS

Questions welcome in the replies.

[Caine Builds 🐧 @CaineArdayfio]

Mobile Use + Jev for audio = ULTRAFAST⚡️

10x faster and 20x cheaper than Gemini Live.

It's our Audio Action model, Thud.

Thud listens and then does. It has a pre-built list of actions it can take on every page and listens for that action.

It's 2026. We don't need to transcribe every voice command before spoon-feeding it to the AI. The AI is smart enough to just hear audio and do! It doesn't need text.

[铜匠AI・十点睡觉 @CopperForgeAI]

OpenAI 官方博客披露了核心团队用 GPT-6 系列维护代码库的一手工程实践。直接公布了几个实际运行的提示词，
靠这些提示词在两周内合并了 1400 多个清理 PR，删除超过 800k 行代码，并将一个服务的容器镜像压缩48%。

第一个提示词用于清理死代码

“请帮我找出并清理仓库里的无用代码。你可以随意查看
Slack、Statsig（功能开关平台）或任何需要的来源，用来确认这些代码确实不再被使用、可以被清理或简化。”
这个提示词的关键在于允许 GPT-6 Astra 自主去翻阅 Slack 讨论、Statsig 实验配置甚至生产环境的性能 profiling数据，只有在确认生产环境零调用后才在 PR 中列出删除依据。

第二个提示词用于清理更棘手的废弃实验与功能开关

“/goal 找出整个仓库中所有陈旧、常量化、已废弃或全量上线的 Statsig开关、配置和实验，在不改变生产行为的前提下安全移除。审计所有项目资产，不限于标记为陈旧的对象。��过枚举、常量、包装函数、别名和动态拼
接名称寻找间接引用。核对生产引用的常量，不只给保守清单。结合生产遥测与性能分析评估 CPU 开销，找出昂贵的无用判断。生成 Markdown报告，按归属和文件工作流分组以便并行处理，附带生产取值、证据与 CPU 开销。不要修改线上配置或服务。”

第三组提示词用于 CI 优化与规则自省

“关于减轻本仓库的 CI 负载你有什么想法？请将想法列成表格，并根据该仓库的历史运行记录，按每天预估节省的 CI 分钟数进行排序。”

面对不稳定测试修复，他们运行：

“/goal 查看所有最近的 CI 运行记录，找出偶发失败的测试并尝试修复，成功则提交PR。你的任务是针对简单问题找到明确修复方案，而不是穷尽修复所有问题。”

针对 agent 上下文规则的自检提示词更精妙：

“翻阅我最近的 Codex 会话记录，找出一条误导了 agent 或导致无效工作的 AGENTS.md指令。选出该会话提示词，在相同仓库快照和模型下重放两次：一次保持原指令不变，一次仅移除或修正该指令。对比任务质量、agent
行为、工具调用、token 消耗与成本，展示该指令并说明修改是否有效。”

强烈建议好好学习和利用这些应用于超大工程实践的提示词

[April @April1qing]

@AlexFinn @bot 看下这个视频

[April @April1qing]

@ScarletKc 把 Fable 5.1 排在 Astra 前面挺有意思。实际用下来，两者在复杂任务上差的是深度还是稳定性？

[April @April1qing]

@lxfater 花几万攒 V100 跑 DeepSeek V4.1，结果 Opencode 上免费了，这心���历程太真实了。现在那条免费线路排队和限速怎么样？

[April @April1qing]

@dotey 额度按周单独算，不占 3.8 Flash 的份，这点对长期用 Antigravity 的人挺关键。每周到底给多少、哪天重置，文档里还没写，有人已经收到了吗？

[April @April1qing]

Matt Pocock 说，让 agent 写前端时，先让它搭一个状态机，哪怕只是一个 reducer，代码都会稳很多，之后也更好改、更好测。加载、报错、重试这些状态写清楚了，后面就不用一处处补 if-else。#AI编程 #前端
https://t.co/s5TJKfYDVW

[Matt Pocock @mattpocockuk]

I think I'm addicted to getting agents to build state machines

Every time I see an agent has built a state machine (or even just a reducer) I can feel a huge bump in the robustness

And I know that future updates will be trivial and testable

Would recommend for frontend

[April @April1qing]

Meta 的 Muse Wonder 已经能在 Cursor 里用了。它在 CursorBench 上拿了 46.1%，是 Muse 系列目前最高的分数，价格也比不少前沿模型���宜，可以跟 Opus 换着用几天比比看。#Cursor #AI编程
https://t.co/Szmr7Yptf4

[Cursor @cursor_ai]

Muse Wonder from Meta Superintelligence Labs is now available in Cursor!

It scores 46.1% on CursorBench at default effort, the highest of any Muse model.

Priced at $4/M input and $20/M output tokens.

[April @April1qing]

Opus 5.5 的快速模式取消候补了。模型还是同一个，速度最高快 2.5 倍，价格是标准版的两倍，Azure 上已经能用。#Claude
https://t.co/CukFPO7MJW

[ClaudeDevs @ClaudeDevs]

Opus 5.5 fast mode in the Claude Platform is no longer in research preview, and we’ve removed the waitlist!

It’s the same Opus 5.5 but up to 2.5x faster, at $8 / $40 per million tokens.

It's also available in Microsoft Foundry (Hosted on Azure), with Google Cloud coming soon.

Fast mode in Claude Code remains unchanged, and uses extra usage credits.

See our docs here:
https://t.co/RikPqqIY0W

https://t.co/oB0fhEgC88

[April @April1qing]

Claude Code Review 对 Team 和 Enterprise 正式开放了。它比预览版快了大约 3 倍，每个仓库可以单独选审查深度。管理员打开一次，之后每个新 PR 都会自动审，开发者什么都不用配。#Claude #AI编程
https://t.co/qlPUrSZK2V

[ClaudeDevs @ClaudeDevs]

Code Review is now generally available for Claude Team and Enterprise plans.

Reviews are now faster, catch more of the bugs that matter, and admins can pick from Quick, Standard, or Deep review levels for each repo.

[April @April1qing]

Meta 发布了 Muse Spark Open，总参数 816B，每次激活 42B，上下文 100 万 token，两台 Mac Studio 就能在本地跑起来。Nathan Lambert 说，美国开源模型和中国之间的差距，这次至少缩短了一半。#开源模型 #Meta
https://t.co/xf2CejQWO9

[AI at Meta @AIatMeta]

Today we’re releasing Muse Spark Open, the most capable US open-weight model to date.

Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning.

Key Model Specs:
→ 816B total / 42B active parameters
→ 1M token context window
→ Natively multimodal across text, image, video, audio

[April @April1qing]

@shikamarutsai @bot