金田

AI 服务员💁‍♂️

[卡颂 @kasong2048]

/grill-me 如何在“不降低效果的同时提高提问效率？”
答案是：将“每次提一个问题”改为“每次提一批问题”

更本质来说，如果我们将问题划分为：
- 已知的已知
- 已知的未知
- 未知的已知
- 未知的未知
当 Agent 反复提问 已知的已知、未知的已知 时，经常进入“一路回答 ok”的尴尬境地
本次改动就是在提问前，先将所有上述两种 已知 找出来

[Boris Cherny @bcherny]

I talk to engineers at other companies every day and hear the same thing: one person is 10x'ing their output with Claude but the rest of the org hasn't caught up.

Watching teams adopt AI, I keep seeing the same 4 steps.

I mapped them out here: Steps of AI Adoption https://t.co/kQnRAUMKpP

[金田 @565gonn]

@zhyyu99 @xiaohua_888 哈哈 ok

[金田 @565gonn]

@xhkiyo @xiaohua_888 其实挺简单的，你也可以

[金田 @565gonn]

@Sukiea1008 @xiaohua_888 ❤️❤️❤️

[金田 @565gonn]

@dyhdhdbsnsk @xiaohua_888 我和 codex 说，你这个也不实用啊，我想要的是真实的效果，把背景和侧边栏也改改，（截图指示他），然后也强调要用蔡徐坤的元素

[金田 @565gonn]

@si_qing0821 @xiaohua_888 也是看到了有人定制明星专属微信名，想到做皮肤的话，也做个明星的，想想谁流量高，大家接受度高的通用的，感觉坤坤很好玩就试试这个先，也想了其他主题的皮肤，先优化完这个，打磨成品后再弄 skill，后面会方便生成其他的主题。

[lifcc @mylifcc]

OpenAI 发布了 GPT-5.6 Sol 的官方 Prompting Guide。明确告诉我们：GPT-5.6 的最优 prompting 方式，和 GPT-5.5 / GPT-4 已经完全不同了。

官方内部数据：
精简 prompt 后，评测分数 +10-15%，token 降低 41-66%，成本降低 33-67%。
结论很明确：过度工程化的 prompt 正在成为新的瓶颈。

核心变化只有一句话：
从“手把手教怎么做”，变成“定义结果 + 边界 + 证据，让模型自己选最优路径”。
过去那种又长又详细的 prompt，在 GPT-5.6 上反而会拖累性能。

GPT-5.6 新特性 Programmatic Tool Calling：
模型可以写 JS 在沙箱里处理大量工具返回结果（过滤、聚合、去重等），大幅减少来回调用轮次。
适合需要批量处理数据的场景。

社区总结的 IDEA 框架很好用：

Intent：最终目标
Data：可用证据
Edges：边界与限制
Answer：交付格式与成功标准

比传统 prompt 模板更适合 GPT-5.6。

迁移建议：
不要一次性大改旧 prompt。 先切换模型跑 baseline，再逐步删掉重复指令，只保留真正改变行为的规则。
每改一次就验证效果。

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[Boris Cherny @bcherny]

Something I have been thinking about: in the past, the best engineers I knew spent a lot of time automating their work in various ways. Better vim/emacs automations, writing lint rules to catch repeat code issues, building up a suite of e2e tests so they don't need to smoke test the app manually. These kinds of things were the highest leverage activities an engineer could do, because it multiplied their own output, which in turn meant they could build more things.

I think many of these automations have become even more important now. This is true for a number of reasons.

First, infra and DevX automation speeds you up. And if you are running an army of agents, each of those agents will be sped up also. More automation == more output per unit of time.

Second, moving things to code improves efficiency. Your agent could fix an issue every time it sees that issue happen, but that uses tokens and might miss cases. If Claude instead writes a lint rule, CI step, or routine, that class of issue can be fully automated forever. This is really what people are talking about when they talk about loops -- it's about automating entire types of busywork rather than solving them one off. This isn't a new idea at all. Engineers have been doing this for a long time!

Third and most importantly, automation makes it possible for others to contribute to the codebase more easily. Increasingly what I am seeing is engineers are contributing to codebases on day one because Claude can navigate the codebase for them, and that non-engineers are able to contribute to a codebase as effectively as engineers can. What gets in the way of both of these is domain knowledge that lives in peoples' heads rather than in automation -- the stuff you used to have to learn when ramping up. What has changed thanks to agents is the domain knowledge that can be encoded as infrastructure is no longer limited to what is expressible in lint rules and types and tests; it can now capture nearly all domain knowledge, encoded as code comments and skills and CLAUDE.md rules and memories. If I put up a PR for an iOS codebase I don't know and a code reviewer rejects it because it doesn't use the right framework, or if a designer builds a new feature and it gets rejected because it doesn't follow the right architectural patterns, these are failures of automation.

Every team should be writing the CLAUDE.md's, REVIEW.md's, skills, and docs that enable agents to productively work in their codebase with zero additional context from the prompter. This sounds crazy, and at the same time is a natural extension of the stuff engineers have always done: automate, and encode domain knowledge as infrastructure. As the model gets smarter and as the harness matures, this task becomes easier. In the meantime, it is on every team to look for ways to convert their domain knowledge to infra so that Claude can write code better, so that code review catches issues automatically, and so the next person working on your codebase can contribute more easily.

[季白羽 @vbjby3]

笑死。。这个风格现在真的嘎嘎乱杀。。。搭配好了。随意都可以出一条视频。。。plus会员在这一刻价值千金。。。再也想不起claude那个渣男了。。

[Cell 细胞 @cellinlab]

AgentKey 不是一个“帮你写爆款”的内容工具。

我更习惯把它理解成 Agent 的统一数据接入层。

它的 Store 目前显示有 58 个服务、1,000+ endpoints，
覆盖搜索、网页抓取、社交媒体、金融、电商和 Crypto 数据。

原来需要分别申请 API、管理 Key、额度和账单的东西，
现在统一到了一个账户和一套 Credits 里。

对我来说，价值不是“API 数量很多”。

而是以前不值得花时间搭建的数据链路，现在可以用一条配置快速验证！

比如，让 Agent 同时读取社交讨论、视频评论和市场数据，再自己完成：

发现趋势 → 收集材料 → 交叉验证 → 提炼角度 → 生成选题 🚀

自己试试 👉 https://t.co/jbEdx2erup

[开发者Hailey @IndieDevHailey]

OpenCut — 目前最火的开源CapCut平替！

GitHub 66k+ Stars 纯浏览器运行（也可桌面/移动端），视频全程本地处理，绝不上云、零安装、零水印、MIT永久免费！

- 多轨时间线、专业剪辑、实时预览
- 本地AI能力（字幕、效果等持续更新）
- 跨平台统一代码：网页 + 桌面 + 移动端
- 隐私优先，所有处理都在你设备上完成

正在快速迭代中，界面和操作手感极度接近CapCut，适合短视频创作者直接上手！

和OpenReel一样，都是浏览器本地剪辑的顶级选择，两个都试试，挑顺手的用就行！

[Thijs @cdngdev]

i gave 5.6 sol access to my camera roll and had it extract pictures of every piece of clothing i own from my photos

then, told it to find new outfits for me and render them on me with gpt-image!

its kinda cool to see your entire wardrobe in a collection like this

[Orange AI @oran_ge]

@Tz_2022 是这样的

[shivam @shivamhwp]

claudex has been achieved.

[Berryxia.AI @berryxia]

喜大普奔啊，𝕏 的任何用户都可以开始免费Grok 4.5 了。

还没有体验的赶紧试试吧，速度是真的快！

地址： https://t.co/Aj00j3Z5oI

[Esther @SjwEsther]

天呐真的成了！！！！！尖叫！！！！！

[Geek @geekbb]

卧槽，兄弟们，CLIProxyAP 上大分，官方认证了属于是，快安装起来吧～
https://t.co/3x2XVcZOtk