Nick

In code we trust.

[Xudong Han @Xudong07452910]

这篇论文让我感觉，我们对「AI 会取代程序员」这件事的讨论方向可能全错了。

核心观点：AI Agent 的出现不是让软件工程师工作效率更高，而是让「把决策逻辑永久编码进软件」这件事本身变得越来越不必要。

作者说的是一个更根本的范式变化：传统软件工程的本质是，人类把判断逻辑「固化」成代码——if-else、状态机、算法，这些都是把人类决策「提前写死」的方式。但在以 LLM 为核心推理引擎的 Agent 系统里，代码变成了「临时生成、用完即扔的工具」，每次任务，Agent 动态生成需要的代码，执行完就不需要了。决策不再被预先编码，而是在运行时由 LLM 推理循环动态产生。这不是增量改进，而是软件生产范式的结构性替换。

我觉得这个观点里最值得注意的细节是：这不只是生产力工具的升级，而是「软件」这个概念本身的角色在变。以前代码是「系统的中心」，Agent 框架是外壳。现在 LLM 推理循环是中心，代码变成了外壳里的临时辅助。如果这个趋势持续，软件工程的核心能力可能不再是「写出好代码」，而是「设计出可靠的推理约束边界」。

以前我们关注写出可维护的代码，以后可能更要关注设计出可靠的推理边界。

https://t.co/28rZe5qvBP

[Connor Ewing @ConnorMEwing]

Good job, Pope.

[Rohan Paul @rohanpaul_ai]

New Google paper: A forecast needs context, not just history.

Some patterns are caused by events, not time. Nexus reframes forecasting as a reasoning problem, where events and numbers have to explain each other.

Nexus argues that forecasting improves when models read the world around the numbers, not just the numbers themselves.

In the Zillow tests, one Claude-based version cut average MAPE by 86.6% versus direct chain-of-thought prompting.

That matters because most time series models are fluent in pattern, but mute about cause.

A housing inventory curve can reflect seasonality, mortgage pressure, migration, layoffs, and local supply, while a stock price can be bent by earnings, regulation, hype, and fear.

Nexus separates those jobs instead of asking one prompt to do everything.

One agent turns messy historical text into a clean event timeline, one reads the broad regime, another tracks local shocks, and a synthesizer reconciles them with calibration from past errors.

The interesting result is not merely that context helps, but that structure helps the language model use context without losing the time series.

The evidence is still narrow: Zillow counts, seven equities, post-cutoff data, and single-run evaluations, so this is not a universal law of forecasting.

But the direction is clear: future forecasters will not only extrapolate curves; they will argue about what made the curve move.

----

Paper Link – arxiv. org/abs/2605.14389

Paper Title: "Nexus : An Agentic Framework for Time Series Forecasting"

[safari @safaricheung]

5 月 2 日，中国东方航空 MU5406 (B-324W)执飞成都双流至上海虹桥航段，落地进位过程中，出现越过停止线并反推连续撞击廊桥导致左翼以及250机位宽体廊桥明显受损，廊桥操作员情况不明。

[Nick @ipaddicting]

Regenerate on Stitch is useless... More like a random pick without following any previous instructions.

[Physics & Astronomy Zone @zone_astronomy]

The highest quality video of the moon was just released… this is so beautiful.

[David Vance @DavidVance]

This is what economic suicide looks like;

[一只麋鹿 @JaneCarney87]

2021年，德州心跳法案通过后，Josseli Barnica，一位28岁的孕妇在怀孕约17周时出现流产迹象。她两次去医院，但医生表示胎儿仍有心跳，必须等待心跳停止才能干预��最终她分娩后感染败血症死亡
当一些伪君子在指责伊朗神权强迫女性戴头巾时，灯塔美国请照照镜子自己是不是神棍国家，是不是也该挨导弹？

[宝玉 @dotey]

以前程序员是等编译，现在是等生成

[Look at my shining eyes @GothicOrnate]

SeeDance 2.0 这个做的好有创意 非常漂亮

[阑夕 @foxshuo]

关于Qwen基模负责人林俊旸离职，我要说句不意外，肯定有人又要说我马后炮，但其实我这几个月来在上播客和写稿子里不止一次的提到过一个细节：

清华大学那场AI-Next峰会上，林俊旸邀请观众体验Qwen，用的是https://t.co/aoUtwXThw0这个根本没有运营投入的实验室网址，而不是千问App。

模型干模型的，产品干产品的，这当然是模型团队的舒适区了，只投身研究、不负责市场，Qwen在开源社区里的究级口碑就是这么打下来的，但产品团队就难受了，做什么事情都需要和自己平级的模型团队配合，只能协调，不能统筹。

产模分离是阿里过去几年里最奇怪的地方，在千问App还没有被独立出来发展的时候，还可以说���行且珍惜，现在千问App要直接和豆包元宝打消耗战了，还让模型团队保持例外论，这肯定行不通。

最新的形势就是，大厂在做DAU，豆包现在还在发红包你敢信，小虎在冲ARR，月暗智谱MiniMax三家给洋人卖Tokens卖疯了，那么Qwen的位置在哪里呢，你总得沾个边吧？

事实上，AI一号位工程的调整，是这一年来几乎所有国内互联网大厂都在做的事情，字节和腾讯都干了，轮到阿里大家觉得很奇怪，主要还是Qwen模型本身没出太大问题，突然换将有些刺激。

还是那句话，组织的方向很重要，不认同组织的判断，就会很麻烦，这和你是不是一个好人、你做的工作是不是很优秀，关系不大。

就像没人会不尊重杨立昆的学术成就，没有他老���家的贡献，AI的发展指不定还要晚多少年，但大家也都心知肚明，继续让他带Meta的AI实验室，那Meta就真完蛋了，你不可能让一个不相信Transformer的人去带领大模型⋯⋯

前段时间，因为诉讼披露，微软的CTO有一封2023年底发给老板的邮件被曝光，里面是他但是作为对接人看到并汇报的OpenAI宫斗事件，说以Ilya Sutskever为首的研究团队讨厌需要和产品团队争夺资源，how dare you，竟敢让我们排队等GPU！

微软的CTO在此批注，表示自己内心都是崩溃的：「OpenAI的研究团队根本没有明白，如果没有Applied的商业成功，他们根本不可能拥有现在这么多GPU。」

所以，从国内到海外的实践经验都说明，产模分离是一个���园牧歌的理想状态，它可以暂时性的存在，但前提是公司没有市场回报的预期，愿意千金买马骨，Qwen这几年过的的好日子，都是这么来的。

新的剧本就是阿里不愿意了，作风依然很粗暴很阿里，但就目的来说，其实没那么大的争议。

再说了，和OpenAI的11个联合创始人里走了9个相比，这才哪到哪啊⋯⋯

记得马老师曾说阿里要每年定期给社会输送人才，结果都是些年满35岁后被优化出来的P7P8们，给接收方的牛马们带去了一点点阿里味的震撼，连山姆看似牢不可破的名声都差点被打崩了，不过这次林俊旸流入自由转会市场，就没得阴阳了，是真的大牛出圈，大厂们赶紧抢吧。

[白骏知识分享 @cj3214567667]

Seedance2.0太强了，侃爷《新鸳鸯蝴蝶梦》MV~

[Luke Barnett @LukeBarnett]

Literally 2026.

[Peter Girnus 🦅 @gothburz]

Last quarter I rolled out Microsoft Copilot to 4,000 employees.

$30 per seat per month.

$1.4 million annually.

I called it "digital transformation."

The board loved that phrase.

They approved it in eleven minutes.

No one asked what it would actually do.

Including me.

I told everyone it would "10x productivity."

That's not a real number.

But it sounds like one.

HR asked how we'd measure the 10x.

I said we'd "leverage analytics dashboards."

They stopped asking.

Three months later I checked the usage reports.

47 people had opened it.

12 had used it more than once.

One of them was me.

I used it to summarize an email I could have read in 30 seconds.

It took 45 seconds.

Plus the time it took to fix the hallucinations.

But I called it a "pilot success."

Success means the pilot didn't visibly fail.

The CFO asked about ROI.

I showed him a graph.

The graph went up and to the right.

It measured "AI enablement."

I made that metric up.

He nodded approvingly.

We're "AI-enabled" now.

I don't know what that means.

But it's in our investor deck.

A senior developer asked why we didn't use Claude or ChatGPT.

I said we needed "enterprise-grade security."

He asked what that meant.

I said "compliance."

He asked which compliance.

I said "all of them."

He looked skeptical.

I scheduled him for a "career development conversation."

He stopped asking questions.

Microsoft sent a case study team.

They wanted to feature us as a success story.

I told them we "saved 40,000 hours."

I calculated that number by multiplying employees by a number I made up.

They didn't verify it.

They never do.

Now we're on Microsoft's website.

"Global enterprise achieves 40,000 hours of productivity gains with Copilot."

The CEO shared it on LinkedIn.

He got 3,000 likes.

He's never used Copilot.

None of the executives have.

We have an exemption.

"Strategic focus requires minimal digital distraction."

I wrote that policy.

The licenses renew next month.

I'm requesting an expansion.

5,000 more seats.

We haven't used the first 4,000.

But this time we'll "drive adoption."

Adoption means mandatory training.

Training means a 45-minute webinar no one watches.

But completion will be tracked.

Completion is a metric.

Metrics go in dashboards.

Dashboards go in board presentations.

Board presentations get me promoted.

I'll be SVP by Q3.

I still don't know what Copilot does.

But I know what it's for.

It's for showing we're "investing in AI."

Investment means spending.

Spending means commitment.

Commitment means we're serious about the future.

The future is whatever I say it is.

As long as the graph goes up and to the right.

[naiive @naiivememe]

Me making first payment of 50-year mortgage

[Nythis @Nythis110929]

Donald Trump's Door Bell Camera 📸

[Ода НетРussie @X_41822Nobunobu]

最搞笑的部分还是当他们想要画一个巴勒斯坦人的时候
他们只能画一个恐怖分子

[Spencer Hakimian @SpencerHakimian]

NASDAQ call volume spiked minutes before the 90 day tariff pause was announced.

Not a good look at all.

[Erka @Erka2013a]

@whyyoutouzhele 贸易关税之王。😁