Io

[Jaynit @jaynitx]

Elon Musk explains his 5-step algorithm for solving any problem:

"The most common mistake of smart engineers is to optimize a thing that should not exist."

"I have this very basic first principles algorithm that I run as a mantra."

Elon breaks it down:

Step 1: Question the requirements.

"Make the requirements less dumb. The requirements are always dumb to some degree, no matter how smart the person who gave you those requirements. You have to start there, because otherwise you could get the perfect answer to the wrong question."

Step 2: Try to delete it.

"Try to delete the part or the process step entirely. If you're not forced to put back at least 10% of what you delete, you're not deleting enough. Most people feel like they've succeeded if they haven't been forced to put things back in. But actually they haven't, they've been overly conservative and left things in that shouldn't be there."

Step 3: Optimize or simplify.

"The most common mistake of smart engineers is to optimize a thing that should not exist. So you don't optimize until after you've tried to delete."

Step 4: Speed it up.

"Any given thing can be done faster than you think. But you shouldn't speed things up until you've tried to delete it and optimize it otherwise, you're speeding up something that shouldn't exist."

Step 5: Automate.

"And then the fifth thing is to automate it."

Elon explains why the order matters:

"I've gone backwards so many times where I've automated something, sped it up, simplified it, and then deleted it. I got tired of doing that. So that's why I have this mantra."

[Io @RolloIo]

@meh_agarwal Gross

[Phosphen @phosphenq]

She's 22. Her AI runs inside the Fortune 100. a16z just led her $21M raise.

"Put up the most minimal fake version you probably can of something and then go sell it."

In 17 minutes, MIT grad Jessica Wu reveals the YC playbook that got her here in 24 months.

From youngest hedge fund quant to founder + the trick her batchmates missed + the Christmas Day deployment + the 10-year founder test

Worth more than a year of YC advice on your timeline

[Io @RolloIo]

@kangminlee Look same as average left wing couple in kooohreeeah

[Io @RolloIo]

@downbadcomment The husband is right.

[Io @RolloIo]

@vkhosla @Stanford @Google @sundarpichai You idiot

[Io @RolloIo]

@RT_com American police are stupid. Too bad Americans are too dumb to realise this

[Io @RolloIo]

@RealPostFolder Lmao this looks so fake

[paperpaper @paperpaper886]

最近在带入组的本科实习生，发现怎么读论文其实是科研训练里最容易被忽略的一步。
推荐一篇每个科研新人都该读的经典短文：S. Keshav 的 How to Read a Paper。

文章提出了非常实用的“三遍读论文法”：
第一遍，5 到 10 分钟快速扫读：标题、摘要、引言、章节标题、结论和参考文献。
目标是回答 5C：
Category, Context, Correctness, Contributions, Clarity。
也就是判断这篇论文是什么、和谁相关、假设是否合理、贡献是什么、写得清不清楚。
第二遍，认真读论文主线，但先跳过证明细节。重点看图表、实验设置、结果是否清楚、引用了哪些关键工作。
第三遍才进入深度理解：尝试像复现一样重建作者的思路，检查假设、方法、创新点和潜在漏洞。

放在今天看，这个方法和 AI 辅助读论文其实很契合。
第一遍可以让 AI 帮忙快速总结论文的研究问题、核心贡献和主要结论，但自己一定要判断这篇文章是否真的值得继续读。
第二遍可以让 AI 帮忙解释方法、实验设置、图表和不熟悉的概念，但不能只看 AI 总结。关键图表、实验设计和结果数字一定要回到原文核对。
第三遍可以让 AI 扮演 reviewer，帮你追问：这篇文章的假设是否成立？实验是否支持结论？有没有 missing baseline？有没有潜在的数据泄漏、评价偏差或过度 claim？

读论文不是“读完”就行。真正重要的是知道什么时候快速跳过，什么时候认真理解。
尤其在 AI 工具越来越强的情况下，科研新人更需要训练自己的判断力。
AI 可以帮你压缩信息，但不能替你决定一篇论文是否重要、是否可信、是否值得借鉴。

https://t.co/8gUc4HbLwR

[Io @RolloIo]

@NothingIsArt2 Don't care. Kill the cunts

[Io @RolloIo]

@johnennis @NickKnudsenUS Yes, Mr (p)Enis.

[Io @RolloIo]

@zackvoell That include you?

[Io @RolloIo]

@benwehrman White people kill each other all the time by accident

[Vaibhav Sisinty @VaibhavSisinty]

A guy on Reddit with 10 years of engineering experience just shared the one thing he'd teach every vibe coder first.

And it'll save you thousands in AI costs. 🤯

Most people using Claude Code use it the expensive way. They call the AI every time the tool runs. Every run burns tokens. Every token costs money.

His advice: flip it. Use Claude Code to BUILD the tool once. Then run it forever without spending a single token.

Simple example. You want to check a website daily for updates. The expensive way: have an LLM search the site every day. Burns tokens every single time.

The free way: use Claude Code to write a script that scrapes the page and alerts you if anything changed. Build it once. Runs forever. Zero tokens.

Then he took it further. He had Claude Code build him a full neural network something that used to take weeks and years of ML training while he cooked dinner.

It runs for free. No tokens. No API calls. Forever.

Spend tokens once to build it. Run it for free forever.
That's it. That's the insight most vibe coders are missing.

[Professor Charley T | The Meta Ads Guy @CTtheDisrupter]

I saw this and I totally agree. I think what's missing here is the little wrinkle that some ads can check multiple boxes, and a 322 ad can create multiple ads. A 322 ad can fill the role of multiple ads.

In the Olympic rings analogy that I use to teach creative strategy, we can follow this exact framework of the three rings on top being one for each avatar, and the two rings on the bottom being ads designed as a second touch from more than one avatar. Now you're sitting with four to eight proven ads, with two or three creative tests being all you need

[Io @RolloIo]

@KingTargaryenn It was so fucking shit wasted fucking my life

[Gurnoor Singh @gurnoor__]

Brands with the best Creative Strategists:

> Spacegoods
> O Positiv
> Hexclad
> Primal Queen
> Hi-Smile
> Gymshark
> Norse Organics
> Jones Road
> Gruns
> Manscaped
> Qure Skincare
> Hike Footwear
> Rosabella
> Spartan
> Happy Mammoth
> Undrdog Hemp
> Nooro
> Kovaria

[Anuj @anujcodes_21]

Claude FULL COURSE 1 HOUR (Build & Automate Anything)

[How To Prompt @HowToPrompt__]

A toothpaste company has quietly killed the entire market research industry and nobody is talking about it.

Colgate published a paper showing you can predict real purchase intent at 90% accuracy by simply asking LLMs to roleplay customers.

And this is beyond insane.

If you ask an AI, "Rate this product from 1 to 5," it gives safe, middle-of-the-road garbage.

So researchers invented a method called Semantic Similarity Rating (SSR).

Instead of asking the AI for a number, they asked it to roleplay.

They gave the LLM a demographic profile. They showed it a product concept. And they asked it to write down its raw, unfiltered thoughts.

Then, they used a semantic model to translate those written thoughts into a numerical score.

The results are staggering.

Tested against 57 real corporate surveys and 9,300 actual human responses, the synthetic AI consumers matched real human buying behavior with 90% reliability.

They perfectly mirrored how different age brackets and income levels react to price changes.

And they provided detailed, qualitative feedback that was deeper and more critical than what actual humans wrote.

This destroys the economics of traditional market research.

You don't need to wait a month to see if a product will sell.

You can simulate 1,000 hyper-targeted customer interviews overnight.

You can A/B test pricing across every demographic instantly.

[Olly Hudson @oliverwhudson]

If you run Meta ads, this is the only video on Andromeda you need to watch.

We sat down with a Creative Strategist at Meta, someone who works directly with the top 1% of disruptor brands on the platform.

Here's our full conversation

00:00 Working with Disruptor brands at Meta
02:34 What is Andromeda?
09:34 Andromeda + GEM working together
11:15 The shift from targeting to creative
14:50 Creative similarity explained
19:10 Targeting personas
23:30 Creative strategists in the AI era
25:13 Creative themes
30:29 Is changing the script enough?
36:56 How many creative concepts should you run?
41:22 Brand consistency vs creative diversity
43:40 Partnership ads deep dive
52:55 Meta AI tools brands should be using
57:56 Measurement & ad account structures
1:01:18 Advice for brands wanting to become Disruptors
1:02:40 Where to stay up to date with Meta updates

our most valuable episode to date