Andrew

[Dr. Sylvie Watikum 🇨🇲🇯🇵 Migration Intelligence @sylviewatikum]

Your Statement of Purpose (SOP) should NOT just tell the university that you are passionate and hardworking. 🎓

By the time someone finishes reading your SOP, they should clearly understand these 8 things about you. 👇

1️⃣ WHO ARE YOU ACADEMICALLY?
What have you studied?
What parts of your academic background are relevant to the program you're applying for?

2️⃣ WHY THIS FIELD?
What genuinely led you to this subject?
Don't just say you've “always been passionate about it.”
Give context.

3️⃣ WHAT HAVE YOU DONE SO FAR?
Show evidence.
🔬 Research
💼 Work experience
📚 Projects
🏆 Achievements
🤝 Volunteering
🧪 Internships
Use the experiences that actually support your application.

4️⃣ WHY THIS SPECIFIC PROGRAM?
What does the program offer that connects with what you want to learn or do next?
Mention specific modules, research areas, facilities, or opportunities where relevant.

5️⃣ WHY THIS UNIVERSITY?
Please don't write:
“Your prestigious university is internationally renowned for academic excellence.”
You could send that sentence to 500 universities.
Show that you've actually researched THIS university.

6️⃣ WHAT DO YOU WANT TO DO AFTER GRADUATION?
Your SOP should connect:
Past → Present → Future
Where have you been?
Why are you pursuing this degree now?
Where is it supposed to take you?

7️⃣ WHY ARE YOU A GOOD FIT?
What knowledge, experience, skills, or perspective will you bring to the program?
Admission isn't only about what the university can give YOU.

8️⃣ WHY SHOULD THEY BELIEVE YOUR STORY?
This is where everything needs to connect.
Your degree.
Your experience.
Your chosen program.
Your university.
Your future plans.

🎯 Want to know your chances of winning a fully funded scholarship? I'll personally review your CV, academic certificates, and profile, identify your strengths and weaknesses, and recommend the best next steps for your scholarship journey. 🎯

Comment "Interested" below, and I'll send you the link to get started.

[Matt Deitke @mattdeitke]

I’ll take the opposite point, that for the vast majority of people, it probably doesn’t make sense to do a PhD in AI right now, and is better to join industry. I say this with love for Phillip, whose work and philosophy I do admire, and respect for putting this out there!

Everything in AI is, generally speaking, working well. Most things come from stacking wins and good ideas over and over again, and if you’re in industry, you have a much better opportunity to learn what is actually working and what doesn’t work, and then push the forefront of what’s possible at a much larger scale. Internships also don’t really feel the same because you’re certainly not working on the most important projects for the company and often come in with a high risk agenda and work alone. In industry, you learn a lot about how to manage budgets, work in large teams, and push projects that require a lot of effort and work to get done.

Academic project budgets are generally quite minuscule, maybe being O($1 - $100K) for many projects and you’re often working alone. If you really want to push novel ideas, another route I don’t think most people consider is also a startup, where you may be able to raise 1-100x more money to do the same project you may do in a PhD, with more resources and the ability to hire other people. And, if it goes well, the ability to scale it dramatically.

When it comes to hiring, often times, it’s easier to get an offer having worked well with other people in industry and then having them change jobs and bring you over. Doing a PhD can get your foot in the door, but often it’s still for entry level positions after doing a 5-year PhD. In industry too, you also will make a lot of strong connections, similar to those you’d make in academia. Though it might be the case that it’s good to do 1 year of a PhD and then drop out to work in industry at this point to get the benefit of both.

Doing a PhD right now is on extremely hard mode, where not much work ends up mattering and even if it works, there’s high cost to change the ways of industry to adapt something that might provide incremental gains when everything generally is working well with enough resourcing and effort.

[UC Berkeley EECS @Berkeley_EECS]

Are you applying to grad school this fall? The Equal Access to Application Assistance program for @Berkeley_EECS is now accepting applications! Any PhD applicant to @Berkeley_EECS can submit their application for feedback by Oct. 15th at 11:59 PM PST. https://t.co/hQ9pSgsBuR

[Kosta Derpanis @CSProfKGD]

It’s that time of year again: graduate admissions season.

Inspired by @phillip_isola’s post today on doing a PhD in the age of AI (highly recommended!), I share some thoughts on how to email a professor about joining their lab.

It’s a first cut, one person’s perspective, and meant to be a living document and discussion starter. Add your perspective via Margin Notes!

[PyTorch @PyTorch]

Every mature systems project eventually needs a teaching version.

TinyTorch is a free, open source curriculum where you build a working machine learning framework from scratch, tensors through transformers, using PyTorch’s own API in pure Python.

It requires no GPU, runs on a 4 GB laptop, and covers 20 hands-on modules designed to give developers, students, and engineers a complete mental model of PyTorch internals.

Read the full technical breakdown here: https://t.co/UFeJKNWKmO

@profvjreddi

[Jacob H. Rothschild @ja_rothschild]

For those who missed it yesterday, I wrote the explainer on robot control I wish I'd had when I got into robot learning. For ML people, no robotics background assumed, with animations.

What's in it:
- what a policy's output actually does to a servo
- PID, and why a cheap arm overshoots (clip below)
- how teleop data gets recorded
- joint space vs. task space, FK and IK
- why an SO-100 can reach exactly 0% of the gripper poses within arm's length
- why ACT outputs joint angles, while OpenVLA doesn't

Everything is shown on the SO-100 arm that @LeRobotHF uses. Next one will be on ACT. Follow me to catch it.

[Simons @Simon_Ingari]

If HR asks what your previous salary was, never:

Share your previous salary.
Say you signed an NDA.
Make up a fake salary.

Instead say :

[Hiroshi Takahashi @taka8hiroshi]

This paper was rejected from KDD 2024, NeurIPS 2024, AAAI 2025, ICML 2025, NeurIPS 2025, ICLR 2026, and ICML 2026 before eventually being accepted by Neurocomputing.

It was a long journey, but I’m glad we kept working on it 🎉

[Sahil Goyal @vibespersecond]

PhD applications will be starting soon! Time to start drafting your SOP.

Sharing my SOP from last cycle with annotations and general takeaways. Hope this helps people applying this cycle!
https://t.co/ZG0gKSZ1ov

[Andrew @tcminh03]

@RuiqisNotes @richardzhangsfu Hi, I'm a researcher on this field but am not able to attend ECCV this year. Will there be any written or vid recording of the talk? Thanks

[Hitesh @heetezition]

CoRL 2026 results are out.

these are the 10 papers I’d read first:

[Emmanuel Tsekleves @PhDtoProf]

Most papers are not rejected because of the research. They are rejected because of the end of the first paragraph.

I have published 135+ peer-reviewed papers and reviewed many more.

When I read an introduction, I am looking for a sequence of moves. Most people do not know the sequence exists.

[R𝛼m 🦅 @rambuilds_]

Research papers you must read for AI Engineer interviews -

1. Attention is all you need (Transformers)
2. LoRA (Low rank adaption)
3. PEFT ( Parameter Efficient Fine Tuning)
4. VIT (Vision Transformers)
5. VAE (Variational Auto Encoder)
6. GANs ( Generative Adversarial Networks)
7. BERT ( Bidirectional Encoder Representation from Transformers)
8. Diffusion Models (Stable Diffusion)
9. RAG (Retrieval Augment Generation)
10. GPT (Generative Pre-trained Transformers)

[Mahax @Mahaximus_]

Andrew Ng just dropped a 2-hour course on Graph Engineering: from Loops to full automation

9:14 - Your first agent
33:11 - Loop engineering
1:02:46 - Graph engineering
1:30:15 - Agents that rewrite themselves
1:49:05 - Full graph system

Free, the best thing on graph engineering I've come across

Watch it, then build your first graph with the guide below

[Anatoli Kopadze @AnatoliKopadze]

Anthropic engineer:

"You don't need better prompts. You need graph engineering that makes agents remember everything."

In 25 minutes he shows how engineers at Anthropic wire agents into a graph where each has a job, they run in parallel, verify each other, and share a memory that never resets.

This beats any paid course on building agents I've seen.

Watch it, then read the full guide on graph engineering below.

[Andrew @tcminh03]

@NeurIPSConf I would like to ask if authors are allowed to include links in rebuttals that point to images per reviewer request of qualitative shows?

[Codez @0xCodez]

Anthropic Managed Agents Lead:

"At Anthropic, >90% of our engineers are building with self-improving loops. In 4-6 months, it will be 100%.

my agentic loops can run for hours without spending hundreds of dollars."

in this 40-minute podcast, an Anthropic team lead reveals how to build effective agents from scratch.

Agent → harness → loops → memory = modern agent

This one video will replace 10 paid courses on vibe-coding.

Watch it today, then explore the same setup in the article below.

[unicode @unicodef1wn]

Anthropic engineer:

"Over 90% of Anthropic engineers use a self-improving agentic loop for coding.

The key is to close the loop: give the agent a way to verify and improve its own work"

In this 12-minute talk, an Anthropic engineer explains how to build an agent that continuously improves itself.

Claude + routines + loops + CLAUDE.md - that's the foundation.

Skip Netflix tonight. Save this post!

[Raytar @Raytar]

Anthropic shows this video to every new employee. Someone re-uploaded it.

I hope Anthropic doesn't see this.

14 minutes of how the Claude team actually uses Claude in real work.

I watched the recording last night and kept pausing it. Each time realizing I've been using Claude like a toy.

claude.md + loops is what makes Claude stop fighting you and start working for you.

Most people will keep using Claude the hard way.

[Raytar @Raytar]

Andrej Karpathy joined Anthropic five weeks ago.

Yesterday my friend on his team sent me the Claude.md file he actually uses.

It completely changed how I work with Claude.
From the very first message, the difference was obvious.

With this file, Claude finally stops fighting me and starts working exactly the way I need it to.

Bookmark it before it gets taken down.

Read it now, then check the article below.