Sylvester Obesé

The Age of Research & Enlightenment is Now 🔍 | Research fellow @NormaTechgh | prev ML engineer intern @plantvillage | Agricultural Engineering @UCCGH_Official

[Sylvester Obesé @geekslyy]

@ImClique_

[Ryan Peterman @ryanlpeterman]

Vlad Feinberg (@FeinbergVlad) is Google DeepMind’s pre-training area lead and I asked him all about how to land a job at a frontier lab like Google DeepMind, Anthropic or OpenAI.

In this episode:

• Skills frontier labs need
• Differences between software engineering and AI research
• Domains that matter for frontier research
• Concrete steps engineers can take to get closer to research
• Jeff Dean spot bonus story

Where to watch:

• YouTube - https://t.co/kqn9GtPHOu
• Spotify - https://t.co/MhHYqoOTwt
• Apple Podcasts - https://t.co/jOYDGtGVnt
• Transcript - https://t.co/rza5lwT9Wj

Thank you to the sponsor of this episode for supporting my work:

• WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at https://t.co/y8noBzFEem

Chapters:

00:00 - Intro
00:33 - Skills frontier labs need
08:45 - The difference between AI research and engineering
21:41 - Domains that matter for the frontier
30:50 - Marketing yourself to frontier labs
35:13 - Concrete steps engineers can take
38:29 - Overview of pre-training areas
47:23 - Jeff Dean spot bonus story
50:14 - Favorite Gemini war story
58:59 - Advice for his younger self
01:03:07 - Outro

[University of Cape Coast (UCC) @UCCGH_Official]

Official Statement on the untimely demise of Miss. Innocentia Atsufui Avinu.

May her soul rest in peace 🙏

[Xudong Han @Xudong07452910]

最近看到 Anthropic 研究员 Vivek @itsreallyvivek 的一篇长帖，讲怎么真正训练自己的研究能力。

我最有感触的一点是：很多时候，我们以为自己在做研究，其实只是在追热点、刷 Arxiv、看别人讨论什么，然后把那些问题接过来继续做。

这当然也能产出东西，但很容易变成一种科研角色扮演：看起来很忙，输入很多，笔记很多，实验很多，可心里其实并不确定自己到底在追问什么。

Vivek 这篇帖提醒我，研究能力不是某种玄学天赋，而是一套很朴素的习惯：自己选问题，实验前先预测结果；要多读读原paper；把假设、结果和想法变化都写下来，盯着失败案例，而不是只盯着损失曲线。

越往后越觉得，做研究真的很像训练自己。

你给大脑喂什么输入，它就会长出什么 taste；你愿不愿意面对错误，决定了你进步的速度；你写不写下来，决定了你是在诚实迭代，还是说在靠记忆去美化自己。

同质化输入，只会带来同质化输出。

真正拉开差距的可能是谁能在很长时间里，慢慢训练出属于自己的问题感、判断力和纠错系统。

这篇帖很适合迷茫的时候读一遍。

[Codetopia Community @codetopiacomm]

Growth is built one step at a time 💡

🌟 Mentee of the Week: Konde Ramzy Gbati

Your commitment, consistency, and willingness to learn have stood out this week. Keep going

We're rooting for you! 🚀

#CodetopiaCommunityMentorshipProgram2026 #MenteeOfTheWeek #CodetopiaCommunity

[Mr. OJ @_Just_ice]

by now Mandela vex Nkrumah for asamando

[clem 🤗 @ClementDelangue]

Concentration of power, capabilities and economic wealth is the biggest risk in AI. We need open science and open-source more than ever!

[Tulip King 🌷 @tulipking]

i look forward to our chinese brothers liberating the knowledge from within fable-5 and selling it to me at 5% the cost & 2x the speed

[Ahmad @TheAhmadOsman]

All roads lead to small and specialized models btw

[Sylvester Obesé @geekslyy]

@sycode_x @geeksilas I get free credits from Cursor events I’ve attended so I haven’t paid for one yet. I also haven’t experienced running out of tokens quickly. For pricing, there is a $20/month and $60/month option. I don’t know about enterprise pricing, but I think it’s pretty cheap.

[Blue! 🎀 @bluebeari3]

THANK YOU, WEMBY,
THANK YOU SPURS,TRULY .

[Sylvester Obesé @geekslyy]

@ImClique_

[Sylvester Obesé @geekslyy]

@ImClique_

[San Antonio Spurs @spurs]

YOUR SPURS ARE HEADED TO THE 2026 NBA FINALS!!!!

[Prime Intellect @PrimeIntellect]

Today, we are launching Hosted Evaluations on the platform.

Running evals is an infra problem: harnesses, sandboxes, hours of compute, hundreds of parallel runs.

Running evals is hard. Until now.

[Sue @suekhim]

AI is making kids dumber.

It should be making them geniuses.

Introducing Koji, the first AI tutor that gets kids to actually think. 👇

[Zihan "Zenus" Wang @wzenus]

Very interesting work! It should motivate much room for research in fast VLMs/VLAs :)

[alphaXiv @askalphaxiv]

New paper from Yann LeCun!

"When Does LeJEPA Learn a World Model?"

This paper proves that under Gaussian latent dynamics, LeJEPA can recover the hidden state behind nonlinear observations up to rotation.

The intuition is that linear latent features are the most stable across nearby views, while nonlinear features decay faster, so the objective naturally selects the real world variables.

The key caveat is that this guarantee holds under specific assumptions, and Gaussian latents are the unique case that guarantees this.

[NVIDIA AI @NVIDIAAI]

This #CVPR2026 paper from our research team is trending #1 on @HuggingFace 🤗

Meet LocateAnything: a vision-language detection model that rethinks bounding box prediction. For AI agents and robots, “seeing” is only useful if a model can pinpoint where something is fast enough to act.

Trained on 138M high-quality samples, LocateAnything decodes bounding boxes in parallel instead of one coordinate at a time, improving localization accuracy while dramatically increasing throughput for visual grounding and detection.

Project page: https://t.co/O7JMe8tzFM

[Noam Brown @polynoamial]

After AlphaGo, the skill of human Go players noticeably improved. I suspect we will see a similar pattern in math.