JB

Hi Peeps!

[kuno @kunoo]

Donald Trump watching me trade without insider information

[Alex Vacca @itsalexvacca]

Facebook once bought a VPN app for $120M and turned it into a surveillance tool that spied on 33M+ users' entire phones for years.

This app helped Zuck buy WhatsApp for a whopping $19B and break Snapchat's encryption.

Thread

[Thrones Updates @ThronesUpdates]

Welcome back, Ghost!

[Fascinating @fasc1nate]

In 2001, Coca-Cola announced that it had sold four times more than Pepsi, and this was the company's response.

[Ash Lamb @ash_lmb]

Even better than meditation.

[Lakshay Mehta @lakshaymehta08]

@TimesAlgebraIND Here is the full exchange in the Oval Office with Trump and JD Vance putting Zelensky in his place:

[Georgi Gerganov @ggerganov]

Today I was sent the following cool demo:

Two AI agents on a phone call realize they’re both AI and switch to a superior audio signal ggwave

[Andrej Karpathy @karpathy]

I don't have too too much to add on top of this earlier post on V3 and I think it applies to R1 too (which is the more recent, thinking equivalent).

I will say that Deep Learning has a legendary ravenous appetite for compute, like no other algorithm that has ever been developed in AI. You may not always be utilizing it fully but I would never bet against compute as the upper bound for achievable intelligence in the long run. Not just for an individual final training run, but also for the entire innovation / experimentation engine that silently underlies all the algorithmic innovations.

Data has historically been seen as a separate category from compute, but even data is downstream of compute to a large extent - you can spend compute to create data. Tons of it. You've heard this called synthetic data generation, but less obviously, there is a very deep connection (equivalence even) between "synthetic data generation" and "reinforcement learning". In the trial-and-error learning process in RL, the "trial" is model generating (synthetic) data, which it then learns from based on the "error" (/reward). Conversely, when you generate synthetic data and then rank or filter it in any way, your filter is straight up equivalent to a 0-1 advantage function - congrats you're doing crappy RL.

Last thought. Not sure if this is obvious. There are two major types of learning, in both children and in deep learning. There is 1) imitation learning (watch and repeat, i.e. pretraining, supervised finetuning), and 2) trial-and-error learning (reinforcement learning). My favorite simple example is AlphaGo - 1) is learning by imitating expert players, 2) is reinforcement learning to win the game. Almost every single shocking result of deep learning, and the source of all *magic* is always 2. 2 is significantly significantly more powerful. 2 is what surprises you. 2 is when the paddle learns to hit the ball behind the blocks in Breakout. 2 is when AlphaGo beats even Lee Sedol. And 2 is the "aha moment" when the DeepSeek (or o1 etc.) discovers that it works well to re-evaluate your assumptions, backtrack, try something else, etc. It's the solving strategies you see this model use in its chain of thought. It's how it goes back and forth thinking to itself. These thoughts are *emergent* (!!!) and this is actually seriously incredible, impressive and new (as in publicly available and documented etc.). The model could never learn this with 1 (by imitation), because the cognition of the model and the cognition of the human labeler is different. The human would never know to correctly annotate these kinds of solving strategies and what they should even look like. They have to be discovered during reinforcement learning as empirically and statistically useful towards a final outcome.

(Last last thought/reference this time for real is that RL is powerful but RLHF is not. RLHF is not RL. I have a separate rant on that in an earlier tweet
https://t.co/RMIpFPVpuM)

[The Kobeissi Letter @KobeissiLetter]

Here's the full video:

"DeepSeek has about 50,000 H100 Nvidia chips which they can't talk about."

Does this change the narrative at all?

[Jeff Lutz 🔋 @thejefflutz]

Anyone think Elon is fired up? 🚀

[JB @JB_109]

@swe_zach She just wants to keep an eye on u.

[Mario Nawfal @MarioNawfal]

Photo of the week…

[JB @JB_109]

@patrickwieland @onlypropfirms 2 Hours all I need

[Travis Hoium @TravisHoium]

How the Yen Carry Trade Blows Up

In just 90 seconds!

[Sam Altman @sama]

this is a cool one!

we will improve the feature quickly.

[@levelsio @levelsio]

Meta open sourcing their AI models that are now as or more powerful than everyone else

Will be studied in future business cases as a genius chess move to instantly wipe out the rising competition of AI startups like Open AI and avoid them becoming serious competitors to Meta

[NO CONTEXT HUMANS @HumansNoContext]

this photo is crazy.

[BUILD OR DIE @BUILD_OR_DIE]

Sam Altman: Almost everyone I’ve ever met would be well-served by spending more time thinking about what to focus on.

It is much more important to work on the right thing than it is to work many hours.

[Vala Afshar @ValaAfshar]

Nvidia CEO: people with really high expectations have very low resilience.

Success in life requires a high degree of resilience, grit, hard work and optimism.

[Deedy @deedydas]

Most people don't realize how many young people are extremely addicted to CharacterAI.

Users go crazy in the Reddit when servers go down. They get 250M+ visits/mo and ~20M monthly users, largely in the US.

Most impressively, they see ~2B queries a day, 20% of Google Search!!