EntroPi

..

[Zephyr @zephyr_z9]

Samsung will hit $3T+ imo

[Jason's Chips @jasonschips]

https://t.co/OfYdTHQqyP

[Damnang2 @damnang2]

https://t.co/IRufRx3c0b

[Damnang2 @damnang2]

This article connects the May earnings from Lumentum, Coherent, and Marvell into one AI optical interconnect thesis.

The key point is not just Marvell’s earnings beat. It is that the laser, transceiver, DSP, switch, and silicon photonics layers are all confirming the same demand cycle.

@PhotonCap lays out one of the clearest supply chain maps on how NVIDIA is shaping the optical interconnect ecosystem.

If you are following AI networking, CPO, silicon photonics, or Marvell, this is a must read.

[Gaetano @crux_capital_]

$NOK This is a must read!

[Chris Barber @chrisbarber]

https://t.co/Z5V4Li4QFE

[Anatoli Kopadze @AnatoliKopadze]

Godfather of AI: "If you sleep well tonight, you may not have understood this lecture."

This 47-minute lecture is the best thing I saw about AI in the last few months.

It will definitely help you understand how it actually works and where it's going.

Geoffrey Hinton built the neural networks behind every AI alive, then quit Google to warn the world about it.

The part nobody wanted to hear:

> AI is already developing abilities its creators didn't intend
> in most cognitive tasks it's already ahead of us
> the question is no longer if it surpasses us but when
> the only decision left is which side of that line you're on

Right now the average person opens Claude, types something, gets an answer, closes the tab.

They think they're using AI. they're using maybe 10% of it.

I went through his entire lecture, built a practical system from what he was describing.

18 steps to actually use Claude the right way, with copy-paste prompts that work today.

Full guide in the post below.

[Brett Adcock @adcock_brett]

Watch a team of humanoid robots running a full 8-hr shift at human performance levels. This is fully autonomous running Helix-02 https://t.co/IdZR0T1F5I

[Nutty @NuttyCLD]

AI servers are not just GPUs.

As GPU power delivery changes, the small components around the GPU are starting to be valued by a very different standard.

But the interesting part is this:

Even within AI server components, the market is not pricing everything the same way.

Some companies are already showing AI demand in reported numbers. Others are still mostly trading on products, positioning, and expectations.

This piece is about why that gap exists, and why treating “AI components” as one simple basket can miss what is actually happening underneath.

The piece also links back to my free MLCC explainer, so the two are worth reading together if you want both the technical foundation and the market map.

[Damnang2 @damnang2]

https://t.co/HazyuQg2F3

[Damnang2 @damnang2]

@EndicottInvests This is for you
https://t.co/dR6Z4AcKcs

[Damnang2 @damnang2]

This article is an overview for investors who are starting to think about the AI data center power infrastructure theme.

Rather than treating power as a simple market theme, I break down how electricity moves through the data center stack, what technical changes are happening at each stage, where the key bottlenecks may emerge, and how different companies are positioned across the value chain.

I also evaluate the companies using five key factors and share my view on which names may become more attractive under different scenarios.

The purpose of this article is not to make buy or sell recommendations. It is meant to provide a framework for thinking about the AI data center power stack and help readers build their own investment strategy in search of alpha.

Full article: https://t.co/utaMqj3DIF

[Damnang2 @damnang2]

https://t.co/nOvzfw3Mth

[Damnang2 @damnang2]

I hope this article has been very helpful for your investing.

[Damnang2 @damnang2]

People really need to read this before earnings...

[Nutty @NuttyCLD]

https://t.co/qQxAig9EWk

[Nutty @NuttyCLD]

AI did not just create demand for more GPUs.

It changed what “cloud” is supposed to do.

The old cloud was built to divide resources across many workloads. AI training does the reverse: it stitches thousands of GPUs into one giant job.

That gap is where neoclouds entered.

Full article:
https://t.co/41WumIiL5h

[Nutty @NuttyCLD]

https://t.co/2TIUIO46gu

[Nutty @NuttyCLD]

Optics remains the core sector driving AI infrastructure, but the days of simply chasing keywords are over. A deep understanding of the specific technological 'chokepoints' each company controls within the value chain is now absolutely essential.

@damnang2 ’s latest piece sharply analyzes the extreme price decoupling among 22 key optical companies and pinpoints the next M&A targets. As the market aggressively separates the signal from the noise, this dense, highly analytical breakdown serves as an excellent compass for anyone looking to reposition their portfolio.

[Nutty @NuttyCLD]

While the market fixates on flashy GPU specs and front-end node shrinks, the real battleground for the next AI hardware cycle has shifted to the very end of the line: Assembly. In the era of 16-Hi HBM4, chiplets, and CPO, ecosystem dominance now depends entirely on how precisely we can stack and bond different dies at the atomic level.

Moving beyond superficial ticker lists, @PhotonCap's latest piece isolates the 7 global bonding equipment companies standing at the center of this hidden warzone. From the massive M&A scenarios poised to disrupt the value chain to identifying the purest beneficiaries of the hybrid bonding cycle, this article breaks it all down.