🇭🇰 Researchers at the University of Hong Kong have created what they say is the world's first soft, 3D semiconductor made from hydrogel.
Unlike traditional semiconductors, it behaves more like human tissue and can interact with living cells.
That's a pretty wild step forward.
Tom Lee believes yesterday’s selloff is NOT the beginning of the correction expected later this year.
But he still believes a correction is coming.
His argument:
• Markets had a parabolic move over the last month, so expectations have become much higher.
• SpaceX raising $75B through its IPO, combined with another $75B from $GOOGL issuing new shares, is a massive amount of capital for public markets to absorb.
When markets wobble, investors raise cash.
This may be less about panic and more about liquidity.
Google has published a paper that might end the transformer era.
For the last 7 years, every major AI, ChatGPT, Claude, Gemini, has been built on the exact same architecture: The Transformer.
But Transformers have a fatal flaw.
To remember context, they have to process every single word against every other word. It’s called quadratic complexity. As your prompt gets longer, the compute cost explodes.
The alternative is the old-school RNN (Recurrent Neural Network). RNNs are incredibly cheap and fast, but they have a fixed memory size. If you give them a long document, they get amnesia.
Until today.
Google researchers published Memory Caching: RNNs with Growing Memory.
And it fixes the biggest bottleneck in AI.
Instead of an RNN having a fixed, rigid memory that constantly overwrites itself, Google gave it a "save" button.
The technique allows the RNN to cache checkpoints of its hidden states as it reads.
The memory capacity of the RNN can now dynamically grow as the sequence gets longer.
They built four different variants, including sparse selective mechanisms where the AI actively chooses exactly which checkpoints matter most.
The results rewrite the rules of efficiency.
On long-context understanding and recall-intensive tasks, these new Memory-Cached RNNs closed the gap with Transformers.
They achieved competitive accuracy without the explosive, quadratic compute cost. It perfectly bridges the gap between the cheap efficiency of an RNN and the massive capability of a Transformer.
We have spent billions scaling Transformers because we thought they were the only way an AI could remember a long conversation.
But Google just proved we don't need to process the whole history every single time.
We just needed a smarter cache.
Google $GOOG just released an AI tool called TurboVec that shrinks memory requirements by 92%
Now we know why the collapse began yesterday.
Goodbye Micron $MU, Sandisk $SNDK, Samsung and SK Hynix
I just completed three Microsoft AI Agent modules:
• Memory, State, and Evaluation
• Multi-Agent Systems and Orchestration
• Governance, Guardrails, and Operations
One thing stood out.
We've spent years talking about prompts.
The real challenge is systems.
🧵
$BTC will need to more than double here to get back to ATH’s.
$ETH will need to more than triple here to get back to ATH’s.
Did the institutionalization of crypto spell the actual doom of it?