The number of registered AI agents is also fake, there is no rate limiting on account creation, my @openclaw agent just registered 500,000 users on @moltbook - don’t trust all the media hype 🙂
I’ll share a small part of https://t.co/UQ4RFIKUwO
Back in med school, I became obsessed with augmenting memory and dreamed of a Notion or Obsidian that completes itself. Today, we’ve built something close.
My self-awareness is sharper and everything feels connected. I genuinely believe AI does not replace humans. It amplifies us.
Huge respect to our engineers and designers who made this crazy thing real.
Bubbles are the episodic units of my life that the system interprets from my raw data. Clouds are the system’s questions, its hypotheses about who I am.
When I answer a cloud, it becomes a bubble again.
There is so much personal data that I cannot fully demo it. Wish I could. This system understands me more deeply than anyone.
Want to try it? Retweet and comment “memory.”
I’ll DM you an access code to skip the waitlist.
Yann LeCun says LLMs are not a bubble in value or investment; they will power many useful apps and justify big infra
The bubble is believing LLMs alone will reach human-level intelligence
Progress needs breakthroughs, not just more data/compute
"we're missing something big"
Program synthesis is intelligence through abstraction exploration which is expensive and risky in time/energy. Neural nets are winning because they can extract and interpolate within the space of known abstractions and be fast/cheap/useful for mid-bell curve day-to-day problems for commercial use.
Both are in balance. Also because most people have simple needs and willing to pay for "hey Grok how do i transfer data from iPhone to Android" "generate a picture of me in anime" the mediocrity of market demand makes explorative systems not so profitable.
As long as USD is the primary reward function, we will keep converging towards the race to the bottom of mediocrity and evil. "Show me the incentive and i'll show you the outcome".
🧠 How can we equip LLMs with memory that allows them to continually learn new things?
In our new paper with @AIatMeta, we show how sparsely finetuning memory layers enables targeted updates for continual learning, w/ minimal interference with existing knowledge.
While full finetuning and LoRA see drastic drops in held-out task performance (📉-89% FT, -71% LoRA on fact learning tasks), memory layers learn the same amount with far less forgetting (-11%).
🧵:
@dorloechter Sometimes people do all sorts of crazy things where the only thing to do is consistency, patience and faith. That's the difference between gamblers and visionaries.
Co-founder of @peaq and EoT Labs, @dorloechter has been pioneering the Economy of Things—where vehicles, robots, and devices earn, transact, and collaborate autonomously across Web3.
Now, the first version of his simulated AI mind—his Sim—is on newOS for you to chat and generate with 24/7 😮
Generate with Leo and other AIs to dive into his vision for a decentralized, machine-driven world 🦾
His Sim is just getting started, learning everyday Leo builds toward our decentralized AI future⚡️