Scaling tokenization means investing in infrastructure. Normally, those costs are extracted as pure fees. But what if infrastructure growth flowed directly to token holders? That's the $TKFG flywheel.
1/7 🧵
Unpopular hot take: Yes, HFT is good for the world by improving liquidity/price discovery/allocation, but we should ban it anyway, because the benefits are too marginal to be worth the sheer number of geniuses that spend their whole career in finance.
@beylin Hey Mark, interesting article and I would love to jam an idea with you - even bought premium just now to DM you but it’s still not working. Hit me up pls! ☀️
Just minted my Mode Sentinel NFT only available on @Intractcampaign & unlocked the door to bonus XPs and exclusive perks on @modenetwork
Join Mode Onchain Takeover 👉 https://t.co/n4ASlzhWny
Our "Deciphering BCR" campaign is now live on Galxe!✨
Understand the intricacies of Taiko’s Based Contestable Rollup (BCR) design and test your knowledge! 🧠
Challenge yourself with our quiz, explore our ecosystem partner showcase, and earn points! 🎁
Quick, Puffy needs you!
Carrots have invaded our Ethereum waters, posing a threat to its decentralization. But no sweat – Puffy’s got this, munching on those carrots like a champ! 🐡🥕
Yes, Puffy's Crunchy Carrot Quest starts... TODAY!
Find out more here👇
1/ We're kicking off a recap of the Web3 Transitions Summit which we co-hosted with @Safe, starting with @VitalikButerin's keynote on The Path Forward.
Watch Now 📺 https://t.co/4mjf7qFxkF
One element to keep solo-staking accessible is by providing convenient and user-friendly testing options.
Ephemery, a testnet that resets automatically at regular intervals, serves as a promising solution to this challenge.
https://t.co/yrpFqIumtY
Does GPT understand the world?
Here is what @ilyasut, co-founder of OpenAI, says during a discussion with Jensen Huang, CEO of Nvidia:
(1) When we train a large neural network to accurately predict the next word in lots of different texts from the internet, the AI is learning a world model.
(2) On the surface, it may look like learning correlations in text, but it turns out that to 'just learn' statistical correlations in text, to compress information really well, what the neural network learns is some representation of the process that produced the text.
(3) This text is a projection of the world...what the neural network is learning is aspects of the world, of people, of the human conditions, their hopes, dreams, motivations, their interactions...the situations we are in. The neural network learns a compressed, abstract, usable representation."
Do you think learning representations = understanding?
Are large language models simply stochastic parrots, or are they much more?
🧵Thrilled to declare the milestone achievement of deploying the **FIRST** Security Token, compliant with the German Electronics Security Law (“#eWpG") on the #Avalanche Mainnet. Celebrating our client, e-Sec, an active #crypto security registrar pioneering this space in Germany