Crypt⨀winter| Zetarium

If you still think quantum computing isn’t a real threat to digital security, read this. 🚨 We live in a hyper connected world. Smartphones, watches, even our homes are online. But are they really protected/safe? What if a global threat was quietly putting our devices, assets — and even our safety — at risk? What if we could leverage such a huge number of online devices(50B+) to create a decentralized trust mesh where each device will act as a validator incentivized to protect the entire network? We are about to enter a new era where our current encryption systems can't survive, and @NaorisProtocol is making it post-quantum secure Here is how👇 #quantum #BlockchainSecurity

Always! BTW what can I dyor on currently?

@BinanceDesi Wow this is beautiful!

Advice so valuable, you will be passing it on to your friends! Tag your buddies 👇

This really need to be considered Most people just see the bad side from the region, they do forget all the good. Though we all know where the real bad actors are from

@daveontheway @monad Nice one

@Cryptowinter39 @monad Just in

me following back all nads on @Monad just 8 hours after joining the ecosystem.

@moonpay Good morning MoonPay baby🟣

gm!

@pareen It’s always sweet when it’s new!

testing the mutuals feature if you can reply i might be following you

@moonpay 🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞🤞

winners will be picked at 6 PM ET on Friday and contacted directly by the MoonPay account full rules: live.moonpay.com/btc-eth-sol-xr… 🫡🫡🫡

to celebrate the launch of OWS, this week's @nelkboys giveaway will feature: 💰 $250 of Bitcoin 💰 $250 of Ethereum 💰 $250 of Solana 💰 $250 of XRP to enter, just like + RT this post and reply "OWS" 4 winners will be selected on Friday, March 27!

@moonpay @orangie Yeah me too I want that

@orangie I’d like a longer tutorial

i created an ai agent that trades memecoins and he's up $1300 in the last week. i used @moonpay cli to set it up; and some additional tips i mention in here if you'd like a longer tutorial, please drop some support :)

@orangie @moonpay Please share a longer tutorial

@monad Monad!

Monad!

@Binance_intern Gm intern

GM. Let's keep it simple today.

@moonpay Because MoonPay is for everyone 🟣💜

the answer is always everyone

@Ruffyking001 @PerleLabs Thanks fam

@Cryptowinter39 @PerleLabs Well written

Most people focus on the speed at which AI models operates, but what should matter most is the data that feeds the AI systems. And not just any data, but expert-level data. Here’s why @PerleLabs is building a new kind of economy 🧵 #PerleAI #ToPerle

Perle Labs is trying to shift AI from: Cheap data → Trusted data Crowd work → Expert economy Opaque pipelines → Sovereign infrastructure If successful, this model could become the rofessional layer of AI data. That’s why the socialized expert economy of Perle is interesting: It doesn’t just build AI data, it builds an expert-driven AI labor market. — Participating in @PerleLabs community campaign.

No system is perfect. Here are some realistic risks: 1. Expert Supply Problem Recruiting real experts at scale is difficult. Challenges: • Doctors and lawyers are busy • High-quality experts are expensive • Scaling globally is hard It's all well and good that experts in specific fields should contribute their knowledge to train AI models in order to improve the quality of training data, but given that these AI models threaten some people's livelihoods, do you think these experts will be willing to contribute to improving the quality of the data needed to train the models? Also, these experts are generally highly paid: doctors are known for their substantial salaries, especially specialists, and some lawyers also have quite high fees, etc. Are the compensation and incentive systems sufficient to motivate these experts to contribute? 2. Cost vs Competitors Expert-driven datasets are: • Higher quality • But more expensive Some companies may still choose cheaper alternatives 3. Reputation system manipulation Possible risks: • Gaming the reputation system • Coordination between contributors • Hidden bias in evaluation This must be carefully managed. 4. Slow data production Expert validation takes longer than crowd-sourced labeling. Some AI teams may prioritize speed.