The recent "counter-hack" that recovered $225M of stolen tokens sets a great example for the crypto world! ๐
This case demonstrates that just because you use crypto tokens doesn't mean that you have a decentralized solution.
Read on to know why.
https://t.co/9pLSylQHN5
Cohort 13 of the "Building Machine Learning Systems That Don't Suck" program starts tomorrow.
There's no better place to learn how to build Machine Learning systems that work in the real world.
It's a live class. It's tough. It's a ton of fun.
https://t.co/UFauMuMKBY
Wow; this is really bad.
This is an easy way to steal money from anyone. Another vector that Apple should check for esp for crypto wallets.
Download your wallets from the official source.
PSA: The Leather Wallet app currently in the iOS store is FAKE ๐จ
โ ๏ธ Do not download it, and definitely do not input your seed phrase.
We promise we'll let you know once our mobile app is actually ready!
Leather should only be downloaded directly from https://t.co/V9zpQR40uC.
We need to improve the capital efficiency in lending markets.
Undercollaterized lending needs to step up since it is not as prevalent as it needs to be
@WildcatFi doing a great job at filling the gap here.
We've moved onto the podium in the undercollateralised lending category on DefiLlama!
With US$25.6 million in TVL, @WildcatFi is now the third largest credit facilitator on any chain.
Not bad for something three months old!
A very interesting panel @EthereumDenver if you are interested in DeFi lending
@euler_mab and @functi0nZer0 talk about two different approaches to DeFi lending - overcollaterized and undercollaterized
listen to the recording to get interesting nuggets about lending,risk mgt etc
So Bitcoin spot ETF has been approved.
You might be wondering how to react?
If so, check this information rich thread by @CryptoGirlNova - high quality opinion like always from her.
I look at that slightly differently to keep the inputs similar.
So in one case the algorithm figures out the patterns in historical data to create models while in another humans figure out the patterns to create models.
Then the model is deployed to make decisions/predictions.
The attached picture is the simplest explanation of Machine Learning I've seen.
Programmers write rules and apply them to data to produce answers. But if those rules aren't obvious, they are stuck.
With Machine Learning, we don't write the rules; we learn them from data. That's a superpower!
Think about how we learn:
We look at things, discover patterns, and then generalize those ideas whenever we see something similar.
That's the same principle behind Machine Learning: we use existing data to infer the answer to similar, future samples.
It's 2024. The best time to start learning Machine Learning is now!
(The picture is from @fchollet's Deep Learning with Python book. It's a genius representation of the differences between the two fields.)
Here's the story of another technology that faced massive backlash in its time that will sound very familiar to today's battles over #AI.
Coffee.
a thread.
@svpino@MalikAarif1430 I joined this a while back; it is worth every $ that you pay
Yes, i have completed only half of the main course since life gets in the way but I plan to join a cohort in the future to finish it for free
btw i have leveraged my learnings in my projects (don't tell @svpino๐)
Yes I found this aspect of hauling snow fascinating to watch; I have lived in several places where it snows but never in a place where snow is hauled off
How many rollups will there be?
I donโt know, but a helpful analogy that Iโve found is TV channels ๐บ
Clearly having all TV shows on a single channel wonโt work. Thereโs just too much programming.
But equally clearly it wouldnโt make sense for every TV show to have its own channel. Weโd end up with channels that had no programming for most of the day. And each one would have to find its own advertisers, distributors etc. And discovery of new programs would be a nightmare.
The system we have today is a natural one โ channels are loosely organized by genres. If you like cartoons, you can watch the Cartoon Network. If you like nature, you can watch the National Geographic channel. And itโs not always an exact science โ cartoons about nature could go either way.
I envision a similar world emerging for blockchains. Each app wonโt have its own network, but likeminded apps that appeal to common users will form their own networks. Weโre already seeing this with DeFi and gaming but I imagine that many more verticals will emerge over time. And it makes sense. The network can be tailored to those apps and users will be able to easily engage with and discover similar apps with no friction.
So will we ultimately have a dozen networks? I think so. Will we have millions? I think not. How many exactly will we have? Only time will tell.
In case you are wondering why the jump in crypto prices today.
And this is good for the crypto space in the long run too
Since this can lead to better regulation as opposed to ad-hoc "mood based" regulation.
1/ Grayscale's victory over the SEC is *massive.*
It's very rare for a federal circuit court to find that an agency has violated the APA by acting arbitrarily and capriciously.
The DC Circuit just delivered a huge embarrassment for the SEC.
But the ETF isn't approved yet ๐งต
1/ Grayscale's victory over the SEC is *massive.*
It's very rare for a federal circuit court to find that an agency has violated the APA by acting arbitrarily and capriciously.
The DC Circuit just delivered a huge embarrassment for the SEC.
But the ETF isn't approved yet ๐งต
Soon we are going to start enforcing our final Stages requirement - the existence of an open-source node that can recreate the state from L1 data.
We'll be contacting projects and updating the Stage designation for systems that do not meet the requirement.
Thread for context ๐งต