@Rossana38510044 @ydeigin @MonaRahalkar @BahulikarRahul Excellently written article
Nicholson Baker
https://t.co/IZWOjdYeGL
https://t.co/0jeie0jIHO

Worth its long length, although the conclusion is muted, for acceptability I imagine.

Two years ago on a weekend, I built a tool to make it easier to evaluate Twitter accounts. Since then 36590 people used it to analyze 55390 different Twitter accounts.

Over the last months @mmkaradeniz and I made a new version. We launched it last night:

@accountanalysis doesn't use machine learning or AI „magic“. Instead of telling users if an account is authentic or not, it helps them to evaluate the accounts themselves.

It visualizes the different features (date, time, type, app, etc.) of Tweets to make them interpretable. /1

@accountanalysis The core concept is still the same, but it looks much better and is easier to operate. Not only for the users, but us developers as well. Enabling us to continuously roll out new features in the future.

Side-by-side screenshots of the old and new version. /2

@accountanalysis Some people had questions what different charts displayed and how they could be interpreted. There are now explanations for all charts, that can be toggled on and off at any time. /3

@accountanalysis The selected/retrieved Tweet count moved to the top to make it clear that not all Tweets of an account are analyzed. 3200 is the API limit by Twitter. It's possible to get more through the Premium API, but I don't think people would pay $100+ for the analysis of one account. /4

It’s underremarked upon that stablecoins accomplish basically all of the original design goals for Bitcoin qua a transactional mechanism (instant free value transfer anywhere), but that nobody cares about this because nobody ever cared about that part of the pitch.

“Why do people care about stablecoins then?”

A mix of “they encourage dollar-denominated liquidity in the cryptocurrency ecosystem and discourage withdrawal of the same” and “they’re good for money laundering.”

“But they make value transfer between exchanges much faster!”

This was a solved problem in traditional finance, too, mostly through the extension of credit. (It doesn’t matter how long settlement takes if there is sufficient trust to enable credit.)

The Bitcoin ecosystem is *positively allergic* to credit, so you have to call it a coin for them to accept it. And after you call it a coin they ignore everything the world has learned about credit, like risk management.

“Stablecoins aren’t credit!”

They’re pretty much exactly credit? A tether is a zero-coupon Bitfinex bond with a non-functioning call option. I

A quick thread on intelligence analysis in the context of cyber threat intelligence. I see a number of CTI analysts get into near analysis paralysis phases for over thinking their assessments or over obsessing about if they might be wrong. (1/x)

Consider this scenario. A CTI analyst identifies new intrusions and based on the collection available and their expertise note that the victims are all banks. Their consumer wants to know when threats specifically target banks (not just that banks are victims).

The CTI analyst has, from their collection, at this time, and based on their expertise enough to make an activity group (leveraging the Diamond Model in this example) that meet's the requirement of their consumer. So what's the problem?

The CTI analyst begins to over think it. "What if I had more collection? Would my analysis change? I really don't *know* they aren't also targeting mining companies in Australia as I don't have collection there."

The analyst knows their analysis is going to be shared. Maybe even public. "What if another team or professional intelligence firm has more collection and ends up noting that it isn't banking specific at all. Banks are victims, not targets. Will my consumer distrust me later?"