“Smear and harass Ms.Heard while simultaneously promoting and praising Mr Depp’s agent and attorney, Adam Waldman.” - This part is specifically important.
Deconstructing AH’s ‘bot’ list - A Thread 🤖
#JusticeForJohnnyDepp
#AmberHeardIsALiar
“Smear and harass Ms.Heard while simultaneously promoting and praising Mr Depp’s agent and attorney, Adam Waldman.” - This part is specifically important.
• No Longer exists
• No Connection at all
• P*rn accounts
• Depp Fan accounts
• Only 1 tweet with any link
• Other
• and finally @adam_waldman 🤣
#JusticeForJohnnyDepp, Justice, Johnny, Depp, Amber and Heard
I used all the most commonly used terms with this case so it would throw up anything connected
This included accounts with:
Only a few tweets, none that were relevant.
Accounts who tweet about other Johnny’s or Amber’s but not JD or AH.
A Movie update sort of account (had posted positively about Aquafish) and
Political accounts.
My eyeballs need a cleansing. That’s all I have to say about these 😬😂
All of these are basically supporter/ Fan accounts.
They tweet about things other than the case (both JD and general life.)
They’ve posted selfies/ photos of their children things like that.
They interact with other accounts frequently.
They post photos of merch, new DVDs etc.
Not every account is a typical ‘Stan’ account in this section but basically they are all very obviously human and the common theme is they all just want Justice for Johnny. No bots here.
These tweets included simple #JusticeForJohnnyDepp tweets- no malice involved,
A tweet about Pirates of the Caribbean,
A tweet defending AH
and accounts with one singular RT.
Coincidental? 🤫😂
Basically the other random accounts I found these included:
Private accounts (she wouldn’t see their tweets unless she followed.)
Accounts with minimal tweets but a few of those relating to the case (most I found on one account was 3 tweets.)
And one account who tweeted about Lily-Rose once in 2012... 😅
There’s no comment for this really other than the fact he is very clearly human and not bot 😂
Not one of those 209 accounts were used to “Smear and harass Ms.Heard while simultaneously promoting and praising Mr Depp’s agent and attorney, Adam Waldman.” And it wasn’t hard to disprove either.
More from Tech
On press call, Zuckerberg says FB users "naturally engage more with sensational content" that comes close to violating its rules. Compares it to cable TV and tabloids, and says, "This seems to be true regardless of where we set our policy lines."
Zuckerberg says FB is in the process of setting up a "new independent body" that users will be able to appeal content takedowns to. Sort of like the "Facebook Supreme Court" idea he previewed earlier this year.
Zuckerberg: "One of my biggest lessons from this year is that when you connect more than 2 billion people, you’re going to see the good and bad of humanity."
This is how Facebook says it's trying to change the engagement pattern on its services. https://t.co/3p0PGc912o
.@RebeccaJarvis asks Zuckerberg if anyone is going to lose their job over the revelations in the NYT story. He dodges, says that personnel issues aren't a public matter, and that employee performance is evaluated all the time.
Zuckerberg says FB is in the process of setting up a "new independent body" that users will be able to appeal content takedowns to. Sort of like the "Facebook Supreme Court" idea he previewed earlier this year.
Zuckerberg: "One of my biggest lessons from this year is that when you connect more than 2 billion people, you’re going to see the good and bad of humanity."
This is how Facebook says it's trying to change the engagement pattern on its services. https://t.co/3p0PGc912o
.@RebeccaJarvis asks Zuckerberg if anyone is going to lose their job over the revelations in the NYT story. He dodges, says that personnel issues aren't a public matter, and that employee performance is evaluated all the time.
The 12 most important pieces of information and concepts I wish I knew about equity, as a software engineer.
A thread.
1. Equity is something Big Tech and high-growth companies award to software engineers at all levels. The more senior you are, the bigger the ratio can be:
2. Vesting, cliffs, refreshers, and sign-on clawbacks.
If you get awarded equity, you'll want to understand vesting and cliffs. A 1-year cliff is pretty common in most places that award equity.
Read more in this blog post I wrote: https://t.co/WxQ9pQh2mY
3. Stock options / ESOPs.
The most common form of equity compensation at early-stage startups that are high-growth.
And there are *so* many pitfalls you'll want to be aware of. You need to do your research on this: I can't do justice in a tweet.
https://t.co/cudLn3ngqi
4. RSUs (Restricted Stock Units)
A common form of equity compensation for publicly traded companies and Big Tech. One of the easier types of equity to understand: https://t.co/a5xU1H9IHP
5. Double-trigger RSUs. Typically RSUs for pre-IPO companies. I got these at Uber.
6. ESPP: a (typically) amazing employee perk at publicly traded companies. There's always risk, but this plan can typically offer good upsides.
7. Phantom shares. An interesting setup similar to RSUs... but you don't own stocks. Not frequent, but e.g. Adyen goes with this plan.
A thread.
1. Equity is something Big Tech and high-growth companies award to software engineers at all levels. The more senior you are, the bigger the ratio can be:
2. Vesting, cliffs, refreshers, and sign-on clawbacks.
If you get awarded equity, you'll want to understand vesting and cliffs. A 1-year cliff is pretty common in most places that award equity.
Read more in this blog post I wrote: https://t.co/WxQ9pQh2mY
3. Stock options / ESOPs.
The most common form of equity compensation at early-stage startups that are high-growth.
And there are *so* many pitfalls you'll want to be aware of. You need to do your research on this: I can't do justice in a tweet.
https://t.co/cudLn3ngqi
4. RSUs (Restricted Stock Units)
A common form of equity compensation for publicly traded companies and Big Tech. One of the easier types of equity to understand: https://t.co/a5xU1H9IHP
5. Double-trigger RSUs. Typically RSUs for pre-IPO companies. I got these at Uber.
6. ESPP: a (typically) amazing employee perk at publicly traded companies. There's always risk, but this plan can typically offer good upsides.
7. Phantom shares. An interesting setup similar to RSUs... but you don't own stocks. Not frequent, but e.g. Adyen goes with this plan.
THREAD: How is it possible to train a well-performing, advanced Computer Vision model 𝗼𝗻 𝘁𝗵𝗲 𝗖𝗣𝗨? 🤔
At the heart of this lies the most important technique in modern deep learning - transfer learning.
Let's analyze how it
2/ For starters, let's look at what a neural network (NN for short) does.
An NN is like a stack of pancakes, with computation flowing up when we make predictions.
How does it all work?
3/ We show an image to our model.
An image is a collection of pixels. Each pixel is just a bunch of numbers describing its color.
Here is what it might look like for a black and white image
4/ The picture goes into the layer at the bottom.
Each layer performs computation on the image, transforming it and passing it upwards.
5/ By the time the image reaches the uppermost layer, it has been transformed to the point that it now consists of two numbers only.
The outputs of a layer are called activations, and the outputs of the last layer have a special meaning... they are the predictions!
At the heart of this lies the most important technique in modern deep learning - transfer learning.
Let's analyze how it
THREAD: Can you start learning cutting-edge deep learning without specialized hardware? \U0001f916
— Radek Osmulski (@radekosmulski) February 11, 2021
In this thread, we will train an advanced Computer Vision model on a challenging dataset. \U0001f415\U0001f408 Training completes in 25 minutes on my 3yrs old Ryzen 5 CPU.
Let me show you how...
2/ For starters, let's look at what a neural network (NN for short) does.
An NN is like a stack of pancakes, with computation flowing up when we make predictions.
How does it all work?
3/ We show an image to our model.
An image is a collection of pixels. Each pixel is just a bunch of numbers describing its color.
Here is what it might look like for a black and white image
4/ The picture goes into the layer at the bottom.
Each layer performs computation on the image, transforming it and passing it upwards.
5/ By the time the image reaches the uppermost layer, it has been transformed to the point that it now consists of two numbers only.
The outputs of a layer are called activations, and the outputs of the last layer have a special meaning... they are the predictions!
