1. One of the best changes in recent years is the GOP abandoning libertarianism. Here's GOP Rep. Greg Steube: “I do think there is an appetite amongst Republicans, if the Dems wanted to try to break up Big Tech, I think there is support for that."
Republican North Dakota legislators have introduced #SB2333, a bill that prohibits large tech companies from locking their users into a single app store or payment processor.https://t.co/PgyhgOhFAl
— Cory Doctorow #BLM (@doctorow) February 11, 2021
1/ pic.twitter.com/KZ8BMFQoPO
@FTCPhillips and @CSWilsonFTC are both pro-monopoly. Both voted *against* the antitrust case on FB. That case was 3-2, with a GOP Chair and 2 Dems teaming up against 2 Rs.
More from Tech
The first area to focus on is diversity. This has become a dogma in the tech world, and despite the fact that tech is one of the most meritocratic industries in the world, there are constant efforts to promote diversity at the expense of fairness, merit and competency. Examples:
USC's Interactive Media & Games Division cancels all-star panel that included top-tier game developers who were invited to share their experiences with students. Why? Because there were no women on the
ElectronConf is a conf which chooses presenters based on blind auditions; the identity, gender, and race of the speaker is not known to the selection team. The results of that merit-based approach was an all-male panel. So they cancelled the conference.
Apple's head of diversity (a black woman) got in trouble for promoting a vision of diversity that is at odds with contemporary progressive dogma. (She left the company shortly after this
Also in the name of diversity, there is unabashed discrimination against men (especially white men) in tech, in both hiring policies and in other arenas. One such example is this, a developer workshop that specifically excluded men: https://t.co/N0SkH4hR35
USC's Interactive Media & Games Division cancels all-star panel that included top-tier game developers who were invited to share their experiences with students. Why? Because there were no women on the
ElectronConf is a conf which chooses presenters based on blind auditions; the identity, gender, and race of the speaker is not known to the selection team. The results of that merit-based approach was an all-male panel. So they cancelled the conference.
Apple's head of diversity (a black woman) got in trouble for promoting a vision of diversity that is at odds with contemporary progressive dogma. (She left the company shortly after this
Also in the name of diversity, there is unabashed discrimination against men (especially white men) in tech, in both hiring policies and in other arenas. One such example is this, a developer workshop that specifically excluded men: https://t.co/N0SkH4hR35
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!
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Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇
It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details): https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha
I've read it so you needn't!
Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.
The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.
Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.
It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details): https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha
I've read it so you needn't!
Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.
The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.
Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.
