Observing the public conversation around FB, and the private ones happening among techies and ex-FBers, I think the mutual misunderstanding is worse than when I set out two years (and 500 pages) ago to (in a small way) bridge that gulf.
We're basically fucked.
Techies take weird, improbable visions, and make them realities: some BS pitch deck to a VC, mixed with money and people, really does turn into some novel thing.
Facebook & Co. can take on the most egregious disinformation examples, or efforts undertaken by identifiable state actors (maybe), but it will never be able to shut it down entirely.
Why do I feel confident in this assertion (that I'm sure will get trolled)?
Where'd that end up? Nowhere. We got GDPR, which is pointless, and if anything solidified FB/GOOG's position in Europe. Ditto CCPA.
If you sat down to a meal in the 80s, and took out a camera and took a photo of your food, while telling everyone you were sending copies to your friends, you'd have been locked up in an insane asylum.
The Beacon scandal that blew up FB in the late aughts now seems like a joke. People got worked up over that?
We'll read the current disinformation coverage the same way.
It's the bridge generation (looks in mirror) that's mostly freaking out about it.
https://t.co/LqB2xNe7Cw
More from Tech
1/ 👋 Excited to share what we’ve been building at https://t.co/GOQJ7LjQ2t + we are going to tweetstorm our progress every week!
Week 1 highlights: getting shortlisted for YC W2019🤞, acquiring a premium domain💰, meeting Substack's @hamishmckenzie and Stripe CEO @patrickc 🤩
2/ So what is Brew?
brew / bru : / to make (beer, coffee etc.) / verb: begin to develop 🌱
A place for you to enjoy premium content while supporting your favorite creators. Sort of like a ‘Consumer-facing Patreon’ cc @jackconte
(we’re still working on the pitch)
3/ So, why be so transparent? Two words: launch strategy.
jk 😅 a) I loooove doing something consistently for a long period of time b) limited downside and infinite upside (feedback, accountability, reach).
cc @altimor, @pmarca
4/ https://t.co/GOQJ7LjQ2t domain 🍻
It started with a cold email. Guess what? He was using BuyMeACoffee on his blog, and was excited to hear about what we're building next. Within 2w, we signed the deal at @Escrowcom's SF office. You’re a pleasure to work with @MichaelCyger!
5/ @ycombinator's invite for the in-person interview arrived that evening. Quite a day!
Thanks @patio11 for the thoughtful feedback on our YC application, and @gabhubert for your directions on positioning the product — set the tone for our pitch!
Week 1 highlights: getting shortlisted for YC W2019🤞, acquiring a premium domain💰, meeting Substack's @hamishmckenzie and Stripe CEO @patrickc 🤩
2/ So what is Brew?
brew / bru : / to make (beer, coffee etc.) / verb: begin to develop 🌱
A place for you to enjoy premium content while supporting your favorite creators. Sort of like a ‘Consumer-facing Patreon’ cc @jackconte
(we’re still working on the pitch)
3/ So, why be so transparent? Two words: launch strategy.
jk 😅 a) I loooove doing something consistently for a long period of time b) limited downside and infinite upside (feedback, accountability, reach).
cc @altimor, @pmarca

4/ https://t.co/GOQJ7LjQ2t domain 🍻
It started with a cold email. Guess what? He was using BuyMeACoffee on his blog, and was excited to hear about what we're building next. Within 2w, we signed the deal at @Escrowcom's SF office. You’re a pleasure to work with @MichaelCyger!
5/ @ycombinator's invite for the in-person interview arrived that evening. Quite a day!
Thanks @patio11 for the thoughtful feedback on our YC application, and @gabhubert for your directions on positioning the product — set the tone for our pitch!

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!
