We aren't doing this near enough.
Some of the things I've learned in more than 20 years in the tech industry.
You need to hear these.
🧵👇
We aren't doing this near enough.
No small improvement is too small.
Just aim for something new every day, and you'll be surprised at the end.
Be the person that pulls everyone out of the rabbit holes.
Great results will get you farther than processes, but good processes can help you achieve good results.
It's funny how everything you share finds a way to reward you back.
We all make mistakes. Move on from them and focus on what's coming.
Ask away!
(There are, however, stupid people with fragile egos that get bothered when others ask. Ignore them.)
Embrace change.
People fantasize about perfection, but perfectionism rarely wins.
Shipping more often will give you better odds than gilding the lily.
What you know today will be outdated tomorrow.
Make a plan to keep up and follow it... or you'll get behind.
(And it looks horrible in your resume.)
More from Santiago
You gotta think about this one carefully!
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
Really doesn\u2019t fit well in a tweet. pic.twitter.com/xN0pAyniFS
— Dr. Lena Sugar \U0001f3f3\ufe0f\u200d\U0001f308\U0001f1ea\U0001f1fa\U0001f1ef\U0001f1f5 (@_jvs) February 18, 2021
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
10 machine learning YouTube videos.
On libraries, algorithms, and tools.
(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)
🧵👇
1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.
Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.
https://t.co/HqE9yt8TkB
2⃣ The Pandas library is the gold-standard to manipulate structured data.
Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."
https://t.co/aOLh0dcGF5
3⃣ Data visualization is key for anyone practicing machine learning.
Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.
https://t.co/QxjsODI1HB
4⃣ Another trendy data visualization library is Seaborn.
@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.
https://t.co/eAU5NBucbm
On libraries, algorithms, and tools.
(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)
🧵👇
1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.
Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.
https://t.co/HqE9yt8TkB
2⃣ The Pandas library is the gold-standard to manipulate structured data.
Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."
https://t.co/aOLh0dcGF5
3⃣ Data visualization is key for anyone practicing machine learning.
Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.
https://t.co/QxjsODI1HB
4⃣ Another trendy data visualization library is Seaborn.
@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.
https://t.co/eAU5NBucbm
More from Tech
A brief analysis and comparison of the CSS for Twitter's PWA vs Twitter's legacy desktop website. The difference is dramatic and I'll touch on some reasons why.
Legacy site *downloads* ~630 KB CSS per theme and writing direction.
6,769 rules
9,252 selectors
16.7k declarations
3,370 unique declarations
44 media queries
36 unique colors
50 unique background colors
46 unique font sizes
39 unique z-indices
https://t.co/qyl4Bt1i5x
PWA *incrementally generates* ~30 KB CSS that handles all themes and writing directions.
735 rules
740 selectors
757 declarations
730 unique declarations
0 media queries
11 unique colors
32 unique background colors
15 unique font sizes
7 unique z-indices
https://t.co/w7oNG5KUkJ
The legacy site's CSS is what happens when hundreds of people directly write CSS over many years. Specificity wars, redundancy, a house of cards that can't be fixed. The result is extremely inefficient and error-prone styling that punishes users and developers.
The PWA's CSS is generated on-demand by a JS framework that manages styles and outputs "atomic CSS". The framework can enforce strict constraints and perform optimisations, which is why the CSS is so much smaller and safer. Style conflicts and unbounded CSS growth are avoided.
Legacy site *downloads* ~630 KB CSS per theme and writing direction.
6,769 rules
9,252 selectors
16.7k declarations
3,370 unique declarations
44 media queries
36 unique colors
50 unique background colors
46 unique font sizes
39 unique z-indices
https://t.co/qyl4Bt1i5x
PWA *incrementally generates* ~30 KB CSS that handles all themes and writing directions.
735 rules
740 selectors
757 declarations
730 unique declarations
0 media queries
11 unique colors
32 unique background colors
15 unique font sizes
7 unique z-indices
https://t.co/w7oNG5KUkJ
The legacy site's CSS is what happens when hundreds of people directly write CSS over many years. Specificity wars, redundancy, a house of cards that can't be fixed. The result is extremely inefficient and error-prone styling that punishes users and developers.
The PWA's CSS is generated on-demand by a JS framework that manages styles and outputs "atomic CSS". The framework can enforce strict constraints and perform optimisations, which is why the CSS is so much smaller and safer. Style conflicts and unbounded CSS growth are avoided.
What an amazing presentation! Loved how @ravidharamshi77 brilliantly started off with global macros & capital markets, and then gradually migrated to Indian equities, summing up his thesis for a bull market case!
@MadhusudanKela @VQIndia @sameervq
My key learnings: ⬇️⬇️⬇️
First, the BEAR case:
1. Bitcoin has surpassed all the bubbles of the last 45 years in extent that includes Gold, Nikkei, dotcom bubble.
2. Cyclically adjusted PE ratio for S&P 500 almost at 1929 (The Great Depression) peaks, at highest levels except the dotcom crisis in 2000.
3. World market cap to GDP ratio presently at 124% vs last 5 years average of 92% & last 10 years average of 85%.
US market cap to GDP nearing 200%.
4. Bitcoin (as an asset class) has moved to the 3rd place in terms of price gains in preceding 3 years before peak (900%); 1st was Tulip bubble in 17th century (rising 2200%).
@MadhusudanKela @VQIndia @sameervq
My key learnings: ⬇️⬇️⬇️
Bubble or Bull Market? Join us for a short presentation and candid one on one on 27th Jan, 4pm with Shri \u2066@MadhusudanKela\u2069. \u2066@VQIndia\u2069 \u2066@sameervq\u2069 #bubbleorbullmarket pic.twitter.com/LBvlBrz6mS
— Ravi Dharamshi (@ravidharamshi77) January 24, 2021
First, the BEAR case:
1. Bitcoin has surpassed all the bubbles of the last 45 years in extent that includes Gold, Nikkei, dotcom bubble.
2. Cyclically adjusted PE ratio for S&P 500 almost at 1929 (The Great Depression) peaks, at highest levels except the dotcom crisis in 2000.
3. World market cap to GDP ratio presently at 124% vs last 5 years average of 92% & last 10 years average of 85%.
US market cap to GDP nearing 200%.
4. Bitcoin (as an asset class) has moved to the 3rd place in terms of price gains in preceding 3 years before peak (900%); 1st was Tulip bubble in 17th century (rising 2200%).
