Przy tych danych wygląda, że właściwie nie ma większej różnicy (nie bijemy się tutaj o 0.01 punktu procentowego poprawy accuracy modelu). Może więc czas treningu jest istotny? Python sns.boxplot(data=models_df, x='time_elapsed', y='model') 1 sns.boxplot(data=models_df, x='time_elapsed', y='model')
Training time of some popular ML models. After considering the performance, it's worth using XGBoost and LightGBM.
Teraz w zagnieżdżonych pętlach możemy sprawdzić każdy z każdym podmieniając klasyfikatory i transformatory (cała pętla trochę się kręci):
Example (below) of when creating pipelines with scikit-learn makes sense. Basically, it's convenient to use it while comparing multiple models in a loop
First, you'll need to clone your own copy of the template Google Sheet by clicking here, then pressing Use Template in the top right. This will add the sample spreadsheet to your personal Google account. Once you've done that, click here to open the starter app in Autocode. Poke around the source code if you'd like, then install the app to your Autocode account by pressing the green button. When prompted to link a Google Sheet, follow the instructions in the flow to link your Google account, then select the spreadsheet you just cloned.
Solution (Autocode) to the scalability limits of Google Sheets
Queries became impractically slow around the 500,000 cell mark, but were still below 2 seconds for a 100,000 cell query. Therefore, if you anticipate a dataset larger than a few hundred thousand cells, it would probably be smart to choose a more scalable option.
Scalability of Google Sheets. They have a hard limit of 5,000,000 cells (including blank ones)
What the question does is that it creates a powerful anchoring effect. A candidate who gives this information away will typically ask for only a bit more than what she is currently getting. The human resources dude will then have a big internal smile: the candidate's expected salary is below the range that was decided for that position. He will then happily give the candidate 3.000 dollars more than what the candidate asks for.
Why it's not worth answering "what's your current salary" question
Fractal learning is ultimately about balance and flexibility. It's about: keeping the big picture in your mind even when digging into the details, whilst simultaneously paying close attention to the details even when you are just skimming choosing the appropriate level of abstraction for what you're trying to do, whilst simultaneously zooming in and out of different levels of abstraction.
Further explanation of fractal learning
When you are new to a field and trying to get an overall sense of how the field works, it might be a waste of time to fall down any rabbit holes as you don't have the experience to know which lines of questioning are relevant. So a breadth-centric approach might serve you well. In this case, you should form appropriate abstractions for the concepts you are encountering so that you can dig deeper into them later if you need.
When you're new to a field, it's better to use the BFS (Breadth-First Search) learning
Fractal learning is essentially a strategy used to make sense of complex systems without getting too lost in the details. It's about being in that Goldilocks zone of not wasting your time learning too much (you have other things to do) while at the same time getting an overall understanding of how things fit together.
Fractal learning
use code to parameterize calls:
You can write Python code to parametrize calls:
python -c "
from mymodule import set_dragon_feeding_schedule, Creatures, Date
set_dragon_feeding_schedule(
feeding_times=['10:00', '14:00', '18:00'],
dishes={Creatures.Tiger: 2, Creatures.Human: 1},
start_day=Date('1020-03-01'),
)
"
instead of:
python -m mymodule \
set_dragon_feeding_schedule \
--feeding-times ['10:00','14:00','18:00'] # hopefully this way it gets recognized \
# how will you define parsing a dict with enum to integer mapping?
--dishes=Creatures.Tiger:2 \
--dishes=Creatures.Human:1 \
--start-day=1020-03-21 # BTW bash allows no comments in multiline calls
That’s it. Types are parsed, checked and converted. Defaults and description are picked from function itself. Even provides bash completions you can install. You wrote no code for that!
Good example of writing CLI interfaces in Python with typer:
import typer
from pathlib import Path
app = typer.Typer()
@app.command()
def find_dragon(name: str, path: Path, min_age_years: int = 200):
<implementation goes here>
@app.command()
def feed_dragon(dragon_name: str, n_humans: int = 3):
<implementation goes here>
if __name__ == "__main__":
app()
later we can call it that way:
python example.py find_dragon 'Drake' --path /on/my/planet
NestedText is a file format for holding data that is to be entered, edited, or viewed by people. It allows data to be organized into a nested collection of dictionaries, lists, and strings. In this way it is similar to JSON, YAML and TOML, but without the complexity and risk of YAML and without the syntactic clutter of JSON and TOML.
NestedText - new format similar to JSON, YAML and TOML
Merge (|) and update (|=) operators have been added to the built-in dict class. Those complement the existing dict.update and {**d1, **d2} methods of merging dictionaries.
From Python 3.9 it's much more convenient to:
| (pipe) operator, e.g. x | y|=Playing video games as a child can improve working memory years later
Use Streamlit if you want to get going as quickly possible and don’t have strong opinions or many custom requirements.Use Dash if you need something more flexible and mature, and you don’t mind spending the extra engineering time.
Streamlit vs Dash
Here’s a table showing the tradeoffs:
Comparison of dashboard tech stack as of 10/2020:
Tabular Comparison Between All Deployment Methods:
Tabular comparison of 4 deployment options:
Today we begin the Early Access Program for Code With Me, a tool for remote collaborative development and pair programming that you have long been waiting for.
Code With Me - new feature from JetBrains for collaborative programming. Unfortunately, soon it won't be free.
"Dorks" are search lines that utilize the search engine different features, with targeted search strings to pinpoint results. Here's a fun list of Google searches from the exploit DB.
Database of Google's Dorks: Google Hacking Database
The internet archive, also known as the "Wayback Machine" holds periodic scans of websites all over the internet for years and years back. This is a mining field for hackers with a target. With tools like waybackcurls (based on waybackcurls.py) one can scan any target of old files. This means that even if you've found and removed a key but did not rotate it, a hacker might still find it in an old version of your website and use it against you.
Hackers do use Wayback machine to find specific security flaws on your website
Enforce MFA everywhere - Google, GitHub, Cloud providers, VPNs, anywhere possible. If it's not optional, reconsider the system in use Rotate keys and passwords constantly, employ and enforce rotation policies Scan your code regularly. Preferably as part of the release process Delegate login profiles and access management to one central system where you control and monitor
20% actions for 80% of effect to protect your API keys/passwords/SSH encrypted keys/certificates
Spending more time looking for a solution rather than thinking about it.
Stack Overflow Antipattern:
DuckDB is an embeddable SQL OLAP database management system
Database not requiring a server like SQLite and offering advantages of PostgreSQL
The <output> tag represents the result of a calculation. Typically this element defines a region that will be used to display text output from some calculation.
How <output> tag can be used in HTML5
The best data scientists are just people who try to understand the ins and outs of business processes and look at problems with healthy suspicion and curiosity. The ability to explain the nuances of manifolds in SVMs is not something that comes into it outside these contrived interviews. I prefer to ask candidates how they would approach solving a problem I’m facing at that moment rather than these cookie cutter tests which are easy to game and tell me nothing
Interesting approach from an experienced data scientist to interview new professionals
What do you think the most important things we should be doing over the next year? What will get in the way of us doing that? What’s going well, i.e. what should we make sure we don’t change? Is there anything you think I should know about?
Good questions to ask as a new PM
Tribuo is a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification.
Tribuo - Java ML library from Oracle
‘Viking’ was a job description, not a matter of heredity, massive ancient DNA study shows
Increase in alcohol-industry funded research is a cause for concern, study suggests
Ten minutes of massage or rest will help your body fight stress
With your passport number, someone could: Book an international flight as youFootnote 2626. Apply for anything that requires proof of identity documentation with the government, e.g. Working with children check Activate a SIM card (and so get an internet connection that’s traceable to you, not them, hiding them from the government) Create a fake physical passport from a template, with the correct passport number (which they then use to cross a border, open a bank account, or anything) who knows what else, not me, bc i have never done a crime
What can be done with out passport number
I’d now found Tony Abbott’s: Passport details Phone number Weird Qantas staff comments.
What information can be found on the "Manage Booking" page inside the website code
3.5 PEP 478 security 2015-09-13 2020-09-13 Larry Hastings
All Python versions less than 3.6 are now EOL
To be reliably able to focus on something, you need to be intuitively, emotionally invested in the outcome.
Without emotions, you might not get the right focus level on the problem
The output of knowledge workers is extremely skewed based on focus. The productivity tiers seem to be:<10% focused on the job at hand: meaningful risk of getting fired.10-50% focus: “meets expectations,” gets regular raises.50%+ focus: superstar, 10x engineer, destined for greatness.
3 focus levels in a career
What you actually need from an ML/Data Science person:- Experience with data cleaning (this is most of the gig)- A solid understanding of linear and logistic regression, along with cross-validation- Some reasonable coding skills (in both R and Python, with a side of SQL).
Basic skills to seek for in Data Scientists
deep learning is good in some scenarios and not in others, so anyone who's just a deep learning developer is not going to be useful to most companies.
Having worked with researchy vs more product/business driven teams, I found that the best results came when a researchy person took the time to understand the product domain, but many of them believe they're too good for business (in which case you should head back to academia).
Problem of PhD profiles in business
Imagine what it must be like for the senior leadership of an established company to actually become data-driven. All of a sudden the leadership is going to consent to having all of their strategic and tactical decision-making be questioned by a bunch of relatively new hires from way down the org chart, whose entire basis for questioning all that expertise and business acumen is that they know how to fiddle around with numbers in some program called R? And all the while, they're constantly whining that this same data is junk and unreliable and we need to upend a whole bunch of IT systems just so they can rock the boat even harder? Pffft.
Reality of becoming a data-driven company
but companies will discover that ML is (for the vast majority) a shiny object with no discernible ROI. Like Big Data, I think we'll see a few companies execute well and actually get some value, while most will just jump to the next shiny thing in a year or two.
As long as ROI isn't clearly visible in ML, as long it might not bring more ML positions
I think a lot of educational Youtube channels aren't that great in actually teaching you anything. What they are great at is sparking the interest and planting the seed for your own work. At least my experience is that actually doing things is how I learn them. Youtube can be a great springboard for that.
Well said
"We find essentially no evidence for climate-driven extinctions during the past 126,000 years Instead, we find that human impact explains 96% of all mammal extinctions during that time"
This command will give you the top 25 stocks that had the highest anomaly score in the last 14 bars of 60 minute candles.
Supriver - find high moving stocks before they move using anomaly detection and machine learning. Surpriver uses machine learning to look at volume + price action and infer unusual patterns which can result in big moves in stocks
Autorzy najnowszych badań skupili się na języku i odkryli, że dzieci podczas przetwarzania języka mówionego używają obu półkul mózgu.
In comparison, almost all adults use only the left hemisphere of the brain
Analyses replicated those from past research using the PEW data, showing that beardedness was more frequent under prevailing conditions of lower health and higher economic disparity.ConclusionsThese findings contribute to evidence that men’s decision to augment their masculinity via full beardedness occurs under conditions characterised by stronger inter-sexual and intra-sexual selection.
It turns out that during a meeting, he asked them how long it would take to remove staff cars from the lot and start digging the first hole for the Boring Company tunnel. The answer: two weeks. Musk asked why, and when he gathered the necessary information, he concluded, “Let’s get started today and see what’s the biggest hole we can dig between now and Sunday afternoon, running 24 hours a day.” Within three hours, the cars were gone and there was a hole in the ground.
The way Elon Musks motivates his employees
An fMRI-based study of error-monitoring shows that students who are focused on monitoring their own learning process, rather than on getting right answers, learn better over time.
The study adds evidence that education focused on correctness is less beneficial to education focused on deeply engaging with content
Would you really forget to push your code if that triggers a rocket launch?
Git-rocket (VS Code extension).
Lol :D
Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there.
After all, magically you get the right Python snippet (based on scikit-learn)
TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming
TPOT automates the following:
The Splitgraph DDN is a single SQL endpoint that lets you query over 40,000 public datasets hosted on or proxied by Splitgraph.You can connect to it from most PostgreSQL clients and BI tools without having to install anything else. It supports all read-only SQL constructs, including filters and aggregations. It even lets you run joins across distinct datasets.
Splitgraph - efficient DDN (Data Delivery Network):
Fastprogress is a clean, well-designed progress bar library brought to you by the fastai family.
Might come in handy for tracking progress of long executions:
! pip install fastprogress
from fastprogress import master_bar, progress_bar
Remote in through VSCode using SSH and ngrok
Don’t forget to tell Git who you are, add this cell so you don’t have to answer every time you commit during a new session!
Authenticate yourself with GitHub:
!git config --global user.email <YOUR EMAIL>
!git config --global user.name <YOUR NAME>
This will allow you to grab both public and private repos without leaving your password exposed in the notebook.
Connecting your GitHub:
import os
from getpass import getpass
import urllib
user = 'rbracco'
password = getpass('Password: ')
repo_name = 'fastai2_audio'
# your password is converted into url format
password = urllib.parse.quote(password)
cmd_string = 'git clone https://{0}:{1}@github.com/{0}/{2}.git'.format(user, password, repo_name)
os.system(cmd_string)
cmd_string, password = "", "" # removing the password from the variable
# Bad password fails silently so make sure the repo was copied
assert os.path.exists(f"/content/{repo_name}"), "Incorrect Password or Repo Not Found, please try again"
Gdown is a nice library for downloading large files from drive to colab.
Use Gdown library to download large files, if:
due to weird Google Drive quota issues, you are better off copying the archive to colab and decompressing it there than you are decompressing the archive while it is hosted on your drive
Decompress your archives on Google Colab, not on the host machine
I learned and did to make it possible to do Automated Speech Recognition research on a Colab instance.
It's possible to do Automated Speech Recognition with Google Colab.
It has been an open secret that you can avoid getting disconnected on Colab by opening the console and entering JavaScript to click the reconnect button for you. It gets very old pressing Ctrl-Shift-I, finding this snippet, and pasting it in every time you start a new session, but Colab gives you the ability to run JavaScript from a cell using the %%javascript magic. Add this cell before your training loop and run it when you plan to do a long training run to avoid getting disconnected mid-training.
%%javascript%%javascript
function ClickConnect(){
console.log("Working");
document.querySelector("colab-toolbar-button#connect").click()
}setInterval(ClickConnect,60000)
As you know, Colab deletes any files you’ve downloaded or created when you end a session. The best option is to use Github to store your code(details below), and Google Drive to store datasets, logs, and anything else that would normally reside on your filesystem but wouldn’t be tracked by git.
That's why you might want to run the code:
from google.colab import drive
drive.mount('/content/drive')
After running it you'll click a link and follow a 30 seconds process
/contant/driveUploads from your computer to google drive can be incredibly slow, especially when dealing with multiple GBs of data. Download speeds are much faster, so take advantage with the command ! wget -c -P save_path url This allows you to download the data only once saving you time and saving bandwidth for the generous owners of publicly hosted datasets.
It's more efficient to get your datasets with ! whet -c -P save_path_url rather than uploading it:
! wget -c -P '/content/drive/My Drive/Colab Notebooks/data/' http://www.openslr.org/resources/12/train-clean-100.tar.gz
Towarzystwo mruczącego kota na kolanach jest niezastąpionym lekarstwem na depresję. Kot odwraca uwagę od problemu, dotrzymuje towarzystwa, okazuje uczucia bez względu na stan psychiczny właściciela i pokazuje, że jest on niezastąpiony. Ponadto pomaga człowiekowi w wyrobieniu odpowiedzialnej postawy i rytualnych odruchów, jak regularne karmienie. Co więcej, spokój kotów udziela się także choremu właścicielowi.
Cats help with depression
Obserwowali oni 40 dzieci z autyzmem. Okazało się, że te, które miały w domach zwierzęta, w tym koty, były spokojniejsze i łatwiej się komunikowały z otoczeniem, niż dzieci bez domowych pupili. Owa otwartość u dzieci ma związek ze wzrostem poziomu oksytocyny (hormonu szczęścia), która wydziela się podczas głaskania futra. Pieszczoty wzmacniają też uczucia zaufania i miłości.
Cats help kids with autism to communicate
wystarczy 20 minut oglądania śmiesznych filmów z kotami, by dostatecznie spadł poziom kortyzolu we krwi (hormonu stresu) i poprawiła się pamięć krótkotrwała u osób starszych. Ponadto śmiech wspomaga pracę serca i układu immunologicznego.
Cats are funny (healthy) to watch ;)
O zbawiennym wpływie mruczenia na poziom ciśnienia we krwi i nastrój pisało już wielu naukowców. Co więcej, koty potrafią modulować częstotliwość dźwięków od 20 do 140 Hz, a niektóre rejestry działają na ludzki organizm skuteczniej niż niejedna terapia.
Cats get rid of your stress
Naukowcy zajmujący się chorobami naczyniowymi na Uniwersytecie w Minosocie dowiedli, że właściciele kotów przejawiają mniejsze skłonności do zawałów. Przez 10 lat obserwowali ok. 4,5 tys. osób, spośród których 3/5 było właścicielami futrzaka. Okazało się, że u miłośników kotów ryzyko ataku serca było o 30 proc. niższe niż u ludzi, którzy nie posiadali żadnego zwierzęcia futerkowego.
Cats save your heart
Chattanooga most likely. My company is doing 85% base salary with no adjustment to RSUs. Tennessee is a great place to save money due to 1) no state income tax, 2) no capital gains taxes, and 3) low cost of living. I estimate I’ll be able to save ~$100k more per year than I currently am in the Bay Area. Plus Chattanooga is known for having some of the fastest internet in the country, rolled out by the city a few years ago. Signal Mountain has great schools for when my kids are older, and it doesn’t hurt that it’s a naturally beautiful place
Chattanooga - great place for a remote work
Instead of avoiding anything hard, let's turn down the difficulty dial by focusing on small steps.
If you lack the ability to do something hard, break it into smaller steps
The important part is not only to know the reason, but to feel it, to get excited about it. How to do that? Link the task to a personal goal that you want to achieve. Attach exciting reasons to the task.
Bad example: Write a thesis
Good examples:
We feel motivated when we viscerally feel how an action is meaningful, or valuable to us and when we believe we can take it
Where motivation comes from.
If you don't feel motivated, remind yourself why you planned to do this task
According to FBM, there are three things we need to do something
Fogg Behavior Model says that we need 3 things at the same time to do something
Archive your AWS data to reduce storage cost
By archiving data on AWS we can reduce the costs up to 97%
search the site itself for codes
Trickt to quickly find discount codes on a website:
site:curology.com ("coupon" | "referral code" | "affiliate code" | "discount code" | "VIP")
find every email on a web page that you're on. The big kahuna - this works for every website. Inject it into a site with Chrome DevTools (more here)
Use this code below to find every e-mail on a webpage:
var elems = document.body.getElementsByTagName("*");
var re = new RegExp("(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$)");
for (var i = 0; i < elems.length; i++) {
if (re.test(elems[i].innerHTML)) {
console.log(elems[i].innerHTML);
}
}
An Image is an ordered collection of root filesystem changes and the corresponding execution parameters for use within a container runtime
Official definition of a container image
VANDERTRAMP verbs use être as opposed to avoir when placed into the past tense.
Devenir (to become) past participle: devenu
Revenir (to come back) past participle: revenu
Monter (to climb) past participle: monté
Rester (to stay) past participle: resté
Sortir (to leave) past participle: sorti
Passer (to pass) past participle: passé .
Venir (to come) past participle: venu
Aller (to go) past participle: allé
Naître (to be born) past participle: né
Descendre (to descend) past participle: descendu
Entrer (to enter) past participle: entré
Rentrer (to re-enter) past participle: rentré
Tomber (to fall) past participle: tombé
Retourner (to turn around) past participle: retourné
Arriver (to arrive / to come) past participle: arrivé
Mourir (to die) past participle: mort
Partir (to leave) past participle: parti
heightparent’s heightwidthparent’s widthtopparent’s heightleftparent’s widthmargin-topparent’s widthmargin-leftparent’s widthpadding-topparent’s widthpadding-leftparent’s widthtranslate-topself’s heighttranslate-leftself’s width
What % means in different CSS elements
If there were no miracles, what then? Well the answer is really quite boring: notes. I would keep notes religiously of everything, detailed accounts of debugging and records of conversations throughout the day, if even a few words. If it happend I was going to have some recording of it somewhere.
Notes are what helped the autistic developer to improve his memory problem
“Blogging” has a poor brand within many high status employers, whereas “memos” and “essays” do not. Try to categorize your writing as these things rather than a blog post.
Interesting alternatives for the word "blogging"
Why [[Tyler Cowen]] still responds to every [[Email]] and loves it. He finds time for this because of what he doesn’t do: he hardly watches [[TV]], **his social life is basically the same as his intellectual life **- his social life is geared towards thinking, discussing, exploring ideas. With no TV, you end up with a lot of [[time]]. #[[unproductive internet activities]] Isn’t [[email]] a low leverage use of his time? **He learns a lot from people that email him, and has filtered his audience so it’s mostly smart people. **He does this by being "sufficiently weird". He’s not even sure it’s highly leveraged. He met [[Patrick Collison]] that way. He doesn’t care if it’s highly leveraged if he’s learning from it. #[[Audience Building]]
By not watching TV you will have more time for activities like responding to every e-mail. You don't have to think that it's unproductive if you exchange thoughts with smart people
As you get older and more successful, it’s harder to get critical [[feedback]] from people. Hang out with critical people and hope you can get benefits. It’s hard to do this. If you are around people that are above you in the hierarchy, you should be critical too. #aging
On being critical
Just keep [[writing]] and re-writing. A book will typically be reworked 10 times. Effort and application – there are no tricks.
Indeed, I agree with that technique
Advantages of people in [[Silicon Valley]]:** super smart but not necessarily highly educated so they don’t just believe what everyone else does. **They think outside the box. They’re thinkers as well as people that have had to do things and pass [[reality]] tests. The only test most academics face is "can I publish this piece?"
What differs people in Silicon Valley and typical students
Libra is a machine learning API designed for non-technical users. This means that it assumes that you have no background in ML whatsoever.
With Libra you can write your ML code much faster:
For example, that's how it compares to Keras.
To facilitate the TDI protocol, an interdisciplinary team at the Media Lab designed and developed Dormio, a sleep-tracking device that can alter dreams by tracking hypnagogia and then delivering audio cues based on incoming physiological data, at precise times in the sleep cycle, to make dream direction possible. Upon awakening, a person’s guided dream content can be used to complete tasks such as creative story writing, and compared experimentally to waking thought content.
Dormio - new device to achieve TDI protocol
Previous neuroscience studies from researchers such as sleep and cognitive sciences expert Stickgold show that hypnagogia (the earliest sleep stage) is similar to the REM stage in terms of brainwaves and experience; however, unlike REM, individuals can still hear audio during hypnagogia while they dream.
Hypnagogia vs REM phase of your dream
In a new paper, researchers from the Media Lab’s Fluid Interfaces group introduce a novel method called “Targeted Dream Incubation” (TDI). This protocol, implemented through an app in conjunction with a wearable sleep-tracking sensor device, not only helps record dream reports, but also guides dreams toward particular themes by repeating targeted information at sleep onset, thereby enabling incorporation of this information into dream content. The TDI method and accompanying technology serve as tools for controlled experimentation in dream study, widening avenues for research into how dreams impact emotion, creativity, memory, and beyond.
TDI - new protocol to record your dreams and guide them toward particular themes
Return sends a specified value back to its caller whereas Yield can produce a sequence of values. We should use yield when we want to iterate over a sequence, but don’t want to store the entire sequence in memory. Yield are used in Python generators. A generator function is defined like a normal function, but whenever it needs to generate a value, it does so with the yield keyword rather than return. If the body of a def contains yield, the function automatically becomes a generator function.
Simple explanation of a difference between return and yield in Python
Kiedy zastanawiamy się nad czymś w języku obcym, to albo więcej myślimy, albo mniej czujemy. Moim jednak zdaniem bardziej prawdopodobne jest wytłumaczenie, że w języku obcym wszystko mnie mniej pobudza, w związku z tym emocje mniej mnie rozpraszają w racjonalnym myśleniu
Język obcy - w porównaniu z ojczystym - sprawia więc, że kiedy napotykamy problem, jesteśmy bardziej skłonni, by rozwiązać go w sposób racjonalny. A mniej bierzemy pod uwagę, jakie normy etyczne rozwiązanie to nagina.
Kiedy myślimy w języku, który znamy słabiej, ważniejsze staje się osiągnięcie założonego efektu, a mniej ważny jest wymiar moralny działań
Obiecujący, zwłaszcza w kontekście młodszych pokoleń, wydaje się też właśnie wkraczający do Polski coliving, czyli formuła mieszkania polegająca na dzieleniu się z innymi wszystkim tym, czym podzielić się da. Współlokatorzy mają własne sypialnie z łazienkami, korzystają jednak z przestronnych i funkcjonalnie zaaranżowanych części wspólnych. Zyskują dzięki temu przestrzeń, która umożliwia im integrację i spędzanie wolnego czasu choćby na oglądaniu filmów czy grze w planszówki.
Coliving as a solution to staying alone
Do jeszcze bardziej przytłaczających wniosków doszła Julianne Holt-Lunstad, która, posiłkując się wynikami 70 badań naukowych, ogłosiła, że samotność zwiększa śmiertelność w takim samym stopniu co otyłość czy wypalanie 15 papierosów dziennie. Z kolei Nicole Valtorty z Uniwersytetu Newcastle ustaliła, że prawdopodobieństwo ataku serca u osób osamotnionych rośnie o 29 proc., a zagrożenie udarem – o 32 proc. „To niezależny czynnik przyczyniający się do śmierci. Może cię po prostu zabić. Znajduje się na tej samej liście co choroby serca i rak – twierdzi dr Josh Klapow, psycholog kliniczny z Uniwersytetu Alabamy.
Data on health consequences of being alone
powszechna plaga nie oszczędza młodych. Poczucie izolacji wpływa u nich na sen, zwiększa poziom stresu, powoduje spadek odporności, redukuje funkcje poznawcze, wywołując objawy podobne do otępienia oraz zwiększa ryzyko depresji.
Health consequences of being alone as a young person
Naukowcy odkryli, że samotność ma na nas wyniszczający wpływ, i to już na poziomie komórkowym. Profesor Steve Cole, dyrektor laboratorium UCLA Social Genomics Core dowiódł nawet, że z powodu izolacji społecznej w ludzkim organizmie dochodzi do tworzenia się przewlekłego, niedającego jawnych objawów stanu zapalnego.
Health consequences of being alone
Z danych GUS-u i tych zebranych przez portale randkowe wynika, że w Polsce w ciągu ostatnich 10 lat liczba osób żyjących samotnie wzrosła o 34 proc.
Wśród krajów europejskich w niechlubnym rankingu zwycięża jednak Szwecja, w stolicy której samotnie mieszka aż 58 proc.(!) populacji. Z kolei w Stanach Zjednoczonych odsetek ten wynosi 27 proc. (w Nowym Jorku prawie 50 proc.) i cały czas rośnie – dla porównania w roku 1920 jednoosobowe gospodarstwo domowe prowadziło tam 5 proc. obywateli.
Percentage of people living alone
the market size: the global note-taking management software market is estimated to reach $1.35 billion by 2026, growing at a CAGR of 5.32% from 2019 to 2026greater scope for innovation: eg., be it creating a task list, a roadmap, or a design repository, Notion can handle it alllack of satisfaction: it’s noted that people always use a combination of note-taking apps and hardly stick to one for a long time
Three reasons why we constantly see more note-taking apps, which in return increase our paradox of choice
if you’re looking for a tool to increase your metacognition, try Roam Research.
Roam Research is one of the tool suggestions to improve your metacognition
Metacognition, put simply, is “thinking about thinking” or “knowing about knowing.” It’s being aware of your own awareness so you can determine the best strategies for learning and problem-solving, as well as when to apply them. The word “metacognition” literally means “above cognition”—it’s one of the most powerful forms of self-monitoring and self-regulation. It’s a fancy word for something fairly simple once you break it down.
Metacognition
An end-user (resource owner 👤) grants a printing service (app 📦) access to their photo (resource 🖼) hosted in a photo-sharing service (resource server 📚), without sharing their username and password. Instead, they authenticate directly with a server trusted by the photo-sharing service (authorization server 🛡), which issues the printing service delegation-specific credentials (access token 🔑).
Clear OAuth flow example
For this study, researchers conducted technical interviews of 48 computer science undergraduates and graduate students. Half of the study participants were given a conventional technical interview, with an interviewer looking on. The other half of the participants were asked to solve their problem on a whiteboard in a private room. The private interviews did not require study participants to explain their solutions aloud, and had no interviewers looking over their shoulders. Researchers measured each study participant’s interview performance by assessing the accuracy and efficiency of each solution. In other words, they wanted to know whether the code they wrote would work, and the amount of computing resources needed to run it. “People who took the traditional interview performed half as well as people that were able to interview in private,” Parnin says. “In short, the findings suggest that companies are missing out on really good programmers because those programmers aren’t good at writing on a whiteboard and explaining their work out loud while coding.”
Interesting experiment focused on recruitment. "Candidates" who are able to solve the challenge alone in quiet rooms perform better
(This is why writing is important. It’s harder to fool yourself that you understand something when you sit down to write about it and it comes out all disjointed and confused. Writing forces clarity.)
This is why I like to repeat that writing shapes your understanding of the topic
(This is why writing is important. It’s harder to fool yourself that you understand something when you sit down to write about it and it comes out all disjointed and confused. Writing forces clarity.)
This is why I like to repeat that writing shapes your understanding of the topic
One component of it is energy: thinking hard takes effort, and it’s much easier to just stop at an answer that seems to make sense, than to pursue everything that you don’t quite get down an endless, and rapidly proliferating, series of rabbit holes.
To think in an intelligent way, you need to take effort (energy)
What this means is that you can internalize good intellectual habits that, in effect, “increase your intelligence”. ‘Intelligence’ is not fixed.
Fix your intelligence with the right habits
Intelligent people simply aren’t willing to accept answers that they don’t understand — no matter how many other people try to convince them of it, or how many other people believe it, if they aren’t able to convince them selves of it, they won’t accept it.
Question authority
The smartest person I’ve ever known had a habit that, as a teenager, I found striking. After he’d prove a theorem, or solve a problem, he’d go back and continue thinking about the problem and try to figure out different proofs of the same thing. Sometimes he’d spend hours on a problem he’d already solved.
Take your time and ponder
So in brief, for our application service, if we understand the access patterns very well, they’re repeatable, they’re consistent, and scalability is a big factor, then NoSQL is a perfect choice.
When NoSQL is a perfect choice
Comparison Time … 🤞
Brief comparison of 8 aspects between SQL vs NoSQL
The lessons you learn from chess are generalizable only at a high level (e.g. a bad plan is better than no plan). But if you have games that are (a) fun and (b) accurate for some aspects of reality, such as KSP or Factorio, you do get learning that is real and transferable. The challenge is in making games that satisfy both constraints.
Chess to be a good educator misses the accuracy for some aspects of reality
Although the impact will not be distributed evenly, digital transformation will touch virtually every corner of the global workforce — from food production (324,000 new jobs) to healthcare (2 million) and the automotive industry (6 million).
The main industries affected:
check the PowerBI visualisation below for more info
Over the next five years, we estimate that the global workforce can absorb around 149 million new technology-oriented jobs. Software development accounts for the largest single share of this forecast, but roles in related fields like data analysis, cyber security, and privacy protection are also poised to grow substantially.
It is estimated that 2025 will require 4.6 times more IT profiles than 2020.
Data source: Microsoft Data Science utilising LinkedIn data
Most of the discourse is about how AI will “replace” humans. I prefer the Licklider school of thought: human-computer symbiosis. AI will make humans vastly more effective by automating tedious tasks. For example, humans can use text AI such as GPT-3 to generate ideas/boilerplate writing to get around the terror of the blank page, and then simply pick the best ones and refine/iterate on those. (AI Dril, which was based on GPT-2, was an early example of this). As AI gets better, “assistive creativity” will become a bigger thing, enabling humans to create sophisticated artifacts (including video games!) easier and better than ever.
Why AI is important for human productivity
So it makes sense that video games would be the primary educational environment of the future: they are the best way we have of (a) creating simulations of reality (b) with fast feedback loops (c) accessible at low cost.
Games as the future of learning
Video games will become a core component of education. This sounds absurd, but consider that simulations are already used widely for learning
When you understand something very well it’s almost as though you can play around with it using all your senses — touch, feeling, space.
The fundamental principle of education is to give students an environment, and tools, where they can make discoveries themselves. This requires space, and time, and autonomy.
Wyróżniamy 2 podstawowe metody wykrywania malware(można mówić o większej ilości, jednak na potrzeby przybliżenia podstaw wspomnę tylko o dwóch):
2 ways antiviruses detect malware:
How to prevent the environment from being “invalidated”?Docker containers (Rocker)
Rocker
SAS, R, Stata, SPSS may return different results even for quantiles, or due to floating number representation! The results should be maximally close to each other, but what about resampling methods (SAS and R gives different random numbers for the same seed)?
Different results between SAS, R, Stata, SPSS
Typical software requirements specify the following
99.9% open-source. 0.1% is licensed (free for non-commercial use)
License of libraries in R
Status of R on the Clinical Research market
Differences in
Differences between R and SAS:
Tospeeduptheprocesswithoutsacrificingaccuracy,theteamalsousesRevolutionRanalyticproducts
Revolution R
The results suggest that, in addition to a distinct beginning and end to the unprecedented bloodshed of 1910-1950, there was another abrupt shift towards a greater level of peace in the early 1990s.
The brain uses the same area to save coding as it does to save our speech. They found that programming is like talking. The research found out that the brain regions that are most active during coding are those that are also relevant in the processing of natural language.
Estimates say that 83% of us will be hit with a mental health crisis in our lives, we can all make the choices to invest wisely in this area to improve our ‘mental durability’ to deal with it properly.
in Python - setting up basic logger is very simple
Apart from printing the result, it is better to debug with logging.
Sample logger:
import logging
logging.basicConfig(
filename='application.log',
level=logging.WARNING,
format= '[%(asctime)s] {%(pathname)s:%(lineno)d} %(levelname)s - %(message)s',
datefmt='%H:%M:%S'
)
logging.error("Some serious error occurred.")
logging.warning('Function you are using is deprecated.')
the sample result:
[12:52:35] {<stdin>:1} ERROR - Some serious error occurred.
[12:52:35] {<stdin>:1} WARNING - Function you are using is deprecated.
to find its location, type:
logging.getLoggerClass().root.handlers[0].baseFilename
This advertising system is designed to enable hyper-targeting, which has many unintended consequences that have dominated the headlines in recent years, such as the ability for bad actors to use the system to influence elections, to exclude groups in a way that facilitates discrimination, and to expose your personal data to companies you’ve never even heard of.
Where your Google data goes to
if you search for something on Google, you may start seeing ads for it everywhere.
In comparison to DuckDuckGo, Google presents you ads everywhere, not just in the search results
Alarmingly, Google now deploys hidden trackers on 76% of websites across the web to monitor your behavior and Facebook has hidden trackers on about 25% of websites, according to the Princeton Web Transparency & Accountability Project. It is likely that Google and/or Facebook are watching you on most sites you visit, in addition to tracking you when using their products.
When you search on DuckDuckGo, we can show you an ad based on the keywords you type in. That’s it. And it works.
Simple model how DuckDuckGo makes its business
Almost all of the money search engines make (including Google) is based on the keywords you type in, without knowing anything about you, including your search history or the seemingly endless amounts of additional data points they have collected about registered and non-registered users alike. In fact, search advertisers buy search ads by bidding on keywords, not people. It makes intuitive sense, too. If you search for ‘car’, you are more likely to respond to a car ad than something you searched for last week.
Search engines do most of the business on keywords
If you want a fast website without breaking the bank, go for GitHub Pages. It’s completely free and super fast. It does, however, require you to open source your site. If that’s not doable, CloudFront is a good alternative, but its price depends on how much bandwidth you push around. For most personal sites, CloudFront won’t cost more than a couple of dollars per month. The same thing goes for Google Cloud Storage.
Advice for a personal use regarding the static webhosting benchmark (2020 May)
The best all-around performer is AWS CloudFront, followed closely by GitHub Pages. Not only do they have the fastest response times (median), they’re also the most consistent. They are, however, closely followed by Google Cloud Storage. Interestingly, there is very little difference between a regional and multi-regional bucket. The only reason to pick a multi-regional bucket would be the additional uptime guarantee. Cloudflare didn’t perform as well I would’ve expected.
Results of static webhosting benchmark (2020 May):
Some free, digital Zettelkastens include zettelkasten.de, zettlr, and roamresearch. I use Roam.
One of the best solutions to implement Zettelkastens: Roam. However, in my case OneNote is doing fine. Maybe I can switch to Roam if I will start working on a specific research problem?
The key is to make connections between ideas during note-taking, way before you need to review them for your work. This forces you to actively connect the dots (during note-taking) and lets you find relevant ideas with ease in future.
How Zettelkasten works:
German sociologist Niklas Luhmann. One thing you should know about Luhmann—he was extremely productive. In his 40 years of research, he published more than 70 books and 500 scholarly articles. How did he do accomplish this? He credits it to his Zettelkasten which focuses on connections between notes.
To be super productive, Niklas Luhmann used to take notes relating to each other
it allows each new key to be given a default value based on the type of dictionary being created
Difference between defaultdict and dict
Memoization can be achieved through Python decorators
Example of memoization in Python:
import timeit
def memoize_fib(func):
cache = {}
def inner(arg):
if arg not in cache:
cache[arg] = func(arg)
return cache[arg]
return inner
def fib(num):
if num == 0:
return 0
elif num == 1:
return 1
else:
return fib(num-1) + fib(num-2)
fib = memoize_fib(fib)
print(timeit.timeit('fib(30)', globals=globals(), number=1))
print(timeit.timeit('fib(30)', globals=globals(), number=1))
print(timeit.timeit('fib(30)', globals=globals(), number=1))
Output:
4.9035000301955733e-05
1.374000021314714e-06
1.2790005712304264e-06
A deep copy refers to cloning an object. When we use the = operator, we are not cloning the object; instead, we reference our variable to the same object (a.k.a. shallow copy).
Difference between a shallow copy (=) and a deep copy:
Python 2 is entrenched in the software landscape to the point that co-dependency between several softwares makes it almost impossible to make the shift.
Shifting from Python 2 to 3 isn't always straight to the point
The @property decorator allows a function to be accessed like an attribute.
@property decorator
var = true_val if condition else false_val
Example of a ternary operator (one-line version of if-else):
to_check = 6
msg = "Even" if to_check%2 == 0 else "Odd"
print(msg)
Usual if-else:
msg = ""
if(to_check%2 == 0):
msg = "Even"
else:
msg = "Odd"
print(msg)
This method is automatically called to allocate memory when a new object/ instance of a class is created.
__init__ method in Python (which essentially all classes have)
decorator is a design pattern in Python that allows a user to add new functionality to an existing object without modifying its structure
Decorators
The philosophy behind Flask is that it gives only the components you need to build an app so that you have the flexibility and control. In other words, it’s un-opinionated. Some features it offers are a build-int dev server, Restful request dispatching, Http request handling, and much more.
Flask isn't as full of features as Django (which makes him lighter), but it still offers:
The only difference is that range returns a Python list object and xrange returns an xrange object. This means that xrange doesn’t actually generate a static list at run-time like range does.
Difference between range() and xrange()
NumPy is not just more efficient; it is also more convenient. You get a lot of vector and matrix operations for free, which sometimes allow one to avoid unnecessary work. And they are also efficiently implemented.
Main advantage of NumPy arrays over (nested) Python lists
process of retrieving original Python objects from the stored string representation
Unpickling
Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function
Pickling
The beauty of lambda functions lies in the fact that they return function objects. This makes them helpful when used with functions like map or filter which require function objects as arguments.
When to use lambdas
In Python, the term monkey patch only refers to dynamic modifications of a class or module at run-time.
Monkey patching in Python
Python has a multi-threading package but if you want to multi-thread to speed your code up, then it’s usually not a good idea to use it. Python has a construct called the Global Interpreter Lock (GIL). The GIL makes sure that only one of your ‘threads’ can execute at any one time. A thread acquires the GIL, does a little work, then passes the GIL onto the next thread.
Multi-threading is available in Python but it;s often not a good idea
the only meditation I have EVER found effective in my life. 1:19:57 Intro and explanation + 1:27:51 Meditation start
Meditation with triphasic breathing (you won't fight with your thoughts)
It focuses on 6 steps (feel a stop between each phase):
Beginning:
Repeat:
Ending:
Video material
- High-level modules should not depend on low-level modules. Both should depend on the abstraction.- Abstractions should not depend on details. Details should depend on abstractions.
SOLI(D)
Dependency Inversion
Clients should not be forced to depend on methods that they do not use.
SOL(I)D
Interface Segregation
If S is a subtype of T, then objects of type T in a program may be replaced with objects of type S without altering any of the desirable properties of that program.
SO(L)ID
Liskov Substitution
Classes should be open for extension, but closed for modification
S(O)LID
Open-Closed
A class should have a single responsibility
(S)OLID
Single Responsibility
Alternatively, think about the decision in different timelines. Ask yourself: What will be the consequences of this decision in 10 minutes? 10 months? 10 years?
Another way of examining the second order of decision's consequences
Consider a decision you have to make. Start by looking at the most immediate effects of making this decision – the first order. Then for each of the effect ask yourself: "And then what?"
The way to examine the second order of decision's consequences
I've found that the most underrated problem solving tool is simply typing out my thought process.I used to be the type who asked a lot of questions until I realized that formulating the question was often more important than getting the answer.
You're asking a lot of questions? It might be also a sign of formulating/shaping your decisions
Amazon Machine Learning Deprecated. Use SageMaker instead.
Instead of Amazon Machine Learning use Amazon SageMaker
The MRI results showed that people for whom this effect was the strongest--those whose exposure to diverse experiences was more strongly associated with positive feeling ("affect")--exhibited greater correlation between brain activity in the hippocampus and the striatum. These are brain regions that are associated, respectively, with the processing of novelty and reward-- beneficial or subjectively positive experiences.
The interface is slightly less polished (and, unfortunately, I experience a delay between opening it and getting a chart), but the tracking works. Since it is in Vue I love, one day I might modify it to my needs.
Comparing ActivityWatch to RescueTime
It is a modern-day “memento mori”
Mortality - New Tab (browser extension)
With Intention I:know how much of my time is spent on distractions,decide how much time I need,I see the countdown (so I know if I need to wrap-up a reply, or if it makes sense to start writing a new one),it automatically blocks these sites,yet, it distinguishes between “normal use” of YouTube and e.g. using it for creating a workshop on deep learning (looking for video abstract of recent papers).
Intention is well recommended by Piotr Migdał for your productivity. More than toggl or RescueTime.
There are a few plugins (e.g. ColdTurkey) that are “too nuclear”. Being halted in the middle of writing a reply (on Facebook or Hacker News), with no prior warning, left me disturbed.
ColdTurkey is quite harsh in terms of making you more productive
Hot Reloading refers to the ability to automatically update a running web application when changes are made to the application’s code.
Hot Reloading is what provides a great experience with updating your Dash code inside the Jupyter Notebooks
JupyterDash supports three approaches to displaying a Dash application during interactive development.
3 display modes of Dash using Jupyter Notebooks:
# Run app and display result inline in the notebookapp.run_server(mode='inline')
Moreover, you can display your Dash result inside a Jupyter Notebook using IPython.display.IFrame with this line:
app.run_server(mode='inline')
If running the server blocks the main thread, then it’s not possible to execute additional code cells without manually interrupting the execution of the kernel.JupyterDash resolves this problem by executing the Flask development server in a background thread. This leaves the main execution thread available for additional calculations. When a request is made to serve a new version of the application on the same port, the currently running application is automatically shut down first. This makes is possible to quickly update a running application by simply re-executing the notebook cells that define it.
How Dash can run inside Jupyter Notebooks
You can also try it out, right in your browser, with binder.
Dash can be tried out inside a Jupyter Notebook right in your browser using binder.
Then, copy any Dash example into a Jupyter notebook cell and replace the dash.Dash class with the jupyter_dash.JupyterDash class.
To use Dash in Jupyter Notebooks, you have to import:
from jupyter_dash import JupyterDash
instead of:
import dash
Therefore, all the imports could look like that for a typical Dash app inside a Jupyter Notebook:
import plotly.express as px
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
sci-hub is a service that lets you access research papers for free. It’s not quuiiiiiiiiite legal, but buying papers is super expensive, like fifty dollars per paper. Not something I can afford, and I think the entire industry is evil anyway.
Good point on the research industry & Sci-Hub
So instead I have it open up Frink. If Frink is already open, it activates it. If it’s already active, it minimizes it. Much better UX.3
Great AutoHotkey (AHK) script to open any app:
toggle_app(app, location)
{
if WinExist(app)
{
if !WinActive(app)
{
WinActivate
}
else
{
WinMinimize
}
}
else if location != ""
{
Run, %location%
}
}
Launch_App2::toggle_app("Frink", "\Path\to\frink.jar")
My friends ask me if I think Google Cloud will catch up to its rivals. Not only do I think so — I’m positive five years down the road it will surpass them.
GCP more popular than AWS in 2025?
So if GCP is so much better, why so many more people use AWS?
Why so many people use AWS:
As I mentioned I think that AWS certainly offers a lot more features, configuration options and products than GCP does, and you may benefit from some of them. Also AWS releases products at a much faster speed.You can certainly do more with AWS, there is no contest here. If for example you need a truck with a server inside or a computer sent over to your office so you can dump your data inside and return it to Amazon, then AWS is for you. AWS also has more flexibility in terms of location of your data centres.
Advantages of AWS over GCP:
I would argue that there isn’t any other company on the planet that does scalability and global infrastructure better than Google (although CloudFlare definitely gives it a run for its money in some areas).
Scalability of Google's service is still #1
Both AWS and GCP are very secure and you will be okay as long as are not careless in your design. However GCP for me has an edge in the sense that everything is encrypted by default.
Encryption is set to default in GCP
I felt that performance was almost always better in GCP, for example copying from instances to buckets in GCP is INSANELY fast
Performance wise GCP also seems to outbeat AWS
AWS charges substantially more for their services than GCP does, but most people ignore the real high cost of using AWS, which is; expertise, time and manpower.
AWS is more costly, requires more time and manpower over GCP
GCP on the other hand has fewer products but the ones they have (at least in my experience) feel more complete and well integrated with the rest of the ecosystem
GCP has less but more effective products
GCP provides a smaller set of core primitives that are global and work well for lots of use cases. Pub/Sub is probably the best example I have for this. In AWS you have SQS, SNS, Amazon MQ, Kinesis Data Streams, Kinesis Data Firehose, DynamoDB Streams, and maybe another queueing service by the time you read this post. 2019 Update: Amazon has now released another streaming service: Amazon Managed Streaming Kafka.
Pub/Sub of GCP might be enough to replace most (all?) of the following Amazon products: SQS, SNS, Amazon MQ, Kinesis Data Streams, Kinesis Data Firehose, DynamoDB Streams, Amazon Managed Streaming Kafka
At the time of writing this, there are 169 AWS products compared to 90 in GCP.
AWS has more products than GCP but that's not necessarily good since some even nearly duplicate
Spinning an EKS cluster gives you essentially a brick. You have to spin your own nodes on the side and make sure they connect with the master, which a lot of work for you to do on top of the promise of “managed”
Managing Kubernetes in AWS (EKS) also isn't as effective as in GCP or GKE
You can forgive the documentation in AWS being a nightmare to navigate for being a mere reflection of the confusing mess that is trying to describe. Whenever you are trying to solve a simple problem far too often you end up drowning in reference pages, the experience is like asking for a glass of water and being hosed down with a fire hydrant.
Great documentation is contextual, not referential (like AWS's)
Jeff Bezos is an infamous micro-manager. He micro-manages every single pixel of Amazon’s retail site. He hired Larry Tesler, Apple’s Chief Scientist and probably the very most famous and respected human-computer interaction expert in the entire world, and then ignored every goddamn thing Larry said for three years until Larry finally — wisely — left the company. Larry would do these big usability studies and demonstrate beyond any shred of doubt that nobody can understand that frigging website, but Bezos just couldn’t let go of those pixels, all those millions of semantics-packed pixels on the landing page. They were like millions of his own precious children. So they’re all still there, and Larry is not.
Case why AWS doesn't look as it supposed to be
The AWS interface looks like it was designed by a lonesome alien living in an asteroid who once saw a documentary about humans clicking with a mouse. It is confusing, counterintuitive, messy and extremely overcrowded.
:)
After you login with your token you then need to create a script to give you a 12 hour session, and you need to do this every day, because there is no way to extend this.
One of the complications when we want to use AWS CLI with 2FA (not a case of GCP)
In GCP you have one master account/project that you can use to manage the rest of your projects, you log in with your company google account and then you can set permissions to any project however you want.
Setting up account permission to the projects in GCP is far better than in AWS
It’s not that AWS is harder to use than GCP, it’s that it is needlessly hard; a disjointed, sprawl of infrastructure primitives with poor cohesion between them.
AWS management isn't as straightforward as the one of GCP
So be careful running editing a bash script that may be currently executing. It could execute an invalid command, or do something very surprising.
Never modify a running bash command as it can execute something surprising
“If you are a non-college graduate man you have a less than 50/50 shot of ever being married in your life” – Andrew YangIn the 1970s and ‘80s, there were about 17 million manufacturing jobs in the USToday, there are about 12 million of those jobsMore women are graduating from college than men58% of college graduates in the US are women
Today, 40% of children are born to unmarried mothersBack in the 70s and 80s, it was only 15%
The most common method of preventing CSRF is by generating a secret random string, known as a CSRF token, on the server, and checking for that token when the client performs a write.
To completely defend the CSRF attack, one needs to generate a CSRF token
CORS relaxes the Same-Origin Policy (SOP), a critical security measure that prevents scripts on one site (e.g. the attacker’s site) from accessing sensitive data on another site (e.g. the Definitely Secure Bank portal). If something was protecting you from CSRF, it would be the SOP.
Thanks to Cross-Origin Resource Sharing (CORS), Same-Origin Policy (SOP) is being relaxed and CSRF is blocked from cross-origin reads, but not from writes (so POST is still effective but attacker cannot read the response)