The fact that no one is in charge and nothing can be modified also means that mistakes cannot be corrected. A bank can reverse a payment request. This is impossible for bitcoin and other cryptocurrencies. So anything that has been stolen will stay stolen. There is a continuous stream of hackers targeting bitcoin exchanges and users, and fraudsters launching investment vehicles that are in fact pyramid schemes.

Also, in a blockchain you aren’t anonymous, but “pseudonymous”: your identity is linked to a number, and if someone can link your name to that number, you’re screwed. Everything you got up to on that blockchain is visible to everyone. 

Firstly: the technology is at loggerheads with European privacy legislation, specifically the right to be forgotten. Once something is in the blockchain, it cannot be removed. For instance, hundreds of links to child abuse material and revenge porn were placed in the bitcoin blockchain by malicious users. It’s impossible to remove those.

Out of over 86,000 blockchain projects that had been launched, 92% had been abandoned by the end of 2017, according to consultancy firm Deloitte.

It’s very slow (sometimes a transaction takes nine minutes, sometimes nine days!), a lot of hassle (try it for yourself – cutting open hard plastic packaging with scissors is more user friendly), and very unstable (its price rose to €17,000 euros; dropped to €3,000; rose again to now €10,000).

Not only that, but the decentralised utopia that Nakamoto dreamed about, namely avoiding trusted third parties, is still far out of reach. Ironically, there are now three mining pools – a type of company that builds rooms full of servers in Alaska and other locations way up above the Arctic circle – which are responsible for more than half of all the new bitcoin (and also for checking payment requests). 

This is pretty inefficient. And it would be a lot less complicated if you trusted someone to manage your data (a bank, for instance). But that’s not what Satoshi Nakamoto did, which is what the inventor of bitcoin calls himself. He thought banks were bad news. They can make money disappear from your account. So he invented bitcoin. And bitcoin works, it exists, and according to the latest count, there are nearly 1,855 other bitcoin-like currencies out there.

At its core, blockchain is a glorified spreadsheet (think: Excel with one table). In other words, a new way to store data. In traditional databases there’s usually one person who’s in charge, who decides who can access and input data, who can edit and remove it. That’s different in a blockchain. Nobody’s in charge, and you can’t change or delete anything, only view and input data.

Conclusions Draw your own. No? Fine. Good Enough: JSON? Protobuf Cap’n Proto Flatbuffers CBOR msgpack Avoid: YAML XML Thrift? BSON

There’s a story about an art teacher that split their class in half. They told one half of the students that they’d be graded based on a single piece of work, and the other half that they would be graded on the quantity of work produced. The half that was being graded on quantity ended up producing higher quality pieces. By iterating and learning from their mistakes they actually ended up producing better work than the students that only had to produce one piece. Quantity leads to quality. Share your work Sharing work helps you to think and develop. The feedback you get feeds into the next iteration.

Statistical techniques: average, quantiles, probability distribution, association rulesSupervised ML algorithms: logistic regression, neural net, time-series analysisUnsupervised ML algorithms: Cluster analysis, Bayesian network, Peer group analysis, break point analysis, Benford’s law (law of anomalous numbers)

In machine learning, parlance fraud detection is generally treated as a supervised classification problem, where observations are classified as “fraud” or “non-fraud” based on the features in those observations. It is also an interesting problem in ML research due to imbalanced data — i.e. there’s a very few cases of frauds in an extremely large amount of transactions.

With ever-increasing online transactions and production of a large volume of customer data, machine learning has been increasingly seen as an effective tool to detect and counter frauds. However, there is no specific tool, the silver bullet, that works for all kinds of fraud detection problems in every single industry. The nature of the problem is different in every case and every industry. Therefore every solution is carefully tailored within the domain of each industry.

Facebook AI is introducing M2M-100, the first multilingual machine translation (MMT) model that can translate between any pair of 100 languages without relying on English data. It’s open sourced here. When translating, say, Chinese to French, most English-centric multilingual models train on Chinese to English and English to French, because English training data is the most widely available. Our model directly trains on Chinese to French data to better preserve meaning. It outperforms English-centric systems by 10 points on the widely used BLEU metric for evaluating machine translations. M2M-100 is trained on a total of 2,200 language directions — or 10x more than previous best, English-centric multilingual models. Deploying M2M-100 will improve the quality of translations for billions of people, especially those that speak low-resource languages. This milestone is a culmination of years of Facebook AI’s foundational work in machine translation. Today, we’re sharing details on how we built a more diverse MMT training data set and model for 100 languages. We’re also releasing the model, training, and evaluation setup to help other researchers reproduce and further advance multilingual models. 

For Decision Scientists, the business problem comes first. Analysis follows and is dependent on the question or business decision that needs to be made.

The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer.

Your weekly reminder that1. You should not have your development process on a centralized platform, at least not if you're doing anything that smells of copyright issues2. If you do host on a centralized platform, have regular, decentralized backups of code and issue tracking.3. Also, avoid US-centric hosting for this kind of thing. But really, refer to 1.

Junior school is great because you don’t learn to find a job. You’re at school to grow as a citizen, not to prepare to your future life, and actually you’re better not to think about your future if you don’t want to question yourself about the meaning of your existence. This is the same thing for side projects. See them as a way to discover a new topic and enlarge your horizon, not as something that you could action in your current position or to find a new job.

Even from a purely technical point of view, you will not really understand a library or a framework by just sticking to the tutorial or its basic features. You’ll have to dig into and to face its most painful aspects to deeply understand it.

Today, I recommend not hiding your side projects, but to not displaying them as a primary part of your identity, to not scare the recruiter and let him feel that you have a secondary plan.

You can generally assume that a developer coming from a cutting-edge company has better chances to be a great developer than a developer coming from a Java 1.4 shop. He may not be smarter, but he has been hired by a company with a most demanding hiring process, and has been surrounded by some of the smartest developers.

First, most recruiters don’t care about your personal projects or how many meetups you went during the year. What matters the most is your current company - and by that I mean the name of your current company.

suite of over 30 separate packages of React visualization primitives that fall into several categories (Figure 2). It is un-opinionated on state management, animation, and styling so it can integrate with any React codebase, and its emphasis on modularity (similar to D3) lets you keep your bundle sizes down by only using the packages you need to create your reusable chart library or a custom one-off chart.

In our research (Figure 1), we found that existing React visualization libraries are often high-level abstractions and optimized for ease of use (i.e., fewer lines of code) at the expense of expressivity. None offer the expressivity of D3 primitives and many don’t allow for the optimization we want in production because computation, animations, state management, styles, and rendering are all encapsulated.

because D3 and React both want to own DOM manipulation, we’ve found that it’s best to only use D3 for the math and React for the DOM because two mental models for updating the DOM opens the door for bugs to sneak in. However, using D3 solely for math means a significant amount of its (DOM-based) functionality is not available for use: selection.join, zoom, drag, brush, and transitions. Additionally, as mentioned above, D3 has its own learning curve and we would like developers to feel like they are writing native React code with standard APIs and familiar patterns.

Leveraging React and its ecosystem would provide learnability and performance, and a low-level, modular API would deliver expressivity.

After 3 years of development, 2.5 years of production use at Airbnb, and a rewrite in TypeScript we are excited to announce the official 1.0 release of visx (formerly vx). You can find the project on GitHub and browse documentation and examples on airbnb.io.

Using The clamp() CSS function we can create a responsive website with only one property

h1 {
  font-size: clamp(16px, 5vw, 34px);
}

Counterintuitively, people love reading about and engaging in what they consider "stupidity".Internet companies capitalize on this by bringing an optimized stream of stupidity for your viewing pleasure. Take a look at the front page of Reddit (logged out, default subs): Half of the content highlights stupidity of others: /r/IdiotsInCars shows the worst drivers from around the world, /r/insanepeoplefacebook shows the most bizarre clips from social media, /r/choosingbeggars highlights the dumbest negotiation attempts, /r/trashy and /r/iamatotalpieceofshit are selected stories of bad behavior, /r/whatcouldgowrong and /r/instantkarma are videos of people making bad decisions and suffering the consequences, /r/publicfreakout is videos of people fighting. Contributors hunt for the most egregious examples to post to Reddit in the hopes of getting upvotes.Twitter isn't much better: Topics spread on Twitter when they promote outrage or allow the reader to feel smugly superior to someone.If you spend your days online consuming this content day in and day out, you're going to become convinced that the world is "stupid" and getting stupider. In reality, you're simply tapping into stupidity concentrators, getting bite-sized views of stupidity so you can react in astonishment and feel superior to stupid people doing stupid things.I think COVID quarantine has worsened this, as people are getting even more of their worldview through social media feeds instead of actually interacting with people in the real world. If 90% of your insight into social interactions comes from clickbait social media sites selecting the most egregious stories and videos from around the world, of course you're going to think "stupidity is expanding". In reality, it's a sign that you need to revaluate your sources of information and move to platforms and networks where people are talking about something other than other people's stupidity.

The total complexity of a system is a constant. If you make a user’s interaction with a system simpler, the complexity behind the scenes increases.

I see this all around me. People are fixated on careers, hobbies (FOMO), spread thin by family obligations and errands. The truth is, happiness does not derive from these things. This "busyness" is an invention. Life is simple, and happiness actually derives from having cats.

Daydreaming at Work Can Fuel Creativity

Sprawdźmy który rodzaj modelu daje najlepszą skuteczność: Python sns.boxplot(data=models_df, x='score', y='model') 1 sns.boxplot(data=models_df, x='score', y='model')

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')

Teraz w zagnieżdżonych pętlach możemy sprawdzić każdy z każdym podmieniając klasyfikatory i transformatory (cała pętla trochę się kręci):

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.

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.

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.

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.

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.

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.

use code to parameterize 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'),
)
"

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!

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()

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.

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.

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.

Here’s a table showing the tradeoffs:

Tabular Comparison Between All Deployment Methods:

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.

"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.

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.

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

Spending more time looking for a solution rather than thinking about it.

DuckDB is an embeddable SQL OLAP database management system

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.

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

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?

Tribuo is a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification.

‘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

I’d now found Tony Abbott’s: Passport details Phone number Weird Qantas staff comments.

3.5 PEP 478 security 2015-09-13 2020-09-13 Larry Hastings

To be reliably able to focus on something, you need to be intuitively, emotionally invested in the outcome.

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.

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).

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).

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.

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.

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.

"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.

Autorzy najnowszych badań skupili się na języku i odkryli, że dzieci podczas przetwarzania języka mówionego używają obu półkul mózgu.

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.

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.

Would you really forget to push your code if that triggers a rocket launch?

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.

TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming

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.

Fastprogress is a clean, well-designed progress bar library brought to you by the fastai family.

! 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!

!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.

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.

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

I learned and did to make it possible to do Automated Speech Recognition research on a Colab instance.

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
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.

Uploads 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.

! 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.

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.

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.

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.

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.

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

Instead of avoiding anything hard, let's turn down the difficulty dial by focusing on small 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.

We feel motivated when we viscerally feel how an action is meaningful, or valuable to us and when we believe we can take it

According to FBM, there are three things we need to do something

Archive your AWS data to reduce storage cost

search the site itself for codes

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)

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

VANDERTRAMP verbs use être as opposed to avoir when placed into the past tense.

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

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.

“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.

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]]

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

Just keep [[writing]] and re-writing. A book will typically be reworked 10 times. Effort and application – there are no tricks.

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?"

Libra is a machine learning API designed for non-technical users. This means that it assumes that you have no background in ML whatsoever.

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.

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. 

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.

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.

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.

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.

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.

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.

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.

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

if you’re looking for a tool to increase your metacognition, try Roam Research.

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.

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 🔑).

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.”

(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 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.)

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.

What this means is that you can internalize good intellectual habits that, in effect, “increase your intelligence”. ‘Intelligence’ is not fixed.

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.

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.

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.

Comparison Time … 🤞

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.

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).

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.

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.

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.

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):

How to prevent the environment from being “invalidated”?Docker containers (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)?

Typical software requirements specify the following

99.9% open-source. 0.1% is licensed (free for non-commercial use)

Status of R on the Clinical Research market

Differences in

Tospeeduptheprocesswithoutsacrificingaccuracy,theteamalsousesRevolutionRanalyticproducts

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

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.')

[12:52:35] {<stdin>:1} ERROR - Some serious error occurred.
[12:52:35] {<stdin>:1} WARNING - Function you are using is deprecated.

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.

if you search for something on Google, you may start seeing ads for it everywhere.

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.

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.

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.

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.