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.

it allows each new key to be given a default value based on the type of dictionary being created

Memoization can be achieved through Python decorators

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))

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).

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.

The @property decorator allows a function to be accessed like an attribute.

var = true_val if condition else false_val

to_check = 6
msg = "Even" if to_check%2 == 0 else "Odd"
print(msg)

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.

decorator is a design pattern in Python that allows a user to add new functionality to an existing object without modifying its structure

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.

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.

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.

process of retrieving original Python objects from the stored string representation

Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function

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.

In Python, the term monkey patch only refers to dynamic modifications of a class or module at run-time.

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.

the only meditation I have EVER found effective in my life. 1:19:57 Intro and explanation + 1:27:51 Meditation start

- 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.

Clients should not be forced to depend on methods that they do not use.

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.

Classes should be open for extension, but closed for modification

A class should have a 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?

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?"

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.

Amazon Machine Learning Deprecated. Use SageMaker instead.

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.

It is a modern-day “memento mori”

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).

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.

Hot Reloading refers to the ability to automatically update a running web application when changes are made to the application’s code.

JupyterDash supports three approaches to displaying a Dash application during interactive development.

# Run app and display result inline in the notebookapp.run_server(mode='inline')

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.

You can also try it out, right in your browser, with binder.

Then, copy any Dash example into a Jupyter notebook cell and replace the dash.Dash class with the jupyter_dash.JupyterDash class.

from jupyter_dash import JupyterDash

import dash

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.

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

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.

So if GCP is so much better, why so many more 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.

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).

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.

I felt that performance was almost always better in GCP, for example copying from instances to buckets in GCP is INSANELY fast

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.

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 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.

At the time of writing this, there are 169 AWS products compared to 90 in GCP.

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”

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.

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.

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.

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.

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.

So be careful running editing a bash script that may be currently executing. It could execute an invalid command, or do something very 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.

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.

Cross-Site Request Forgery is a web security exploit where an attacker induces a victim to perform an action they didn’t mean to. In this case, the attacker tricked you into unintentionally transferring them money.

This might be super basic, but... assume positive intent.Your parent is not your enemy. Your teacher is not your enemy. Your boss is not your enemy. The other team at work is not your enemy.

We present results from technical experiments which reveal that WeChat communications conducted entirely among non-China-registered accounts are subject to pervasive content surveillance that was previously thought to be exclusively reserved for China-registered accounts.

So write with a combination of short, medium, and long sentences. Create a sound that pleases the reader’s ear. Don’t just write words. Write music.”

You can create estimation plots here at estimationstats.com, or with the DABEST packages which are available in R, Python, and Matlab.

Relative to conventional plots, estimation plots offer five key benefits:

For comparisons between 3 or more groups that typically employ analysis of variance (ANOVA) methods, one can use the Cumming estimation plot, which can be considered a variant of the Gardner-Altman plot.

Efron developed the bias-corrected and accelerated bootstrap (BCa bootstrap) to account for the skew whilst obtaining the central 95% of the distribution.

We can calculate the 95% CI of the mean difference by performing bootstrap resampling.

Shown above is a Gardner-Altman estimation plot.

Jitter plots avoid overlapping datapoints (i.e. datapoints with the same y-value) by adding a random factor to each point along the orthogonal x-axes.

Unfortunately, the boxplot still doesn't show all our data.

The barplot has several shortcomings, even though its use in academic journals is endemic.

Continuous Delivery of Deployment is about running as thorough checks as you can to catch issues on your code. Completeness of the checks is the most important factor. It is usually measured in terms code coverage or functional coverage of your tests. Catching errors early on prevents broken code to get deployed to any environment and saves the precious time of your test team.

Continuous Integration is a trade off between speed of feedback loop to developers and relevance of the checks your perform (build and test). No code that would impede the team progress should make it to the main branch.

A good CD build: Ensures that as many features as possible are working properly The faster the better, but it is not a matter of speed. A 30-60 minutes build is OK

A good CI build: Ensures no code that breaks basic stuff and prevents other team members to work is introduced to the main branch Is fast enough to provide feedback to developers within minutes to prevent context switching between tasks

The idea of Continuous Delivery is to prepare artefacts as close as possible from what you want to run in your environment. These can be jar or war files if you are working with Java, executables if you are working with .NET. These can also be folders of transpiled JS code or even Docker containers, whatever makes deploy shorter (i.e. you have pre built in advance as much as you can).

Continuous Delivery is about being able to deploy any version of your code at all times. In practice it means the last or pre last version of your code.

Continuous Integration is not about tools. It is about working in small chunks and integrating your new code to the main branch and pulling frequently.

The app should build and start Most critical features should be functional at all times (user signup/login journey and key business features) Common layers of the application that all the developers rely on, should be stable. This means unit tests on those parts.

Continuous Integration is all about preventing the main branch of being broken so your team is not stuck. That’s it. It is not about having all your tests green all the time and the main branch deployable to production at every commit.

If knowledge of one item makes it harder to remember another item, we have a case of memory interference.

Here again are the twenty rules of formulating knowledge.

If you see a word and immediately check it's translation, you'll hardly memorize it at all. If you try your best to recall what the word means before checking the translation, the chances of memorization are much better. It is much easier to memorize a word's meaning when you know how to pronounce it corretly, so get it right.  

Somewhere between too hard and too easy, there’s a sweet-spot where reviews are challenging enough to hold your interest, but not so hard that it feels like torture. When the challenge of reviews is just right, you’ll actually get a sense of accomplishment and a little jolt of dopamine as you do them. Our brains actually enjoy challenges as long as they aren’t too hard or too easy. As I see it, this level of challenge is where you want to be.

Researchers have found that reviews are more effective when they’re difficult. That is, if you have to work at remembering a card, it’ll have a stronger effect on your memory. The harder a review is, the more it boosts your memory. This is called “desirable difficulty” in the literature.

Kolejna zmienna - "Recency", czyli informacja o tym, jak dawno klient robił zakupy w sklepie.

df['Recency'] = (today - df.InvoiceDate)/np.timedelta64(1,'D')

abt = df.groupby(['CustomerID']).agg({'Recency':'min', 'MonetaryValue':'sum', 'InvoiceNo':'count'})

abt = df.groupby(['CustomerID']).agg({'Recency':'min', 'MonetaryValue':'sum', 'InvoiceNo':'count'})
abt.rename(columns = {'InvoiceNo':'Frequency'}, inplace = True)
abt = abt[['Recency', 'Frequency', 'MonetaryValue']]
abt.head()

Będę oceniać klientów kryteriami, jakie zakłada metoda RFM.

Usuwam brakujące wartości w zmiennej "CustomerID".

df = df[~df.CustomerID.isnull()]

df['CustomerID'] = df.CustomerID.astype(int)

df.drop(['Description', 'StockCode', 'Country'], axis = 1, inplace = True)

Sprawdzam braki danych.

print(str(round(df.isnull().any(axis=1).sum()/df.shape[0]*100,2))+'% obserwacji zawiera braki w danych.')

24.93% obserwacji zawiera braki w danych.

Buduję prosty data frame z podstawowymi informacjami o zbiorze.

summary = pd.DataFrame(df.dtypes, columns=['Dtype'])
summary['Nulls'] = pd.DataFrame(df.isnull().any())
summary['Sum_of_nulls'] = pd.DataFrame(df.isnull().sum())
summary['Per_of_nulls'] = round((df.apply(pd.isnull).mean()*100),2)
summary.Dtype = summary.Dtype.astype(str)
print(summary)

                      Dtype  Nulls  Sum_of_nulls  Per_of_nulls
InvoiceNo            object  False             0         0.000
StockCode            object  False             0         0.000
Description          object   True          1454         0.270
Quantity              int64  False             0         0.000
InvoiceDate  datetime64[ns]  False             0         0.000
UnitPrice           float64  False             0         0.000
CustomerID          float64   True        135080        24.930
Country              object  False             0         0.000

An animated step-by-step guide to improving your pie charts.

If you, on the other hand, want to go the student-like route (living in Wohngemeinschaft, not eating out too much) and try to save, you can easily live on 1,500-2,000 CHF per month and save the majority of your salary.

We are now cooperating with Credit Agricole Bank and Revolut - if you have already moved to Switzerland you can open a free bank account and get 100 CHF bonus - email us to get the bonus code.

120,000 CHF annually according to this calculator gets you 7,746.20 CHF net per month.

2) Rent only a room - it might be a good option if you come without family (in Switzerland it’s called living in a Wohngemeinschaft).

Choose health insurance (Krankenkasse) - in Switzerland you have to pay your health insurance separately (it’s not deducted from your salary). You can use the Comparis website to compare the options. You have 3 months to choose both the company and your franchise.

Other important things - if you plan to use public transport, we recommend you to buy the Half Fare card. It gives you a 50% discount on most public transport in Switzerland (it costs 185 CHF per year).

There are also some general expat groups like Zurich Together

WHERE clause introduces a condition on individual rows; HAVING clause introduces a condition on aggregations, i.e. results of selection where a single result, such as count, average, min, max, or sum, has been produced from multiple rows. Your query calls for a second kind of condition (i.e. a condition on an aggregation) hence HAVING works correctly. As a rule of thumb, use WHERE before GROUP BY and HAVING after GROUP BY. It is a rather primitive rule, but it is useful in more than 90% of the cases.

Scrum means that “you have to get certain things done with those two weeks.” Kanban means “do what you can do in two weeks.”

What people will say is that estimates are for planning – that their purpose is to figure out how long some piece of work is going to take, so that everybody can plan accordingly. In all my five years shipping stuff, I can only recall one project where things really worked that way.

Be explicit about the difference between hard deadlines

If you delegate all your IT security to the InfoSec, they will come up with draconian rules

you should always advocate for having a dedicated SRE if there’s any real risk of after-hours pages that are out of your control.

I try to write a unit test any time the expected value of a defect is non-trivial.

I’m defining an integration test as a test where you’re calling code that you don’t own

Which database technology to choose

I would use a serverless function when I have a relatively small and simple chunk of code that needs to run every once in a while.

Programming languages These will probably expose my ignorance pretty nicely.

Now, a couple of years later, my guidelines for JS are:

Consuming media (books, blogs, whatever) is not inherently a compounding thing. Only if you have some kind of method to reflect, to digest, to incorporate your knowledge into your thoughts. If there is anything valuable in this post, you, reader, will probably not benefit from it unless you do something active to “process” it immediately.

if you have an amazing manager at a shit company you’ll still have a shit time. In some ways, it’ll actually be worse. If they’re good at their job (including retaining you), they’ll keep you at a bad company for too long. And then they’ll leave, because they’re smart and competent. Maybe they’ll take you with them.

Some of the people in the company are your friends in the current context. It’s like your dorm in college.

It’s also okay to take risks. Staying at a company that’s slowly dying has its costs too. Stick around too long and you’ll lose your belief that you can build, that change is possible. Try not to learn the wrong habits.

I do think when a lot of managers realized they’ve hit their peak or comfort level, they then start to focus on playing politics instead of delivering results to hold onto their position. These are also the kind of managers who would only hire people less capable than them, for fear of being replaced.

“In practice people gravitate to, hire and promote individuals they like to be around, not people who demand accountability.”

Dr. Peter advises creative incompetence — pretending to be incompetent but doing it in an area or manner where it doesn’t actually impair your work.

Dr. Peter also surmised that “super competent” people tend to “disrupt the hierarchy.” I suppose that’s a nice way of saying you’ve made your boss look bad by being more capable.In such situations, you’ll probably find yourself deliberately suppressed or edged out sooner or later — for some stupid reason or blame pushing.

So if you’re a highly competent and aggressive individual, it’s best you find yourself a job in a startup, be an entrepreneur, or work in a company that needs turning around.

Dr. Peter also had another interesting theory about getting promoted. He considered working hard and improving your skill sets not as effective as something called pull promotion. That’s when you get promoted — faster than usual — when a mentor or patron pulls you up.No wonder there’s so much butt kissing in the corporate world. They must have read Dr. Peter’s research from the ‘60s.

competency doesn’t factor as much as likability in most corporate promotions, especially when the ship is smooth sailing.

Find a results-oriented job if you’re fiercely independent and opinionated. Climb the ladder in a big corporation if you’re highly diplomatic or a crowd-pleaser.

managers fail to see and address this problem is that they are used to looking at communication and assume it's a good thing. Because they see activity

A study conducted by Gloria Marks, a Professor of Informatics at the University of California, revealed that it takes us an average of 23 minutes and 15 seconds to refocus on a task after an interruption, and even when we do, we experience a decrease in productivity

It doesn't mean that we ignore all messages and only look up from our work when something is on fire – but the general expectation is that it's okay to not be immediately available to your teammates when you are focusing on your work

Working in an open office renders us even more vulnerable

Office hours are chunks of time that makers set aside for meetings, while the rest of the time they are free to go into a Do Not Disturb mode

People think it’s efficient to distribute information all at the same time to a bunch of people around a room. But it’s actually a lot less efficient than distributing it asynchronously by writing it up and sending it out and letting people absorb it when they’re ready to so it doesn’t break their days into smaller bits.”

it's a matter of culture. None of these rules would work if the management fails to see that makers need to follow a different schedule

context switching between communication and creative work only kills the quality of both

since most powerful people operate on the manager schedule, they're in a position to force everyone to adapt to their schedule

The most straightforward way to address this is to build a team knowledge base. Not only does that minimize the number of repetitive questions bounced around the office, it allows new team members to basically onboard themselves.

almost no organizations today support maker schedules

For managers, interruptions in the form of meetings, phone calls, and Slack notifications are normal. For someone on the maker schedule, however, even the slightest distraction can have a disruptive effect

Immediate response becomes the implicit expectation, with barely any barriers or restrictions in place

In our experience, the best way to prevent a useless meeting is to write up our goals and thoughts first. Despite working in the same office, our team at Nuclino has converted nearly all of our meetings into asynchronously written reports.

For many data scientists, the finished product of a work session is a business analysis. They need to show team members—who oftentimes aren’t technical—how their data became a specific recommendation or insight.

Work never ends. No matter how much you get done there will always be more. I see a lot of colleagues burn out because they think their extra effort will be noticed. Most managers appriciate it but do not promote their employees.

Don't focus too much on the salary. It's just one tiny part of the whole package.Your dev job pays your rent, food and savings. I assume that most dev jobs do this quite well.Beyond this, the main goal of a job is to increase your future market value, your professional network and to have fun. So. basically it's about how much you are worth in your next job and that you enjoy your time.A high salary doesn't help you if you do stuff which doesn't matter in a few years.

COVID-19 has spurred a shift to analyze things like supply chain disruptions, speech analytics, and filtering out pandemic-related behavior, such as binge shopping, Burtch Works says. Data teams are being asked to create new simulations for the coming recession, to create scorecards to track pandemic-related behavior, and add COVID-19 control variables to marketing attribution models, the company says.

Data scientists and data engineers have built-in job security relative to other positions as businesses transition their operations to rely more heavily on data, data science, and AI. That’s a long-term trend that is not likely to change due to COVID-19, although momentum had started to slow in 2019 as venture capital investments ebbed.

According to a Dice Tech Jobs report released in February, demand for data engineers was up 50% and demand for data scientists was up 32% in 2019 compared to the prior year.

70% async using Twist, Github, Paper25% sync using something like Zoom, Appear.in, or Google Meet5% physical meetings, e.g., annual company or team retreats

According to the Harvard Business Review article “Collaborative Overload”, the time employees spend on collaboration has increased by 50% over the past two decades. Researchers found it was not uncommon for workers to spend a full 80% of their workdays communicating with colleagues in the form of email (on which workers’ spend an average of six hours a day); meetings (which fill up 15 percent of a company’s time, on average); and more recently instant messaging apps (the average Slack user sends an average of 200 messages a day, though 1,000-message power users are “not the exception”)

we think the async culture is one of the core reasons why most of the people we’ve hired at Doist the past 5 years have stayed with us. Our employee retention is 90%+ — much higher than the overall tech industry. For example, even a company like Google — with its legendary campuses full of perks from free meals to free haircuts — has a median tenure of just 1.1 years. Freedom to work from anywhere at any time beats fun vanity perks any day, and it costs our company $0 to provide

I also recently took about 10 months off of work, specifically to focus on learning. It was incredible, and I don’t regret it financially. I would often get up at 6 in the morning or even earlier (which I never do) just from excitement about what I was going to learn about and accomplish in the day. Spending my time focused Only on what I was most interested in was incredibly rewarding.

I'm working for myself right now, but if one day I needed to go get a full-time job again, I would almost certainly not go to big tech again. I'd rather get paid a fifth of what I was doing, but do something that leaves me with some energy after I put in a day's work

more money comes at the cost of very high expectations and brutal deadlines

Second, in my experience working with ex-FAANG - these engineers, while they all tend to be very smart, tend to be borderline junior engineers in the real world. They simply don't know how to build or operate something without the luxury of the mature tooling that exists at FAANG. You may be in for a reality shock when you leave the FAANG bubble

Things to consider when crafting out Job Ads and descriptions

Truth be told, we found that most companies we worked with preferred to own the analytical backend.

Talented people flock to employers that promise to invest in their development whether they will stay at the company or not.

We want to learn, but we worry that we might not like what we learn. Or that learning will cost us too much. Or that we will have to give up cherished ideas.

The two things I really like about working for smaller places or starting a company is you get very direct access to users and customers and their problems, which means you can actually have empathy for what's actually going on with them, and then you can directly solve it. That cycle is so powerful, the sooner you learn how to make that cycle happen in your career, the better off you'll be. If you can make software and make software for other people, the outcome truly is hundreds of millions of dollars worth of value if you get it right. That's where I'm here to try and encourage you to do. I'm not really saying that you shouldn't go work at a big tech company. I am saying you should probably leave before it makes you soft. 

afternoons are spent reading/researching/online classes.This has really helped me avoid burn out. I go into the weekend less exhausted and more motivated to return on Monday and implement new stuff. It has also helped generate some inspiration for weekend/personal projects.

When People Work Together

Praca w Facebooku - doskonała znajomość JSa, React, zarządzanie projektem OSS na GitHubie, prowadzenie społeczności, pisanie dokumentacji i wpisów na blogu.Szkolenia - dobra znajomość JSa, React, tworzenie szkoleń (struktura, zadania, itd), uczenie i swobodne przekazywanie wiedzy, marketing, sprzedaż.Startupy - dobra znajomość JSa, React, praca w zespole, rozmawianie z klientami, analiza biznesowa, szybkie dowożenie MVP, praca w stresie i dziwnych strefach czasowych.

Defining what “time well spent” means to you and making space for these moments is one of the greatest gifts you can make to your future self.

Research shows that humans tend to do whatever it takes to keep busy, even if the activity feels meaningless to them. Dr Brené Brown from the University of Houston describes being “crazy busy” as a numbing strategy we use to avoid facing the truth of our lives.

A few takeaways

Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true.

There is increasing concern that most current published research findings are false.

golden rule: If someone calls saying they’re from your bank, just hang up and call them back — ideally using a phone number that came from the bank’s Web site or from the back of your payment card.

“When the representative finally answered my call, I asked them to confirm that I was on the phone with them on the other line in the call they initiated toward me, and so the rep somehow checked and saw that there was another active call with Mitch,” he said. “But as it turned out, that other call was the attackers also talking to my bank pretending to be me.”

It is difficult to choose a typical reading speed, research has been conducted on various groups of people to get typical rates, what you regularly see quoted is: 100 to 200 words per minute (wpm) for learning, 200 to 400 wpm for comprehension.

DevOps tools enable DevOps in organizations

While talking about DevOps, three things are important continuous integration, continuous deployment, and continuous delivery.

Basic prerequisites to learn DevOps

DevOps benefits

Operations in the software industry include administrative processes and support for both hardware and software for clients as well as internal to the company. Infrastructure management, quality assurance, and monitoring are the basic roles for operations.

I set it with a few clicks at Travis CI, and by creating a .travis.yml file in the repo

language: node_js
node_js: node

before_script:
  - npm install -g typescript
  - npm install codecov -g

script:
  - yarn lint
  - yarn build
  - yarn test
  - yarn build-docs

after_success:
  - codecov

I set it with a few clicks at Travis CI, and by creating a .travis.yml file in the repo

language: node_js
node_js: node

before_script:
  - npm install -g typescript
  - npm install codecov -g

script:
  - yarn lint
  - yarn build
  - yarn test
  - yarn build-docs

after_success:
  - codecov

Continuous integration makes it easy to check against cases when the code: does not work (but someone didn’t test it and pushed haphazardly), does work only locally, as it is based on local installations, does work only locally, as not all files were committed.

In Python, when trying to do a dubious operation, you get an error pretty soon. In JavaScript… an undefined can fly through a few layers of abstraction, causing an error in a seemingly unrelated piece of code.

function add(a,b) { return + (a + b) }
add(2,2)
add('2', 2)

With Codecov it is easy to make jest & Travis CI generate one more thing:

Continuous Deployment is the next step. You deploy the most up to date and production ready version of your code to some environment. Ideally production if you trust your CD test suite enough.

the limitations of the PPS

Although the PPS has many advantages over the correlation, there is some drawback: it takes longer to calculate.

How to use the PPS in your own (Python) project

The PPS clearly has some advantages over correlation for finding predictive patterns in the data. However, once the patterns are found, the correlation is still a great way of communicating found linear relationships.

Let’s compare the correlation matrix to the PPS matrix on the Titanic dataset.

Let’s use a typical quadratic relationship: the feature x is a uniform variable ranging from -2 to 2 and the target y is the square of x plus some error.

how do you normalize a score? You define a lower and an upper limit and put the score into perspective.

For a classification problem, always predicting the most common class is pretty naive. For a regression problem, always predicting the median value is pretty naive.

Let’s say we have two columns and want to calculate the predictive power score of A predicting B. In this case, we treat B as our target variable and A as our (only) feature. We can now calculate a cross-validated Decision Tree and calculate a suitable evaluation metric.

More often, relationships are asymmetric

there are many non-linear relationships that the score simply won’t detect. For example, a sinus wave, a quadratic curve or a mysterious step function. The score will just be 0, saying: “Nothing interesting here”. Also, correlation is only defined for numeric columns.

There are many types of CRDTs

Some of our main takeaways:CRDT literature can be relevant even if you're not creating a decentralized systemMultiplayer for a visual editor like ours wasn't as intimidating as we thoughtTaking time to research and prototype in the beginning really paid off

traditional approaches that informed ours — OTs and CRDTs

Sometimes it's interesting to explain some code (How many time you spend trying to figure out a regex pattern when you see one?), but, in 99% of the time, comments could be avoided.

When we talk about abstraction levels, we can classify the code in 3 levels: high: getAdress medium: inactiveUsers = Users.findInactives low: .split(" ")

The ideal is not to mix the abstraction levels in only one function.

"The Big Picture" is one of those things that people say a whole lot but can mean so many different things. Going through all of these articles, they tend to mean any (or all) of these things

In recent years we’ve also begun to see increasing interest in exploratory testing as an important part of the agile toolbox

Developing in the cloud