# Input Input: 123, Output: Input: 121, Output: Input: 111, Output: Input: 123454321, Output: Input 123123, Output: # Instruction Output true if input is a palindrome # Output Input: 123, Output: false Input: 121, Output: true Input: 111, Output: true Input: 123454321, Output: true Input 123123, Output: false

Using named arguments is nice for languages that support it, but this is not always a possibility. Even in Python, where time.sleep is defined with a single argument named secs, we can’t call sleep(secs=300) due to implementation reasons. In that case, we can give the value a name instead.Instead of this:time.sleep(300)Do this:sleep_seconds = 300 time.sleep(sleep_seconds)Now the code is unambiguous, and readable without having to consult the documentation.

defaults write NSGlobalDomain WebKitDeveloperExtras -bool true defaults write -g WebKitDeveloperExtras -bool YES

Core: like an engine in a car. The product is meaningless without it.Necessary: like a car’s spare wheel. It’s rarely used but when needed, its function decides the success of the system.Added value: like a car’s cup-holder. It’s nice to have but the product is perfectly usable without it.Unique Selling Point: the main reason people should buy your product instead of your rivals. For example, your car is the best off-road vehicle.

Here is an architecture diagram of the devices I’m currently using

Have you ever built an image only to realize that you actually need it on a user account other than root, requiring you to rebuild the image again in rootless mode? Or have you built an image on one machine but run containers on the image using multiple different machines? Now you need to set up an account on a registry, push the image to the registry, Secure Shell (SSH) to each device you want to run the image on, and then pull the image. The podman image scp command solves both of these annoying scenarios as quickly as they occur.

an onion address is a promise and a mechanism to assure that you are taking seriously the needs of the people who use Tor.

As mentioned earlier, PATCH requests should apply partial updates to a resource, whereas PUT replaces an existing resource entirely. It's usually a good idea to design updates around PATCH requests

Aside from using HTTP status codes that indicate the outcome of the request (success or error), when returning errors, always use a standardized error response that includes more detailed information on what went wrong.

https://api.averagecompany.com/v1/health https://api.averagecompany.com/health?api_version=1.0

When dealing with date and time, APIs should always return ISO 8601-formatted strings.

So, to summarize, don't ask "Any Java experts around?", but rather ask "How do I do [problem] with Java and [other relevant info]?"

You're asking people to take responsibility. You're questioning people's confidence in their abilities. You're also unnecessarily walling other people out. I often answer questions related to languages or libraries I have never used, because the answers are (in a programmer kind of way) common sense.

This isn’t MAIN_PRIORITY, so we aren’t going to do it until at least ESTIMATED_DONE_DATE. Right now our priority is MAIN_PRIORITY because of ONE_SENTENCE_JUSTIFICATION, and this is 100% our shipping focus. I agree this sounds like a really useful feature - once we finish MAIN_PRIORITY, should we consider dropping SECOND_PRIORITY and do it?

for debugging purposes, a good combination is --lf --trace which would start a debug session with pdb at the beginning of the last test that failed:

pytest -l

If you start pytest with --pdb, it will start a pdb debugging session right after an exception is raised in your test. Most of the time this is not particularly useful as you might want to inspect each line of code before the raised exception.

pytest --lf

pytest -x

pytest --collect-only

pytest test_validator.py::test_regular_email_validates

Apart from shared fixtures you could place external hooks and plugins or modifiers for the PATH used by pytest to discover tests and implementation code.

pytest can read its project-specific configuration from one of these files: pytest.ini tox.ini setup.cfg

To have the fixture actually be used by one of your test, you simply add the fixture’s name as an argument

But the problem with Poetry is arguably down to the way Docker’s build works: Dockerfiles are essentially glorified shell scripts, and the build system semantic units are files and complete command runs. There is no way in a normal Docker build to access the actually relevant semantic information: in a better build system, you’d only re-install the changed dependencies, not reinstall all dependencies anytime the list changed. Hopefully someday a better build system will eventually replace the Docker default. Until then, it’s square pegs into round holes.

Third, you can use poetry-dynamic-versioning, a plug-in for Poetry that uses Git tags instead of pyproject.toml to set your application’s version. That way you won’t have to edit pyproject.toml to update the version. This seems appealing until you realize you now need to copy .git into your Docker build, which has its own downsides, like larger images unless you’re using multi-stage builds.

But if you’re doing some sort of continuous deployment process where you’re continuously updating the version field, your Docker builds are going to be slow.

VCR.py works primarily via the @vcr decorator. You can import this decorator by writing: import vcr.

The VCR.py library records the responses from HTTP requests made within your unit tests. The first time you run your tests using VCR.py is like any previous run. But the after VCR.py has had the chance to run once and record, all subsequent tests are:Fast! No more waiting for slow HTTP requests and responses in your tests.Deterministic. Every test is repeatable since they run off of previously recorded responses.Offline-capable! Every test can now run offline.

 75% of people in the U.S. never tweet.On an average weeknight in January, just 1% of U.S. adults watched primetime Fox News (2.2 million). 0.5% tuned into MSNBC (1.15 million).Nearly three times more Americans (56%) donated to charities during the pandemic than typically give money to politicians and parties (21%).

The ENTRYPOINT specifies a command that will always be executed when the container starts. The CMD specifies arguments that will be fed to the ENTRYPOINT.

DevOps is an interesting case study for understanding MLOps for a number of reasons: It underscores the long period of transformation required for enterprise adoption.It shows how the movement is comprised of both tooling advances as well as shifts in cultural mindset at organizations. Both must march forward hand-in-hand.It highlights the emerging need for practitioners with cross-functional skills and expertise. Silos be damned.

MLOps today is in a very messy state with regards to tooling, practices, and standards. However, this is to be expected given that we are still in the early phases of broader enterprise machine learning adoption. As this transformation continues over the coming years, expect the dust to settle while ML-driven value becomes more widespread.

LXC, is a serious contender to virtual machines. So, if you are developing a Linux application or working with servers, and need a real Linux environment, LXC should be your go-to. Docker is a complete solution to distribute applications and is particularly loved by developers. Docker solved the local developer configuration tantrum and became a key component in the CI/CD pipeline because it provides isolation between the workload and reproducible environment.

# line containing 'cake' but not 'at' # same as: grep 'cake' table.txt | grep -v 'at' # with PCRE: grep -P '^(?!.*at).*cake' table.txt $ awk '/cake/ && !/at/' table.txt blue cake mug shirt -7

Jeżeli masz dylemat czy użyć CMD, czy ENTRYPOINT jako punkt startowy twojego kontenera, odpowiedz sobie na następujące pytanie.Czy zawsze moje polecenie MUSI się wykonać? Jeśli odpowiedź brzmi tak, użyj ENTRYPOINT. Co więcej, jeśli potrzebujesz przekazać dodatkowe parametry, które mogą być nadpisane podczas uruchomienia kontenera — użyj również instrukcji CMD.

More backlinks to you and your work: Being the teammate that contributes to the system of knowledge shared shows how much you care about the success of the organization. And, it does it in a way that helps you have more documented and attributable credibility for the value you create within your organization.

Writing supplants meetings: When there is good documentation around a meeting (briefs, meeting notes, etc.), meetings can be leaner and more productive because people don’t have to be in the room to know what’s happening. So, only those who are actively contributing to the discussion need to attend.

Without meeting notes and documentation, companies become reliant on unreliable verbal accounts, 1:1 updates, and needing to be in the room to get things done.

Scales everyone’s knowledge: think about how many people you interact with on a given work day. I’m talking about the real human to human kind where you relay your ideas to others. It’s probably in the 5-20 range. If you put those same ideas into a doc or an email, your distribution goes to infinity. Anyone can read it.

== and != for string comparison -eq, -ne, -gt, -lt, -le -ge for numerical comparison

> will overwrite the current contents of the file, if the file already exists. If you want to append lines instead, use >>

The syntax for “redirecting” some output to stderr is >&2. > means “pipe stdout into” whatever is on the right, which could be a file, etc., and &2 is a reference to “file descriptor #2” which is stderr.

single quotes, which don’t expand variables

This only works if you happen to have Bash installed at /bin/bash. Depending on the operating system and distribution of the person running your script, that might not necessarily be true! It’s better to use env, a program that finds an executable on the user’s PATH and runs it.

It isn't about note taking, it's about supporting a brain that simply isn't capable of retaining the level of information that we have to deal with. The ultimate note taking device is actually an augmented human brain that has perfect recollection and organisation.

In Japan, the country where the first emoji sets originated, red is traditionally used to represent increases in the value of a stock. Meanwhile, green is used to represent decreases in stock value.

the curse of knowledge. It’s a simple but devastating effect: Once we know something, it’s very difficult to imagine not knowing it, or to take the perspective of someone who doesn't.

Adopting Kubernetes-native environments ensures true portability for the hybrid cloud. However, we also need a Kubernetes-native framework to provide the "glue" for applications to seamlessly integrate with Kubernetes and its services. Without application portability, the hybrid cloud is relegated to an environment-only benefit. That framework is Quarkus.

Kubernetes-native is a specialization of cloud-native, and not divorced from what cloud native defines. Whereas a cloud-native application is intended for the cloud, a Kubernetes-native application is designed and built for Kubernetes.

According to Wilder, a cloud-native application is any application that was architected to take full advantage of cloud platforms. These applications: Use cloud platform services. Scale horizontally. Scale automatically, using proactive and reactive actions. Handle node and transient failures without degrading. Feature non-blocking asynchronous communication in a loosely coupled architecture.

two main problems with framing decisions and policies in terms of usefulness: (1) being useful is not always to our own benefit – sometimes, we are being used as a means to someone else’s end, and we end up miserable as a result; and (2) the lenses themselves of usefulness and uselessness can obscure our view of the good life.

The script is in batch with some portions of powershell. The base code is fairly simple and most of it came from Googling ".bat transfer files" followed by ".bat how to only transfer certain file types" etc. The trick was making it work with my office, knowing where to scan for new files, knowing where not to scan due to lag (seriously, if you have a folder with 200,000 .txt files that crap will severally slow down your scans. Better to move it manually and then change the script to omit that folder from future searches)

It essentially scans the on-site drive for any new files, generates hash values for them, transfers them to the Cloud, then generates hash values again for fidelity (in court you have to prove digital evidence hasn't been tampered with).

Salesforce has a unique use case where they need to serve 100K-500K models because the Salesforce Einstein product builds models for every customer. Their system serves multiple models in each ML serving framework container. To avoid the noisy neighbor problem and prevent some containers from taking significantly more load than others, they use shuffle sharding [8] to assign models to containers. I won’t go into the details and I recommend watching their excellent presentation in [3].

Batching predictions can be especially beneficial when running neural networks on GPUs since batching takes better advantage of the hardware.

Inference Service — provides the serving API. Clients can send requests to different routes to get predictions from different models. The Inference Service unifies serving logic across models and provides easier interaction with other internal services. As a result, data scientists don’t need to take on those concerns. Also, the Inference Service calls out to ML serving containers to obtain model predictions. That way, the Inference Service can focus on I/O-bound operations while the model serving frameworks focus on compute-bound operations. Each set of services can be scaled independently based on their unique performance characteristics.

Provide a model config file with the model’s input features, the model location, what it needs to run (like a reference to a Docker image), CPU & memory requests, and other relevant information.

what changes when you need to deploy hundreds to thousands of online models? The TLDR: much more automation and standardization.

“Shadow Mode” or “Dark Launch” as Google calls it is a technique where production traffic and data is run through a newly deployed version of a service or machine learning model, without that service or model actually returning the response or prediction to customers/other systems. Instead, the old version of the service or model continues to serve responses or predictions, and the new version’s results are merely captured and stored for analysis.

you can also mount different FastAPI applications within the FastAPI application. This would mean that every sub-FastAPI application would have its docs, would run independent of other applications, and will handle its path-specific requests. To mount this, simply create a master application and sub-application file. Now, import the app object from the sub-application file to the master application file and pass this object directly to the mount function of the master application object.

There are officially 5 types of UUID values, version 1 to 5, but the most common are: time-based (version 1 or version 2) and purely random (version 3). The time-based UUIDs encode the number of 10ns since January 1st, 1970 in 7.5 bytes (60 bits), which is split in a “time-low”-“time-mid”-“time-hi” fashion. The missing 4 bits is the version number used as a prefix to the time-hi field.  This yields the 64 bits of the first 3 groups. The last 2 groups are the clock sequence, a value incremented every time the clock is modified and a host unique identifier.

Even though most posts are warning people against the use of UUIDs, they are still very popular. This popularity comes from the fact that these values can easily be generated by remote devices, with a very low probability of collision.

This basic example compiles a simple Go program. The naive way on the left results in a 961 MB image. When using a multi-stage build, we copy just the compiled binary which results in a 7 MB image.

# Image size: 7 MB

FROM golang:1.17.5 as builder

WORKDIR /workspace
COPY . .
ENV CGO_ENABLED=0
RUN go get && go build -o main .

FROM scratch
WORKDIR /workspace
COPY --from=builder \
     /workspace/main \
     /workspace/main

CMD ["/workspace/main"]

Docker introduced multi-stage builds starting from Docker Engine v17.05. This allows us to perform all preparations steps as before, but then copy only the essential files or output from these steps.

Making a small change to a file or moving it will create an entire copy of the file. Deleting a file will only hide it from the final image, but it will still exist in its original layer, taking up space. This is all a result of how images are structured as a series of read-only layers. This provides reusability of layers and efficiencies with regards to how images are stored and executed. But this also means we need to be aware of the underlying structure and take it into account when we create our Dockerfile.

In this example, we created 3 copies of our file throughout different layers of the image. Despite removing the file in the last layer, the image still contains the file in other layers which contributes to the overall size of the image.

FROM debian:bullseye

COPY somefile.txt . #1

# Small change but entire file is copied
RUN echo "more data" >> somefile.txt #2

# File moved but layer now contains an entire copy of the file
RUN mv somefile.txt somefile2.txt #3

# File won't exist in this layer,
# but it still takes up space in the previous ones.
RUN rm somefile2.txt

We’re just chmod'ing an existing file, but Docker can’t change the file in its original layer, so that results in a new layer where the file is copied in its entirety with the new permissions.In newer versions of Docker, this can now be written as the following to avoid this issue using Docker’s BuildKit:

FROM debian:bullseye

COPY somefile.txt .

RUN chmod 777 somefile.txt

FROM debian:bullseye

COPY --chmod=777 somefile.txt .

when you make changes to files that come from previous layers, they’re copied into the new layer you’re creating.

Many processes will create temporary files, caches, and other files that have no benefit to your specific use case. For example, running apt-get update will update internal files that you don’t need to persist because you’ve already installed all the packages you need. So we can add rm -rf /var/lib/apt/lists/* as part of the same layer to remove those (removing them with a separate RUN will keep them in the original layer, see “Avoid duplicating files”). Docker recognize this is an issue and went as far as adding apt-get clean automatically for their official Debian and Ubuntu images.

An important way to ensure you’re not bringing in unintended files is to define a .dockerignore file.

# Ignore git and caches
.git
.cache

# Ignore logs
logs

# Ignore secrets
.env

# Ignore installed dependencies
node_modules

...

You can save any local image as a tar archive and then inspect its contents.

bash-3.2$ docker save <image-digest> -o image.tar
bash-3.2$ tar -xf image.tar -C image
bash-3.2$ cd image
bash-3.2$ tar -xf <layer-digest>/layer.tar
bash-3.2$ ls

Technology → Cool Stuff to Work OnIntellection → Smart People to Work WithCertainty → Repeatability in Work Environment

Smalltalk image is unlike a collection of Java class files in that it can store your programs (both source and compiled bytecode), their data, and execution state. You can quit and save your work while some code is executing, move your image to an entirely different machine, load your image… and your program picks up where it left off.

Smalltalk has a virtual machine which allows you to execute your code on any platform where the VM can run. The image however is not only all your code, but the entire Smalltalk system, including said virtual machine (because of course it’s written in itself).

The most important thing you can do is provide actionable feedback. This means being specific about your observations of their work. It also means providing direction about what they could have done differently or what they need to learn or practice in order to improve. If your mentee doesn’t know what to do next, your feedback wasn’t actionable.

So when the morale on the team dips (usually related to lack of clarity or setbacks making things take a long time) people leave the team, which further damages morale and can easily result in a mass exodus.

People come and go (which has the net impact of making you feel like you are just a resource)

Instead of “I have a type, it’s called MyType, it has a constructor, in the constructor I assign the property ‘A’ to the parameter ‘A’ (and so on)”, you say “I have a type, it’s called MyType, it has an attribute called a”

attrs lets you declare the fields on your class, along with lots of potentially interesting metadata about them, and then get that metadata back out.

>>> Point3D(1, 2, 3) == Point3D(1, 2, 3)

    def __eq__(self, other):
        if not isinstance(other, self.__class__):
            return NotImplemented
        return (self.x, self.y, self.z) == (other.x, other.y, other.z)
    def __lt__(self, other):
        if not isinstance(other, self.__class__):
            return NotImplemented
        return (self.x, self.y, self.z) < (other.x, other.y, other.z)

>>> Point3D(1, 2, 3)

def __repr__(self):
    return (self.__class__.__name__ +
        ("(x={}, y={}, z={})".format(self.x, self.y, self.z)))

Look, no inheritance! By using a class decorator, Point3D remains a Plain Old Python Class (albeit with some helpful double-underscore methods tacked on, as we’ll see momentarily).

class Point3D(object):
    def __init__(self, x, y, z):
        self.x = x
        self.y = y
        self.z = z

import attr
@attr.s
class Point3D(object):
    x = attr.ib()
    y = attr.ib()
    z = attr.ib()

The best way to improve your ability to think is to actually spend time thinking.

Thinking means concentrating on one thing long enough to develop an idea about it.

This runs a loop 555 times. Takes a screenshot, names it for the loop number with padded zeros, taps the bottom right of the screen, then waits for a second to ensure the page has refreshed. Slow and dull, but works reliably.

#!/bin/bash
for i in {00001..00555}; do
   adb exec-out screencap -p > $i.png
   adb shell input tap 1000 2000
   sleep 1s
done
echo All done

the need for multiple outputs (FL ASIO) vs better performance (ASIO4ALL)

Gary Klein himself has made a name developing techniques for extracting pieces of tacit knowledge and making it explicit. (The technique is called ‘The Critical Decision Method’, but it is difficult to pull off because it demands expertise in CDM itself).

If you are a knowledge worker, tacit knowledge is a lot more important to the development of your field of expertise than you might think.

Notice how little verbal instruction is involved. What is more important is emulation, and action — that is, a focus on the embodied feelings necessary to ride a bicycle successfully. And this exercise was quite magical for me, for within the span of an hour I could watch a kid go from conscious incompetence to conscious competence and finally to unconscious competence.In other words, tacit knowledge instruction happens through things like imitation, emulation, and apprenticeship. You learn by copying what the master does, blindly, until you internalise the principles behind the actions.

When I was a kid, I taught myself how to ride a bike … by accident. And then I taught my sisters and then my cousin and then another kid in the neighbourhood who was interested but a little scared. They were zooming around in about an hour each. The steps were as follows:

Tacit knowledge is knowledge that cannot be captured through words alone.

Artifactory/Nexus/Docker repo was unavailable for a tiny fraction of a second when downloading/uploading packagesThe Jenkins builder randomly got stuck

Microservices can really bring value to the table, but the question is; at what cost? Even though the promises sound really good, you have more moving pieces within your architecture which naturally leads to more failure. What if your messaging system breaks? What if there’s an issue with your K8S cluster? What if Jaeger is down and you can’t trace errors? What if metrics are not coming into Prometheus?

If you’re going with a microservice:

Let’s take a simple online store app as an example.

some of the pros for going microservices

I think one of the ways that remote work changes this is that I can do other things while I think through a tricky problem; I can do dishes or walk my dog or something instead of trying to look busy in a room with 6-12 other people who are furiously typing because that's how the manager and project manager understand that work gets done.

docker scan elastic/logstash:7.13.3 | grep 'Arbitrary Code Execution'

AAX is Pro Tools' exclusive format for virtual synths. So you really only need it if you use Pro Tools (which doesn't support the VST format).

When you usually try to download an image, your browser opens a connection to the server and sends a GET request asking for the image. The server responds with the image and closes the connection. Here however, the server sends the image, but doesn't close the connection! It keeps the connection open and sends bits of data periodically to make sure it stays open. So your browser thinks that the image is still being sent over, which is why the download seems to be going on infinitely.

be microfamous. Microfame is the best kind of fame, because it combines an easier task (be famous to fewer people) with a better outcome (be famous to the right people).

:w !sudo tee %

Windows 10 Enterprise LTSC 2021 (tak brzmi pełna, poprawna nazwa wersji 21H2 LTSC) będzie otrzymywał aktualizacje do stycznia 2032.

Doświadczeni użytkownicy wiedzieli jednak, że należy czekać na Server. W przypadku Windows 11, wydaje się że należy czekać na wersję LTSC.

if it's important enough it will surface somewhere somehow.

The Feynman technique goes as follows: write down what you know about a subject. Explain it in words simple enough for a 6th grader to understand. Identify any gaps in your knowledge and read up on that again until all of the explanation is dead simple and short.

Similar to frequency lists in a natural language there are concepts in any software product that the rest of the software builds upon. I'll call them core concepts. Let's use git as an example. Three core concepts are commits, branches and conflicts.If you know these three core concepts you can proceed with further learning.

We can also see that converting the original non-dithered image to WebP gets a much smaller file size, and the original aesthetic of the image is preserved.

A lot of us are going to die of unpredictable diseases, some of us young. Really, don't spend your life getting fitter, healthier, more productive. We are all going to die, and Earth will explode in the Sun in a few billion years: please, enjoy some now.

I’d probably choose the official Docker Python image (python:3.9-slim-bullseye) just to ensure the latest bugfixes are always available.

So which should you use? If you’re a RedHat shop, you’ll want to use their image. If you want the absolute latest bugfix version of Python, or a wide variety of versions, the official Docker Python image is your best bet. If you care about performance, Debian 11 or Ubuntu 20.04 will give you one of the fastest builds of Python; Ubuntu does better on point releases, but will have slightly larger images (see above). The difference is at most 10% though, and many applications are not bottlenecked on Python performance.

There are three major operating systems that roughly meet the above criteria: Debian “Bullseye” 11, Ubuntu 20.04 LTS, and RedHat Enterprise Linux 8.

While people who are both trustworthy and competent are the most sought after when it comes to team assembly, friendliness and trustworthiness are often more important factors than competency.

The researchers found that people who exhibited both competence, through the use of challenging voice, and trustworthiness, through the use of supportive voice, were the most in-demand people when it came to assembling teams.

Feature GraphQL REST

There are advantages and disadvantages to both systems, and both have their use in modern API development. However, GraphQL was developed to combat some perceived weaknesses with the REST system, and to create a more efficient, client-driven API.

special permission bit at the end here t, this means everyone can add files, write files, modify files in the /tmp directory, but only root can delete the /tmp directory

We implemented a bash script to be installed in the master node of the EMR cluster, and the script is scheduled to run every 5 minutes. The script monitors the clusters and sends a CUSTOM metric EMR-INUSE (0=inactive; 1=active) to CloudWatch every 5 minutes. If CloudWatch receives 0 (inactive) for some predefined set of data points, it triggers an alarm, which in turn executes an AWS Lambda function that terminates the cluster.

git ls-files is more than 5 times faster than both fd --no-ignore and find

If we call this using Bash, it never gets further than the exec line, and when called using Python it will print lol as that's the only effective Python statement in that file.

#!/bin/bash
"exec" "python" "myscript.py" "$@"
print("lol")

For Python the variable assignment is just a var with a weird string, for Bash it gets executed and we store the result.

There are a handful of tools that I used to use and now it’s narrowed down to just one or two: pandas-profiling and Dataiku for columnar or numeric data - here’s some getting started tips. I used to also load the data into bamboolib but the purpose of such a tool is different. For text data I have written my own profiler called nlp-profiler.

If for some reason you don’t see a running pod from this command, then using kubectl describe po a is your next-best option. Look at the events to find errors for what might have gone wrong.

As with listing nodes, you should first look at the status column and look for errors. The ready column will show how many pods are desired and how many are running.

-o wide option will tell us additional details like operating system (OS), IP address and container runtime. The first thing you should look for is the status. If the node doesn’t say “Ready” you might have a problem, but not always.

This command will be the easiest way to discover if your scheduler, controller-manager and etcd node(s) are healthy.

If something broke recently, you can look at the cluster events to see what was happening before and after things broke.

this command will tell you what CRDs (custom resource definitions) have been installed in your cluster and what API version each resource is at. This could give you some insights into looking at logs on controllers or workload definitions.

kubectl get --raw '/healthz?verbose'

Here are the eight commands to run

kubectl version --short
kubectl cluster-info
kubectl get componentstatus
kubectl api-resources -o wide --sort-by name
kubectl get events -A
kubectl get nodes -o wide
kubectl get pods -A -o wide
kubectl run a --image alpine --command -- /bin/sleep 1d

80% of developers are "dark", they dont write or speak or participate in public tech discourse.

They'll teach you for free. Most people don't see what's right in front of them. But not you. "With so many junior devs out there, why will they help me?", you ask. Because you learn in public. By teaching you, they teach many. You amplify them.

Try your best to be right, but don't worry when you're wrong. Repeatedly. If you feel uncomfortable, or like an impostor, good. You're pushing yourself. Don't assume you know everything, but try your best anyway, and let the internet correct you when you are inevitably wrong. Wear your noobyness on your sleeve.

start building a persistent knowledge base that grows over time. Open Source your Knowledge! At every step of the way: Document what you did and the problems you solved.

Whatever your thing is, make the thing you wish you had found when you were learning. Don't judge your results by "claps" or retweets or stars or upvotes - just talk to yourself from 3 months ago.

For example instead of "the team were incredibly frustrating to work with and ignored all my suggestions", instead "the culture made it very hard to do quality engineering, which over time was demoralising"

Given all that, I simply do not understand why people keep recommending the {} syntax at all. It's a rare case where you'd want all the associated issues. Essentially, the only "advantage" of not running your functions in a subshell is that you can write to global variables. I'm willing to believe there are cases where that is useful, but it should definitely not be the default.

All we've done is replace the {} with (). It may look like a benign change, but now, whenever that function is invoked, it will be run within a subshell.

Coaching is external guidance and feedback on your performance.

Dogadać się z firmami z podobnej branży i umieszczać u siebie zdjęcia ich produktów, bez żadnego kodu. Napisać czasem (odpowiednio oznaczony) artykuł sponsorowany. Dodać link do bezpośrednich wpłat na swoje konto. Pomysłów jest multum. Niestety wielu wybrało najłatwiejszą opcję i podpięcie się pod globalne sieci reklamowe. Niekoniecznie zyskują na tym „partnerstwie”. Elementy reklamowe zbierają informacje o użytkownikach nawet jeśli ich nie klikniemy (a zatem i tak nie przyniesiemy zarobków właścicielom stron).

I’ll recap the steps in case you got lost. I start with the assumption that I’ve already downloaded the invite.ics file.

border collies—could recall and retrieve at least 10 toys they had been taught the names of. One overachiever named Whisky correctly retrieved 54 out of 59 toys he had learned to identify.

those capable of quickly memorizing multiple toy names—shows they often tilt their heads before correctly retrieving a specific toy. That suggests the behavior might be a sign of concentration and recall in our canine pals, the team suggests.

x() is the same as doing x.__call__()

How do you even begin to check if you can try and “call” a function, class, and whatnot? The answer is actually quite simple: You just see if the object implements the __call__ special method.

Python is referred to as a “duck-typed” language. What it means is that instead of caring about the exact class an object comes from, Python code generally tends to check instead if the object can satisfy certain behaviours that we are looking for.

everything is stored inside dictionaries. And the vars method exposes the variables stored inside objects and classes.

The overall flow of Gitflow is: A develop branch is created from main A release branch is created from develop Feature branches are created from develop When a feature is complete it is merged into the develop branch When the release branch is done it is merged into develop and main If an issue in main is detected a hotfix branch is created from main Once the hotfix is complete it is merged to both develop and main

Hotfix branches are a lot like release branches and feature branches except they're based on main instead of develop. This is the only branch that should fork directly off of main. As soon as the fix is complete, it should be merged into both main and develop (or the current release branch), and main should be tagged with an updated version number.

Once develop has acquired enough features for a release (or a predetermined release date is approaching), you fork a release branch off of develop. Creating this branch starts the next release cycle, so no new features can be added after this point—only bug fixes, documentation generation, and other release-oriented tasks should go in this branch. Once it's ready to ship, the release branch gets merged into main and tagged with a version number. In addition, it should be merged back into develop, which may have progressed since the release was initiated.

feature branches use develop as their parent branch. When a feature is complete, it gets merged back into develop. Features should never interact directly with main.

When using the git-flow extension library, executing git flow init on an existing repo will create the develop branch

The main condition that needs to be satisfied in order to use OneFlow is that every new production release is based on the previous release. The most difference between One Flow and Git Flow that it not has develop branch.

The most difference between GitLab Flow and GitHub Flow are the environment branches having in GitLab Flow (e.g. staging and production) because there will be a project that isn’t able to deploy to production every time you merge a feature branch

4 branching workflows for Git

release-* — release branches support preparation of a new production release. They allow many minor bug to be fixed and preparation of meta-data for a release. May branch off from develop and must merge into master anddevelop.

hotfix-* — hotfix branches are necessary to act immediately upon an undesired status of master. May branch off from master and must merge into master anddevelop.

feature-* — feature branches are used to develop new features for the upcoming releases. May branch off from develop and must merge into develop.

The main idea behind the space-based pattern is the distributed shared memory to mitigate issues that frequently occur at the database level. The assumption is that by processing most of operations using in-memory data we can avoid extra operations in the database, thus any future problems that may evolve from there (for example, if your user activity data entity has changed, you don’t need to change a bunch of code persisting to & retrieving that data from the DB).The basic approach is to separate the application into processing units (that can automatically scale up and down based on demand), where the data will be replicated and processed between those units without any persistence to the central database (though there will be local storages for the occasion of system failures).

Microservices architecture consists of separately deployed services, where each service would have ideally single responsibility. Those services are independent of each other and if one service fails others will not stop running.

First of all, if you know the basics of architecture patterns, then it is easier for you to follow the requirements of your architect. Secondly, knowing those patterns will help you to make decisions in your code

Mikrokernel Architecture, also known as Plugin architecture, is the design pattern with two main components: a core system and plug-in modules (or extensions). A great example would be a Web browser (core system) where you can install endless extensions (or plugins).

The idea behind this pattern is to decouple the application logic into single-purpose event processing components that asynchronously receive and process events. This pattern is one of the popular distributed asynchronous architecture patterns known for high scalability and adaptability.

It is the most common architecture for monolithic applications. The basic idea behind the pattern is to divide the app logic into several layers each encapsulating specific role. For example, the Persistence layer would be responsible for the communication of your app with the database engine.

My favourite tactic is to ask a yes/no question. What I love about this is that there’s a much lower chance that the person answering will go off on an irrelevant tangent – they’ll almost always say something useful to me.

instead of finding someone who can easily give a clear explanation, I just need to find someone who has the information I want and then ask them specific questions until I’ve learned what I want to know. And I’ve found that most people really do want to be helpful, so they’re very happy to answer questions. And if you get good at asking questions, you can often find a set of questions that will get you the answers you want pretty quickly, so it’s a good use of everyone’s time!

A screenshot from the document providing an overview of different data retention periods. Image: Motherboard.

$@ is all of the parameters passed to the script. For instance, if you call ./someScript.sh foo bar then $@ will be equal to foo bar.

Go ahead and Clear Data so you can start Onenote. Before you close Onenote click on the Sticky Notes button then close Onenote. Onenote will now open normally. If you forget to click on the Sticky Notes button Onenote will break and it wont start again. When this happens you'll need to Clear Data to get Onenote started. I use Onenote everyday and something changed last night.

indent=True here is treated as indent=1, so it works, but I’m pretty sure nobody would intend that to mean an indent of 1 space

bool is actually not a primitive data type — it’s actually a subclass of int!

complex is a supertype of float, which, in turn, is a supertype of int.

Now since the “compiling to bytecode” step above takes a noticeable amount of time when you import a module, Python stores (marshalls) the bytecode into a .pyc file, and stores it in a folder called __pycache__. The __cached__ parameter of the imported module then points to this .pyc file.When the same module is imported again at a later time, Python checks if a .pyc version of the module exists, and then directly imports the already-compiled version instead, saving a bunch of time and computation.

Bytecode is a set of micro-instructions for Python’s virtual machine. This “virtual machine” is where Python’s interpreter logic resides. It essentially emulates a very simple stack-based computer on your machine, in order to execute the Python code written by you.

Python is compiled. In fact, all Python code is compiled, but not to machine code — to bytecode

Python always runs in debug mode by default.The other mode that Python can run in, is “optimized mode”. To run python in “optimized mode”, you can invoke it by passing the -O flag. And all it does, is prevents assert statements from doing anything (at least so far), which in all honesty, isn’t really useful at all.

np = __import__('numpy') # Same as doing 'import numpy as np'

This refers to the module spec. It contains metadata such as the module name, what kind of module it is, as well as how it was created and loaded.

let’s say you only want to support integer addition with this class, and not floats. This is where you’d use NotImplemented

class MyNumber:
    def __add__(self, other):
        if isinstance(other, float):
            return NotImplemented

        return other + 42

__radd__ operator, which adds support for right-addition

class MyNumber:
    def __add__(self, other):
        return other + 42

    def __radd__(self, other):
        return other + 42

Now I should mention that all objects in Python can add support for all Python operators, such as +, -, +=, etc., by defining special methods inside their class, such as __add__ for +, __iadd__ for +=, and so on.

class MyNumber:
    def __add__(self, other):
        return other + 42

>>> num = MyNumber()
>>> num + 3
45

NotImplemented is used inside a class’ operator definitions, when you want to tell Python that a certain operator isn’t defined for this class.

Doing that would even catch KeyboardInterrupt, which would make you unable to close your program by pressing Ctrl+C.

every exception is a subclass of BaseException, and nearly all of them are subclasses of Exception, other than a few that aren’t supposed to be normally caught.

print(dir(__builtins__))

builtin scope in Python:It’s the scope where essentially all of Python’s top level functions are defined, such as len, range and print.When a variable is not found in the local, enclosing or global scope, Python looks for it in the builtins.

Global scope (or module scope) simply refers to the scope where all the module’s top-level variables, functions and classes are defined.

you can use the nonlocal keyword in Python to tell the interpreter that you don’t mean to define a new variable in the local scope, but you want to modify the one in the enclosing scope.

The enclosing scope (or nonlocal scope) refers to the scope of the classes or functions inside which the current function/class lives.

The local scope refers to the scope that comes with the current function or class you are in.

A builtin in Python is everything that lives in the builtins module.