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.

in Python 3.0 (alongside 2.6), A new method was added to the str data type: str.format. Not only was it more obvious in what it was doing, it added a bunch of new features, like dynamic data types, center alignment, index-based formatting, and specifying padding characters.

So, while DELETE operations are free, LIST operations (to get a list of objects) are not free (~$.005 per 1000 requests, varying a bit by region).

TypedDict is a dictionary whose keys are always string, and values are of the specified type. At runtime, it behaves exactly like a normal dictionary.

you should only use reveal_type to debug your code, and remove it when you’re done debugging.

What this says is “function double takes an argument n which is an int, and the function returns an int.

This tells mypy that nums should be a list of integers (List[int]), and that average returns a float.

from typing import List

def average(nums: List[int]) -> float:

for starters, use mypy --strict filename.py

few battle-hardened options, for instance: Airflow, a popular open-source workflow orchestrator; Argo, a newer orchestrator that runs natively on Kubernetes, and managed solutions such as Google Cloud Composer and AWS Step Functions.

To make ML applications production-ready from the beginning, developers must adhere to the same set of standards as all other production-grade software. This introduces further requirements:

In contrast, a defining feature of ML-powered applications is that they are directly exposed to a large amount of messy, real-world data which is too complex to be understood and modeled by hand.

As a character, I believe that I am quite a responsible and hardworking person. The combination of the two becomes dangerous when you lose the measure of how much you should work to get a job done right.

UPDATE--SHA-1, the 25-year-old hash function designed by the NSA and considered unsafe for most uses for the last 15 years, has now been “fully and practically broken” by a team that has developed a chosen-prefix collision for it.

So, what’s a better way to illustrate JOIN operations? JOIN diagrams!

State of Data Science and Machine Learning 2021

iOS 15.0 introduces a new feature: an iPhone can be located with Find My even while the iPhone is turned "off"

Argo Workflow is part of the Argo project, which offers a range of, as they like to call it, Kubernetes-native get-stuff-done tools (Workflow, CD, Events, Rollouts).

Argo is designed to run on top of k8s. Not a VM, not AWS ECS, not Container Instances on Azure, not Google Cloud Run or App Engine. This means you get all the good of k8s, but also the bad.

If you are already heavily invested in Kubernetes, then yes look into Argo Workflow (and its brothers and sisters from the parent project).The broader and harder question you should ask yourself is: to go full k8s-native or not? Look at your team’s cloud and k8s experience, size, growth targets. Most probably you will land somewhere in the middle first, as there is no free lunch.

In order to reduce the number of lines of text in Workflow YAML files, use WorkflowTemplate . This allow for re-use of common components.

Wet behind the ears: the ones that have just started working as a professional programmers and need lots of guidance. Sort of knows what they are doing: professional programmers, who have sort of figured what is going on and can mostly finish their tasks on their own. Experienced: the ones who have been around the block for a while and are most likely the brains behind all the major design decisions of a large software project. Coworkers usually turn to them when they need advice for a hard technical problem. Some programmers will never reach this level, despite their official title stating otherwise (but more on that later).

The problem with the first approach is that it considers software development to be a deterministic process when, in fact, it’s stochastic. In other words, you can’t accurately determine how long it will take to write a particular piece of code unless you have already written it.

Reading books, being aware of the curiosity gap, and asking a lot of questions:

The difference between smart and curious people and only smart people is that curiosity helps you move forward in life. If you shut the door to curiosity. You shut the door to learning. And when you don’t learn. You don’t move forward. You must be curious to learn. Otherwise, you won’t even consider learning.

Smart people become even smarter because they are smart enough to understand that they don’t have all the answers.

You probably shouldn't use Alpine for Python projects, instead use the slim Docker image versions.

Cost — it’s by far the most affordable headset in its class, and while I have a tendency to be lavish with my gadgetry I’m still a cheapskate: I love a good deal and a favorably skewed cost/benefit ratio even more.

Adapting to the new environment is immediate, like moving between rooms, and since the focal length in the headset matches regular human vision there’s no acclimating or adjustment.

I do highly contextual work, with multiple work orders and their histories open, supporting reference documentation, API specifications, several areas of code (and calls in the stack), tests, logs, databases, and GUIs — plus Slack, Spotify, clock, calendar, and camera feeds. I tend to only look at 25% of that at once, but everything is within a comfortable glance without tabbing between windows. Protecting that context and augmenting my working memory maintains my flow.

Realism will increase (perhaps to hyperrealism) and our ability to perceive and interact with simulated objects and settings will be indistinguishable to our senses. Acting in simulated contexts will have physical consequences as systems interpret and project actions into the world — telepresence will take a quantum leap, removing limitations of time and distance. Transcending today’s drone piloting, remote surgery, etc., we will see through remote eyes and work through remote hands anywhere.

It has a ton of promise, but… I don’t really care for the promise it’s making. 100% of what you can do in Workrooms is feasible in a physical setting, although it would be really expensive (lots of smart hardware all over the place). But that’s the thing: it’s imitating life within a tool that doesn’t share the same limitations, so as a VR veteran I find it bland and claustrophobic. That’s going to be really good for newcomers or casual users because the skeuomorphism is familiar, making it easy to immediately orient oneself and begin working together — and that illustrates a challenge in design vocabulary. While the familiar can provide a safe and comfortable starting point, the real power of VR requires training users for potentially unfamiliar use cases. Also, if you can be anywhere, why would you want to be in a meeting room, virtual-Lake Tahoe notwithstanding?

For meeting with those not in VR, or if I have a video call that needs input rather than passive attendance, I’ll frequently use a virtual webcam to attend by avatar. It’s sufficiently demonstrative for most team meetings, and the crew has gotten used to me showing up as a digital facsimile. I’ll surface from VR and use a physical webcam for anything sensitive or personal, however.

Meetings are best in person, in VR, in MURAL, and in Zoom — in that order. As a remote worker of several years, “in person” is a rarity for me — so I use VR to preserve the feeling of shared presence, of inhabiting a place with other people, especially when good spatial audio is used. Hand tracking enables meaningful gestures and animated expression, despite the avatars cartoonish appearance — somehow it all “just works”, your brain accepts that these people you know are embodied through these virtual puppets, and you get on with communicating instead of quibbling about missing realism (which will be a welcome improvement as it becomes available but doesn’t stop this from working right now).

What’s it like to actually use? In a word: comfortable. Given a few more words, I’d choose productive and effective. I can resize, reposition, add, or remove as much screen space as I need. I never have to squint or lean forward, crane my neck, hunt for an application window I just had open, or struggle to find a place for something. Many trade-offs and compromises from the past no longer apply — I put my apps in convenient locations I can see at a glance, and without getting in my way. I move myself and my gaze enough throughout the day that I’m not stiff at the end of it and experience less eye strain than I ever did with a bunch of desk-bound LCDs.

Since all I need is a keyboard, mouse, and a place to park myself, I’ve completely ditched the traditional desk. I can use a floor setup for part of the day and mix it up with a standing arrangement for the rest.

Supporting 4×4 MIMO takes a lot more power, and for battery powered devices, runtime is FAR more important.

How did your router even get a 'rating' of 5300 Mbps in the first place? Router manufacturers combine/add the maximum physical network speeds for ALL wifi bands (usually 2 or 3 bands) in the router to produce a single aggregate (grossly inflated) Mbps number. But your client device only connects to ONE band (not all bands) on the router at once. So, '5300 Mbps' is all marketing hype.

The only thing that really matters to you is the maximum speed of a single 5 GHz band (using all MIMO antennas).

You have 1 Gbps Internet, and just bought a very expensive AX11000 class router with advertised speeds of up to 11 Gbps, but when you run a speed test from your iPhone XS Max (at a distance of around 32 feet), you only get around 450 Mbps (±45 Mbps). Same for iPad Pro. Same for Samsung Galaxy S8. Same for a laptop computer. Same for most wireless clients. Why? Because that is the speed expected from these (2×2 MIMO) devices!

Before we dive into the details, here's a brief summary of the most important changes:

It’s been a hot, hot year in the world of data, machine learning and AI. 

we will be releasing KServe 0.7 outside of the Kubeflow Project and will provide more details on how to migrate from KFServing to KServe with minimal disruptions

You can attach Visual Studio Code to this container by right-clicking on it and choosing the Attach Visual Studio Code option. It will open a new window and ask you which folder to open.

You don’t have to download them manually, as a docker-compose.yml will do that for you. Here’s the code, so you can copy it to your machine:

Kafka version 2.8.0 introduced early access to a Kafka version without Zookeeper, but it’s not ready yet for production environments.

Kafka consumer — A program you write to get data out of Kafka. Sometimes a consumer is also a producer, as it puts data elsewhere in Kafka.

Kafka producer — An application (a piece of code) you write to get data to Kafka.

Kafka topic — A category to which records are published. Imagine you had a large news site — each news category could be a single Kafka topic.

Kafka broker — A single Kafka Cluster is made of Brokers. They handle producers and consumers and keeps data replicated in the cluster.

Kafka — Basically an event streaming platform. It enables users to collect, store, and process data to build real-time event-driven applications. It’s written in Java and Scala, but you don’t have to know these to work with Kafka. There’s also a Python API.

Then I noticed in the extension details that the "Source" was listed as the location of the "bypass-paywalls-chrome-master" file. I reasoned that the extension would load from there, and I think it does at every browser start-up. So before I re-installed it, I moved the source folder to a permanent location and then dragged it over the extensions page to install it. It has never disappeared since. You need to leave the source folder in place after installation.

Some problems with the feature

One thing to note is that match and case are not real keywords but “soft keywords”, meaning they only operate as keywords in a match ... case block.

Use variable names that are set if a case matches Match sequences using list or tuple syntax (like Python’s existing iterable unpacking feature) Match mappings using dict syntax Use * to match the rest of a list Use ** to match other keys in a dict Match objects and their attributes using class syntax Include “or” patterns with | Capture sub-patterns with as Include an if “guard” clause

It’s tempting to think of pattern matching as a switch statement on steroids. However, as the rationale PEP points out, it’s better thought of as a “generalized concept of iterable unpacking”.

We have decided to spend some extra time refactoring the Goose Protocol. The impact of this is that we will have to push the release out a week, but it will save us time when we implement the Duck and Chicken protocols next quarter.

Often, when you ask someone to perform a task, you have an expectation of what the output of that task will look like. Making sure that you communicate that expectation clearly means that they are set up for success and that you get the outcome you anticipated.

I am finding that it is often more important to bring wider context (“Ah, that would align with the work that Team Scooby are working on.") and ask the right questions (“How are we going to roll back if this fails?"), allowing the engineers (more experienced than you or not) to do their best work.

Machine Unlearning is a new area of research that aims to make models selectively “forget” specific data points it was trained on, along with the “learning” derived from it i.e. the influence of these training instances on model parameters. The goal is to remove all traces of a particular data point from a machine learning system, without affecting the aggregate model performance.Why is it important? Work is motivated in this area, in part by growing concerns around privacy and regulations like GDPR and the “Right to be Forgotten”. While multiple companies today allow users to request their private data be deleted, there is no way to request that all context learned by algorithms from this data be deleted as well. Furthermore, as we have covered previously, ML models suffer from information leakage and machine unlearning can be an important lever to combat this. In our view, there is another important reason why machine unlearning is important: it can help make recurring model training more efficient by making models forget those training examples that are outdated, or no longer matter.

The difference appears to be what the primary causes of that burnout are. The top complaint was “being asked to take on more work” after layoffs, consolidations or changing priorities. Other top beefs included toxic workplaces, being asked to work faster and being micromanaged.

What’s left are two options, but only one for the WWW. To capture the most internet users, the best option is to use a .is TLD; however, for true anonimity and control, a .onion is superior.

When you notice someone feeling anxious at work, try grabbing a notebook and helping them to get their concerns and questions onto paper. Often this takes the form of a list of to-dos or scenarios.

Anxiety comes from not seeing the full picture.

Overreaction is often a sign that something else might be going on that you aren’t aware of. Perhaps they didn’t get enough sleep or recently had a fight with a friend. Maybe something about the situation is triggering an unresolved trauma from their childhood — a phenomenon called transference.

In 2020, 35% of respondents said they used Docker. In 2021, 48.85% said they used Docker. If you look at estimates for the total number of developers, they range from 10 to 25 million. That's 1.4 to 3 million new users this year.

kind, microk8s, or k3s are replacements for Docker Desktop. False. Minikube is the only drop-in replacement. The other tools require a Linux distribution, which makes them a non-starter on macOS or Windows. Running any of these in a VM misses the point – you don't want to be managing the Kubernetes lifecycle and a virtual machine lifecycle. Minikube abstracts all of this.

The best practice is this: #!/usr/bin/env bash #!/usr/bin/env sh #!/usr/bin/env python