There's a number of factors motivating the change. We'll explore the
reasons, from long term viability of Subversion, to wider support
for tools that will make the project better. Today I'll enumerate
these points. There are some logistical points around how the
decision was made. I'll not get into the politics about how we got
here, though. While interesting for insiders who like to argue and
quibble, they are no more relevant to the larger community that the
color of the delivery truck that delivered groceries to your grocer
this morning (even if it had the latest episode of a cool, scrappy
cartoon cat that was involved in a multi-year arc wooing the love of
his life by buying food at this store).
In a neural network, numeric data points, called inputs, are fed
into the neurons in the input layer. Each neuron has a weight, and
multiplying the input number with the weight gives the output of the
neuron, which is transferred to the next layer.
The activation function is a mathematical “gate” in between the
input feeding the current neuron and its output going to the next
layer. It can be as simple as a step function that turns the neuron
output on and off, depending on a rule or threshold. Or it can be a
transformation that maps the input signals into output signals that
are needed for the neural network to function
In the afternoon I took a photo of another sunflower flowering. This
is the second one in our garden, the first one opened the
31st of August 2020.
In the above photo you can see the first one, which has finished
flowering (top, center), and another one that's close to fully open
(bottom, center). The plants grew from seeds dropped by birds. Most
likely western jackdaw (Coloeus monedula) as they were very common in
our garden when we fed seeds to the birds.
Welcome aboard! We're going to implement Git in Python to learn more
about how Git works on the inside.
This tutorial is different from most Git internals tutorials because
we're not going to talk about Git only with words but also with
code! We're going to write in Python as we go.
This is not a tutorial on using Git! To follow along I advise that
you have working knowledge of Git. If you're a newcomer to Git, this
tutorial is probably not the best place to start your Git journey. I
suggest coming back here after you've used Git a bit and you're
comfortable with making commits, branching, merging, pushing and
Despite its growing popularity as a systems language, Go programs
are susceptible to severe performance regressions at large scale. In
systems with high memory usage, garbage collection (GC) can cause
performance regressions by cannibalizing resources from the main
program. The goal of this post is to help you understand:
How Go GC works at a high level? Why would it impact your system’s performance?
What causes GC pressure (more resources spent on GC)?
How to determine if GC pressure is the cause of your performance problems?
How to measure and profile your program’s heap usage?
How to identify which part of the code is the culprit?
What are some steps you can take to lower heap usage and GC pressure?
As we add new code to existing files, it feels natural to append –
add new functions at the end of the file, new requires at the end of
the list of requires, and so on. This approach introduces some
friction, and in this post I'll share some pointers for improved
editing and git workflow.
Enterprise application is a long-lived, reliable system, having a
lot of persisting data for many years. Nowadays the world of Golang
is not providing possibilities to build systems in the way that
enterprise systems are built.
GitHub CLI brings GitHub to your terminal. It reduces context
switching, helps you focus, and enables you to more easily script
and create your own workflows. Earlier this year, we announced the
beta of GitHub
we released the beta, users have created over 250,000 pull requests,
performed over 350,000 merges, and created over 20,000 issues with
GitHub CLI. We’ve received so much thoughtful feedback, and today
GitHub CLI is out of beta and available to download on Windows,
macOS, and Linux.
With GitHub CLI 1.0, you can:
Run your entire GitHub workflow from the terminal, from issues through releases
Call the GitHub API to script nearly any action, and set a custom alias for any command
Connect to GitHub Enterprise Server in addition to GitHub.com
In the evening I installed this update of Thunderbird. I think it will
take some time to get used to the new icons; they pop out more than
the old ones in my opinion.
I also couldn't get email from some accounts. Changing the value of
security.tls.version.min to 1 fixed this. To do so I opened the
Config Editor via the General section of Preferences (scroll to the
bottom). Thanks user ermspv for explaining this in a
comment on Hacker News.
The optional chaining operator (?.) permits reading the
value of a property located deep within a chain of connected objects
without having to expressly validate that each reference in the
chain is valid. The ?. operator functions similarly to the
. chaining operator, except that instead of causing an error if a
reference is nullish (null or undefined), the expression
short-circuits with a return value of undefined. When used with
function calls, it returns undefined if the given function does not
This post is a direct response to Which Parsing
you haven’t read that article, do it now — it is the best short
survey of the lay of the land of modern parsing techniques. I agree
with conclusion — LR parsing is the way to go if you want to do
parsing “properly”. I reasoned the same a couple of years ago:
However, and here’s the catch, rust-analyzer uses a hand-written
recursive descent / Pratt
of the reasons for that is that I find existing LR parser generators
inadequate for production grade compiler/IDE. In this article, I
want to list specific challenges for the authors of LR parser
are one of those topics that never quite clicked for me in the
abstract, but I’ve been able to take advantage of them on a few
recent projects and wanted to share some of what I’ve learned.
According to a
study, 40% of people abandon a website that takes more than 3
seconds to load and a 1-second delay in page response can result in
a 7% reduction in conversions. Yes, every second matters! And we
saved around 2.5 seconds (90th percentile) and 1.2 seconds (50th
percentile) by using Brotli compression over gzip compression for
We all know that parsing is an important part of designing and
implementing programming languages, but it’s the equivalent of
Brussels sprouts: good for the diet, but a taste that only a select
few enjoy. Unfortunately, I’ve come to realise that our general
distaste for parsing is problematic. While many of us think that
we’ve absorbed the advances of the 1960s into our collective
understanding, I fear that we have regressed, and that we are often
making inappropriate decisions about parsing. If that sounds
accusatory, I don’t mean it to be: I spent over 20 years assuming
that parsing is easy and that I didn’t need to understand it
properly in order to use it well. Alas, reality has been a cruel
teacher, and in this post I want to share some of the lessons I’ve
been forced to slowly learn and acknowledge.
Each release has a lot of these "hidden gems" -- features that may
not jump off the page, but can have a big impact when you actually
need them. Postgres 13 is no exception: some of these features make
it easier to write queries, add additional layers of security, or
help you to avoid downtime.
In this installment we are going to look at “container
orchestration” for Docker. In the previous
we just looked at how to run an individual container. However, most
applications are a combination of services which are orchestrated
together to make an application.
While in theory all the pieces of an application could be built into
a single container, it is better to split an application into its
relevant services and run a separate container for each
service. There are several reasons for this, but the biggest one is
Machine learning experts have borrowed the methods of regression
analysis from math because they allow the making of predictions with
as little as just one known variable (as well as multiple
variables). They’re useful for financial analysis, weather
forecasting, medical diagnosis, and many other fields.
Whether you’re just getting to know a dataset or preparing to
publish your findings, visualization is an essential tool. Python’s
popular data analysis library,
pandas, provides several
different options for visualizing your data with .plot(). Even if
you’re at the beginning of your pandas journey, you’ll soon be
creating basic plots that will yield valuable insights into your
In this tutorial, you’ll learn:
What the different types of pandas plots are and when to use them
How to get an overview of your dataset with a histogram
How to discover correlation with a scatter plot
How to analyze different categories and their ratios