Development

After completion of the System Beeps, I wasn’t planning to make another stand alone album release with the pseudo polyphonic music, as I felt the topic had been explored enough. This, however, wouldn’t mean I couldn’t apply the experience and skills gained to make more utilitarian stuff, like an actual retro game OST or an old school demoscene project. Such an opportunity arose in Autumn 2020, as David Murray of The 8-bit Guy Youtube channel fame announced his new game to be in development, the Attack of The PETSCII Robots for Commodore PET and some other Commodore 8-bitters. As I previously worked with David on his previous big release, Planet X3 game for MS-DOS, and this was a perfect opportunity to satisfy my interest towards the pre-graphics era PCs as well as apply my vast experience both in the minimalistic computer music and 6502 assembly programming, I offered my services that had been accepted. Besides the sound code I also had hopes to participate as a music composer this time.

Unfortunately, this time the project didn’t went well on my side, and lots of issues of all kinds eventually turned it into a small scale development hell (you can learn more from a series of posts at my Patreon blog)  The end result was that my code and sound effects were only used in the VIC-20 port, and music for other versions has been created by other people. However, I was left with the full working code of the sound system for PET, and a number of music sketches. It would be a pity to file it into the archive, PET projects aren’t a frequent thing these days, so another chance to use the stuff wouldn’t come any time soon. So I got the idea to develop my music sketches into full songs, and release it as an alternative OST, and having David’s approval it has been done and released in the Winter 2021 as Faulty Robots, a small music album for PET that is available as a digital audio release and a runnable program for the actual PET computer.

• A short excursion into WeChat;
• About the platform, the version of the application, the utilities used and the decryption of the executable file;
• • About two protocols (old one and new one);
• About serialization of objects;
• Used cryptography and key exchange;
• About headers and hash-functions;
• About the exposures found.

If you're interested in learning new languages or teaching them, then you probably know such a way as parallel reading. It helps to immerse yourself in the context, increases the vocabulary, and allows you to enjoy the learning process. When it comes to reading, you most likely want to choose your favorite author, theme, or something familiar and this is often impossible if no one has published such a variant of a parallel book. It's becoming even worse when you're learning some cool language like Hungarian or Japanese.

Today we are taking a big step forward toward breaking this situation.

We will use the lingtrain_aligner tool. It's an open-source project on Python which aims to help all the people eager to learn foreign languages. It's a part of the Lingtrain project, you can follow us on Telegram, Facebook and Instagram. Let's start!

Find the texts

At first, we should find two texts we want to align. Let's take two editions of "To Kill a Mockingbird" by Harper Lee, in Russian and the original one.

I had some experience in the matching engine development for cryptocurrency exchange some time ago. That was an interesting and challenging experience. I developed it in clear C++ from scratch. The testing of it is also quite a challenging task. You need to get data for testing, perform testing, collect some statistics, and at last, analyze collected data to find weak points and bottlenecks. I want to focus on testing the C++ matching engine and show how testing can give insights for optimizations even without the need to change the code. The matching engine I developed can do more than 1’000’000 TPS (transactions per second) and is 10x times faster than the matching engine of the Binance cryptocurrency exchange (see one post on Binance Blog).

On Ali, an interesting toy – an oscilloscope called DSO138 is sold for a very inexpensive price. It has already gained quite a lot of popularity among electronics lovers, but the parameters of this device, alas, allow it to be more or less fully used only for debugging very low-frequency circuits. Actually, it is not positioned as a tool, but rather as a DIY-kit for novice electronics engineers.

This "toy" oscilloscope is assembled on the STM32F103 microcontroller, and with a fairly competent circuit design of the digital part, the presence of a fairly decent 320X240-dot color display, and not the most rotten analog path, everything, alas, is ruined by very weak ADCs on board the 32F103. The claimed band of 200 kHz can be recognized as such only with a very large stretch. Yes, it will show the presence or absence of a signal with such a frequency, but it will not be possible to really look at something beyond this.

At the same time, the 103-series has a slightly more powerful brother - the STM32F303, it is almost completely compatible with the legs, but it is significantly better in terms of the parameters we are interested in, there are 4 ADCs on board with a conversion frequency of 5 MHz (6 MHz with a 10-bit resolution). In this scenario, if you use all 4 ADCs in parallel with a 10-bit resolution, you can get a effective resolution of up to an honest 24 MSPS (millions of samples per second). The microcontroller is also inexpensive; you can easily find it on the same Ali for very reasonable money again. It is clear that the idea to change the microcontroller arose almost immediately after I tried this DSO138.

At the same time, if upgraded the toy can turn out to be a completely full-fledged tool that even professionals, not just novice amateurs, could already use. With these thoughts in mind, I decided to try to do something with a Chinese toy in my free time.

Getting acquainted with the CodeIgniter 4 PHP framework is quite simple.

Spend the evening following the instructions in the «Build Your First Application» section. Since the documentation is written in a good, technically understandable language, it is even possible to get some aesthetic pleasure in the process of familiarizing yourself with this and other sections.

The feeling of airiness and consistency of the CodeIgniter 4 project will be present with you everywhere now.

What is so attractive about CodeIgniter 4?

Four months of awesome work together with a few new contributors finally result in a new major release, which I'm happy to announce about.

Now we get completely new matrices, improved parser, a lot of new functions, almost rewritten interactive package (for working in Jupyter) and many more.

This article about a big update in a FOSS symbolic algebra library for .NET, I hope it may be interesting for someone!

Hackathons could be very intimidating and stressful. The key to getting better is doing simple projects. In this article, we will look at an example of a web app that can be used for sharpening your skills when you prepare for a hackathon. We will use a powerful Google API based on Machine Learning and apply the following technologies: ASP.NET, HTML, Docker, Heroku, and Git.

Originally posted on DEV.to

In my previous post, I touched on the point that Redis is more than just an in-memory cache.

Most people do not even consider Redis as a primary database. There are a lot of use cases where Redis is a perfect choice for non-cache related tasks.

In this article, I will demonstrate how I built a fully functional Q&A board for asking and upvoting the most interesting questions. Redis will be used as a primary database.

I will use Gatsby (React), Netlify serverless functions and Upstash Serverless Redis.

Upstash has been a good choice so far and I decided to try it out in a more serious project. I love everything serverless and how it makes things simpler for me.

Serverless will be a great choice for most tasks however you need to know the pros and cons of the tech you are using. I encourage you to learn more about serverless to get the most out of it.

We don't often get the chance to write something on parallel programming issues. This time we "got lucky". The TraceEvent standard method has some implementation peculiarities. They resulted in an error with multiple threads blocking. So we'd like to warn users about this nuance and cover this interesting case from our users support practice. Why was our support involved? Keep reading to find out. Enjoy the reading!

There is a lot of commotion in text-to-speech now. There is a great variety of toolkits, a plethora of commercial APIs from GAFA companies (based both on new and older technologies). There are also a lot of Silicon Valley startups trying to ship products akin to "deep fakes" in speech.

But despite all this ruckus we have not yet seen open solutions that would fulfill all of these criteria:

• Naturally sounding speech;
• A large library of voices in many languages;
• Support for 16kHz and 8kHz out of the box;
• No GPUs / ML engineering team / training required;
• Unique voices not infringing upon third-party licenses;
• High throughput on slow hardware. Decent performance on one CPU thread;
• Minimalism and lack of dependencies. One-line usage, no builds or coding in C++ required;
• Positioned as a solution, not yet another toolkit / compilation of models developed by other people;
• Not affiliated by any means with ecosystems of Google / Yandex / Sberbank;

We decided to share our open non-commercial solution that fits all of these criteria with the community. Since we have published the whole pipeline we do not focus much on cherry picked examples and we encourage you to visit our project GitHub repo to test our TTS for yourself.

Most solutions to algorithmic problems can be grouped into a rather small number of patterns. When we start to solve some problem, we need to think about how we would classify them. For example, can we apply fast and slow аlgorithmic pattern or do we need to use cyclic sortpattern? Some of the problems have several solutions based on different patterns. In this series, we discuss the most popular algorithmic patterns that cover more than 90% of the usual problems.

It is different from High-School Algorithms 101 Course, as it is not intended to cover things like Karatsuba algorithm (fast multiplication algorithm) or prove different methods of sorting. Instead, Algorithmic Patterns focused on practical skills needed for the solution of common problems. For example, when we set up a Prometheus alert for high request latency we are dealing with Sliding Window Pattern. Or let say, we organize a team event and need to find an available time slot for every participant. At the first glance, it is not obvious that in this case, we are actually solving an algorithmic problem. Actually, during our day we usually solve a bunch of algorithmic problems without realizing that we dealing with algorithms.

The knowledge about Algorithmic Patterns helps one to classify a problem and then apply the appropriate method.

But probably most importantly learning algorithmic patterns boost general programming skills. It is especially helpful when you are debugging some production code, as it trains you to understand the execution flow.

Patterns covered so far:

Sliding Window I

Sliding Window II

Merge Intervals

Dutch National Flag

Matrix Spiral

Iterative Postorder Traversal

Bit Manipulation

Stay tuned :)

<Promo> If you interested to work as a backend engineer, there is an open position in my squad. Prior knowledge of Golang is not required. I am NOT an HR and DO NOT represent the company in any capacity. However, I can share my personal experience as a backend engineer working in the company. </Promo>

Let's consider the following problem: we have an array of integers and we need to find out the length of the smallest subarray the sum of which is no less than the target number. If we don't have such a subarray we shall return -1.

We can start with a naive approach and consider every possible subarray in the input:

Every time when the essential question arises, whether to upgrade the cards in the server room or not, I look through similar articles and watch such videos.

Channel with the aforementioned video is very underestimated, but the author does not deal with ML. In general, when analyzing comparisons of accelerators for ML, several things usually catch your eye:

• The authors usually take into account only the "adequacy" for the market of new cards in the United States;
• The ratings are far from the people and are made on very standard networks (which is probably good overall) without details;
• The popular mantra to train more and more gigantic models makes its own adjustments to the comparison;

The answer to the question "which card is better?" is not rocket science: Cards of the 20* series didn't get much popularity, while the 1080 Ti from Avito (Russian craigslist) still are very attractive (and, oddly enough, don't get cheaper, probably for this reason).

All this is fine and dandy and the standard benchmarks are unlikely to lie too much, but recently I learned about the existence of Multi-Instance-GPU technology for A100 video cards and native support for TF32 for Ampere devices and I got the idea to share my experience of the real testing cards on the Ampere architecture (3090 and A100). In this short note, I will try to answer the questions:

• Is the upgrade to Ampere worth it? (spoiler for the impatient — yes);
• Are the A100 worth the money (spoiler — in general — no);
• Are there any cases when the A100 is still interesting (spoiler — yes);
• Is MIG technology useful (spoiler — yes, but for inference and for very specific cases for training);

The Chinese Mobile RPG: the Genre of Giants.

Chinese RPGs make up 56% of the top 500 grossing iOS games in China. According to data gathered in 2019 by Game Refinery’s Joel Julkunen. Let’s take a look at the genre and some important points for game developers and RPG enthusiasts to consider Chinese Mobile RPGs.

RPG or role-playing game is a game where the player plays as a character, often the main character, in a fictional game world.

This is a fourth article in the series of works (see also first one, second one, and third one) describing Machine Learning system based on Lattice Theory named 'VKF-system'. The program uses Markov chain algorithms to generate causes of the target property through computing random subset of similarities between some subsets of training objects. This article describes bitset representations of objects to compute these similarities as bit-wise multiplications of corresponding encodings. Objects with discrete attributes require some technique from Formal Concept Analysis. The case of objects with continuous attributes asks for logistic regression, entropy-based separation of their ranges into subintervals, and a presentation corresponding to the convex envelope for subintervals those similarity is computed.