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Hey everyone! I represent a game studio without a name, and the project we’re working on goes by the technical name of "CGDrone". I started writing this article earlier today, having tortured myself for ages with sketches, colours, algorithms and correcting bugs in rotations based on quaternions (the last one just about finished me off). You can probably understand I needed a break.

I’ve often come across stories people have posted online about how they made their game, the difficulties they faced, and the result they achieved at the end. Likewise, our team has its own story, and I’d like to share a bit about it.

There is a myriad of articles about where to find remote jobs, particularly in tech. Some of them are outdated and most of them don't provide detailed reviews. So that's why I decided to do my own research. I did a basic search by "React" skill (where possible) and expected to see mostly "Frontend Developer" vacancies.

In 2013 Canonical tried to crowdfund Ubuntu Edge smartphone. Its main feature could be the ability to use the smartphone as a full-fledged PС. Unfortunatly, the crowdfunding campaign did not accumulate enough money, so a dream of having a universal device remained to be the dream.

I've been searching for universality, too, on the software side, not the hardware one. Today I can confidently say I found the necessary combination: Git and JavaScript.

As you know, I have already described the benefits of browser applications (nCKOB static site generator) and the benefits of using Git instead of yet another back-end with API (GitBudget to track personal spendings). Once GitBudget was out, I spent the remaining 2020 to build a system allowing one to create browser applications right inside browsers. GitJS is the name of that system.

AngelList is a social network designed to connect startups with investors and vice versa. The founders of it were dissatisfied with how opaque the VC world was, and found a way to increase the amount of available data. The project began in partnership with just 50 volunteer investors wishing to allocate $80 million in capital, and has grown to be the leading website of its kind. Over the past three years more than 75% of startups that received seed funding from American investors used AngelList to make it happen.

This article is about security. I’ll focus on this in the context of web applications, but I’ll also touch on other types of applications. Before I describe approaches and frameworks, I want to tell you a story.

Background

Throughout my years working in the IT sphere, I’ve had the opportunity to work on projects in a variety of fields. Even though the process of authenticating requirements remained relatively consistent, methods of implementing the authorization mechanism tended to be quite different from project to project. Authorization had to be written practically from scratch for the specific goals of each project; we had to develop an architectural solution, then modify it with changing requirements, test it, etc. All this was considered a common process that developers could not avoid. Every time someone implemented a new architectural approach, we felt more and more that we should come up with a general approach that would cover the main authorization tasks and (most importantly) could be reused on other applications. This article takes a look at a generalized architectural approach to authorization based on an example of a developed framework.

Approaches to Creating a Framework

As usual, before developing something new, we need to decide what problems we’re trying to solve, how the framework will help us solve them, and whether or not there is already a solution to these issues. I’ll walk you through each step, starting with identifying issues and describing our desired solution.

We’re focusing on two styles of coding: imperative and declarative. Imperative style is about how to get a result; declarative is about what you want to get as a result.

There’s a lot of talk about machine learning nowadays. A big topic – but, for a lot of people, covered by this terrible layer of mystery. Like black magic – the chosen ones’ art, above the mere mortal for sure. One keeps hearing the words “numpy”, “pandas”, “scikit-learn” - and looking each up produces an equivalent of a three-tome work in documentation.

I’d like to shatter some of this mystery today. Let’s do some machine learning, find some patterns in our data – perhaps even make some predictions. With good old Python only – no 2-gigabyte library, and no arcane knowledge needed beforehand.

Interested? Come join us.

Voice calling plays a crucial role in personal and professional communication. Be it a friendly conversation among classmates or a business deal between companies – voice calling has been the most easy, convenient, and affordable way to communicate for decades. Thus, communication corporations and developers have brought in innovative ways to integrate their Application Programming Interfaces (APIs) and invent newer ways of voice communication.

Currently, there are hardly any high quality / modern / free / public voice activity detectors except for WebRTC Voice Activity Detector (link). WebRTC though starts to show its age and it suffers from many false positives.

Also in some cases it is crucial to be able to anonymize large-scale spoken corpora (i.e. remove personal data). Typically personal data is considered to be private / sensitive if it contains (i) a name (ii) some private ID. Name recognition is a highly subjective matter and it depends on locale and business case, but Voice Activity and Number Detection are quite general tasks.

Key features:

• Modern, portable;
• Low memory footprint;
• Superior metrics to WebRTC;
• Trained on huge spoken corpora and noise / sound libraries;
• Slower than WebRTC, but fast enough for IOT / edge / mobile applications;
• Unlike WebRTC (which mostly tells silence from voice), our VAD can tell voice from noise / music / silence;
• PyTorch (JIT) and ONNX checkpoints;

Typical use cases:

• Spoken corpora anonymization;
• Can be used together with WebRTC;
• Voice activity detection for IOT / edge / mobile use cases;
• Data cleaning and preparation, number and voice detection in general;
• PyTorch and ONNX can be used with a wide variety of deployment options and backends in mind;

At the beginning of the year 2021, Qrator Labs is celebrating its 10 year anniversary. On January 19 our company marks the official passing of a formal 10 years longevity mark, entering its second decade of existence. 

Everything started a little bit earlier - when at the age of 10 Alex saw the Robotron K 1820 - in 2008, when Alexander Lyamin - the founder and CEO of Qrator Labs, approached the Moscow State University superiors, where he worked as a NOC engineer at the time, with an idea of a DDoS-attack mitigation research project. The MSU's network was one of the largest in the country and, as we know now, it was the best place to hatch a future technology.

That time MSU administration agreed, and Mr Lyamin took his own hardware to the university, simultaneously gathering a team. In two years, by summer 2010, the project turned out to be that successful. It courted the DDoS attack of a bandwidth exceeding the MSU's upstream bandwidth capability. And on June 22 MSU superiors gave Mr Lyamin a choice - to shut down or find money to incorporate.

Alexander Lyamin chose to incorporate with his own means, which effectively meant that the needed infrastructure must be built from scratch. The initial design should be distributed instead of concentrated within one network, which resources were not enough for this specific task. And by September 1, 2010, those first server sites were ready and running.

There are many situations when you need to protect your data, and different tools can be used to do that. For example, a safe. We develop a passcode data protection mechanism by using an FPGA board and Quartus Prime software. It allows demonstrating the basic concepts of a combination lock such as entering data, setting and checking a passcode, and displaying data.

When we think about robots, the first thing that comes to mind are robotic vacuum cleaners. The reason is simple: they are the most "solid" demonstration of success of "consumer" robotics. So making one sounds like a good idea... at first.

But isn't it a bit counter productive - to build something that popular, something we can buy in a store at a commodity (small) price? Should we build something similar, but NOT a vacuum cleaner? Something like... a floor washer, perhaps? Yes, a robotic floor washer.

In this tutorial I am going to build a fully working prototype of a robotic floor washer. By "fully working" I mean that it is going to wash floor, instead of moving dirt around like most robotic "moppers" do. While by "prototype" I mean it is going to be the first step towards production-ready unit, but not a production-ready unit yet. Let me explain.

First of all, it is not going to be THAT solid. You can grab a robotic vacuum cleaner that you got from the store by any part, including wheels and bumper and lift it. It will not fall apart. Ours probably will. The reason is, to make a device "mechanically solid" is a separate task, and if we focus on it, then "robotic" tasks will become more difficult to achieve. So we are going to do what engineers usually do: first they build C3PO without the outside body, wires everywhere and so on. And only then they put a gold-covered outfit on it.

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 latest forecast by Niko Partners shows promise that gender equality in gaming is on track to reach 50/50 by the end of 2020, as figures show a consistent rise of 4% year on year: “Female gamers are rising, making up 42% of total gamers in 2018, 46% in 2019, and forecast to be 50% in 2020, primarily on the mobile platform.” This forecast follows a report by the China Audio-Video and Digital Publishing Association last year that identified growth potential and calls for more quality games targeted at female players.

2019 saw the number of female gamers reach 300 million, contributing RMB 52.7 billion (USD 7.5 billion) or 22.8% of total actual sales revenue of the gaming industry in China. The Association’s report also identified low cost leisure games as most popular among females. Furthermore, the Gamma Data report gives China’s female game market revenue a forecast of RMB 56.8 billion for 2020, with 70% (RMB 40.1 million) of revenue deriving from mobile games.

This is a transcription of a talk that was presented at CSNOG 2020 — video is at the end of the page

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.