Development

In some projects, the build script is playing the role of Cinderella. The team focuses its main effort on code development. And the build process itself could be handled by people who are far from development (for example, those responsible for operation or deployment). If the build script works somehow, then everyone prefers not to touch it, and no one ever is thinking about optimization. However, in large heterogeneous projects, the build process could be quite complex, and it is possible to approach it as an independent project.If you treat the build script as a secondary unimportant project, then the result will be an indigestible imperative script, the support of which will be rather difficult.

In this note we will take look at the criteria by which we chose the toolkit, and in the next one — how we use this toolkit. (There is also a Russian version.)

Author: Sergey Lukyanchikov, Sales Engineer at InterSystems

What is Distributed Artificial Intelligence (DAI)?

Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).

Distributed AI scenarios “for the masses”

We will not be discussing edge computations, confidential data operators, scattered mobile searches, or similar fascinating yet not the most consciously and wide-applied (not at this moment) scenarios. We will be much “closer to life” if, for instance, we consider the following scenario (its detailed demo can and should be watched here): a company runs a production-level AI/ML solution, the quality of its functioning is being systematically checked by an external data scientist (i.e., an expert that is not an employee of the company). For a number of reasons, the company cannot grant the data scientist access to the solution but it can send him a sample of records from a required table following a schedule or a particular event (for example, termination of a training session for one or several models by the solution). With that we assume, that the data scientist owns some version of the AI/ML mechanisms already integrated in the production-level solution that the company is running – and it is likely that they are being developed, improved, and adapted to concrete use cases of that concrete company, by the data scientist himself. Deployment of those mechanisms into the running solution, monitoring of their functioning, and other lifecycle aspects are being handled by a data engineer (the company employee).

Looking out for the best in app chat solution providers to enhance your business.

Getting confused with so many options to choose among  Sendbird and Twilio competitors. 

Then, let’s have some clarity with a detailed discussion over the feature comparison to go for the best Twilio and Sendbird alternative.

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.

Building console application can produce an unexpectedly much larger file than you would think due to implicit references.

What is the problem and how it can be solved?

At the very beginning of this year, Apple released the source code for macOS – Big Sur. It includes XNU, the kernel of the macOS operating system. A few years ago, PVS-Studio has already checked the kernel source code. It coincided with the analyzer release on macOS. It's been a while since then. The new kernel source code has been released. A second check? Why not?

Algebra and language (writing) are two different learning tools. When they are combined, we can expect new methods of machine understanding to emerge. To determine the meaning (to understand) is to calculate how the part relates to the whole. Modern search algorithms already perform the task of meaning recognition, and Google’s tensor processors perform matrix multiplications (convolutions) necessary in an algebraic approach. At the same time, semantic analysis mainly uses statistical methods. Using statistics in algebra, for instance, when looking for signs of numbers divisibility, would simply be strange. Algebraic apparatus is also useful for interpreting the calculations results when recognizing the meaning of a text.

Author: Chris Punches (@cmpunches, Silo group). License: "Please feel free to share unmodified".

The following text is an unmodified copy of now removed issue #2250 on rms-open-letter.github.io repository. The text claims multiple violations of different policies, codes of conduct and other documents in creation, content and support of the "Open letter to remove Richard M. Stallman from all leadership positions". The issue has not been addressed.

Reverse engineering might seem so complex, that not everyone has the bravery required to tackle it. But is it really that hard? Today we are gonna dive into the process of learning how to reverse engineer.

First of all, try to answer yourself, what are you hoping to achieve with reverse engineering? Because reverse engineering is a tool. And you should choose the right tool for your task. So when reverse engineering might be useful?

Most influential programmers say that code must be self-documenting. They find comments useful only when working with something uncommon. Our team shares this opinion. Recently we came across a code snippet that perfectly proves it.

By the beginning of 2021, Qrator Labs filtering network expands to 14 scrubbing centers and a total of 3 Tbps filtering bandwidth capacity, with the San Paolo scrubbing facility fully operational in early 2021;

New partner services fully integrated into Qrator Labs infrastructure and customer dashboard throughout 2020: SolidWall WAF and RuGeeks CDN;

Upgraded filtering logic allows Qrator Labs to serve even bigger infrastructures with full-scale cybersecurity protection and DDoS attacks mitigation;

The newest AMD processors are now widely used by Qrator Labs in packet processing.

DDoS attacks were on the rise during 2020, with the most relentless attacks described as short and overwhelmingly intensive.

However, BGP incidents were an area where it was evident that some change was and still is needed, as there was a significant amount of devastating hijacks and route leaks.

In 2020, we began providing our services in Singapore under a new partnership and opened a new scrubbing center in Dubai, where our fully functioning branch is staffed by the best professionals to serve local customers.

Users sometimes ask how new diagnostics appear in the PVS-Studio static analyzer. We answer that we draw inspiration from a variety of sources: books, coding standards, our own mistakes, our users' emails, and others. Recently we came up with an interesting idea of a new diagnostic. Today we decided to tell the story of how it happened.

The step has been made. Not sure where to, but for sure from the point of no return. Keep calm and keep walking. It is about time to look around and understand the smelly and slippery route before you. And what are those noisy creatures swarming around our fishy “innovative” design we called Mandelbrot blueprint? You don't get a buzzing noise like that, just buzzing and buzzing, without its meaning something.

In microservice world the problem of high load is exteremely big especially when we have a REST API which is accessed quite extensively. Why do we need throttling? The main answer is to decrease the load of the service at the moment.

Different frameworks have different solutions, mostly some additional libraries. Also there is a Guava RateLimiter and Bucket4J . What is interesting Spring MVC being one of the most popular solutions for building REST APIs (thank you Spring Boot) doesn't have any built in rate limiter. As for external solutions there not that many ways around.

Today, I would like to present a tiny experimental library specific for Spring MVC. It is called SpringRateLimitter. The library is very tiny ,works in runtime. The idea is to annotate entire rest controller or specific method , than count the number of incoming requests for the annotated URI and based on the values check if we exceed the allowed number of calls. In case of exceeding an HTTP error code 429 is thrown and after the throttling period is over , the endpoint is available again.

So How does it look like. As first step Maven dependency must be added

Here I am going to cover my own approach to compilation of mathematical functions into Linq.Expression. What we are going to have implemented at the end:

1. Arithmetical operations, trigonometry, and other numerical functions

2. Boolean algebra (logic), less/greater and other operators

3. Arbitrary types as the function's input, output, and those intermediate

Hope it's going to be interesting!

Architect. This word sounds so mysterious. So mysterious that to understand it you are almost forced to add something. Like “System Architect” or “Program Architect”. Such an addition does not make it clearer, but for sure adds weight to the title. Now you know – that’s some serious guy! I prefer to make undoubtful and around 10 years ago added to my email signature “Enterprise Architect of Information Systems”. It’s a powerful perk. Like “Chosen One”. With architects it is always the matter of naming, you know. Maybe that is why the only way to become an architect is to be named as one by others. Like with vampires. One of them has to byte you! That is probably the easiest way to earn the title as there is no degree or school to grant you one. And if there’s a troubling title, somebody’s making a trouble, and the only reason for making a trouble that I know of is because you’re an Enterprise. Huge old and complex multinational corporation. Like a one-legged pirate. Strong and scary, but not a good runner. You own your ship, you had good days, you have some gold, you need new ways.

To get to new treasures and avoid losing the second leg to piranha regulators and local business sharks swarming waters near every enterprise ship – every pirate has a map. A map is a list of major features and requirements in desired order and priority.

Security. What does this word mean to you? Nowadays, companies spare no effort to ensure that their product is secured from hacking and all sorts of information leaks. PVS-Studio decided to help its users and expand the functionality in this area. Therefore, one of the main innovations of the upcoming release will be the introduction of analyzer new features which will ensure code safety and security. This article aims to present these features.

Some of the features of your website require message queue integration. It is not a complex task for most developers. If you work with Azure infrastructure, you are able to choose Azure Service Bus as a queue engine. It sounds quite simple: just create Azure Resource, write some code and then be happy! But what would you say if the resources are limited? What will you do if there are several teammates in your team, and all of you have to debug queues at the same time? I try to give you a simple solution.