PopSci

Recently, a colleague asked me in a chat:

— Is there an article how to pack servers into the racks properly?

I realised that I'm unaware of it. So, I decided to write my text.

Firstly, this is an article about bare metal servers in the data centre (DC) facilities. Secondly, we estimate that there are a lot of servers (hundreds or thousands); the article doesn't make sense for fewer quantities. Thirdly, we consider that there are three constraints in the racks: physical space, electric power per each one, and cabinets stay in the rows adjacent to each other, so we can use a single ToR switch to connect servers in them.

Chapter 2.
(the link to Chapter 1)

The Art of Designing Road Networks

Transport problems of a city through the eyes of a Computer Scientist

If I were recommended an article with the title “The Art of Designing Road Networks,” I would immediately ask how many road networks were built with the participation of its author. I must admit, my professional activity was far from road construction and was recently associated with the design of microprocessors where I, among other responsibilities, was engaged in the resource consumption of data switching. At that time my table stood just opposite the panoramic window which opened up a beautiful view of the long section of the Volgograd Highway and part of the Third Transport Ring with their endless traffic jams from morning to evening, from horizon to horizon. One day, I had a sudden shock of recognition: “The complexities of the data switching process that I struggle with on a chip may be similar to the difficulties the cars face as they flow through the labyrinth of road network”.
Probably, this view from the outside and the application of methods that were not traditional for the area in question gave me a chance to understand the cause of traffic jams and make recommendations on how to overcome the problem in practice.

There was a time when moving from one place to another was a matter of utility. People did not Care About Us lyrics like comfort. Public transportation was dominating the landscape of movement and private transport was considered a luxury.

Today, we live in a world where people expect more than just the movement. They do not want to compromise on the aspects of comfort and convenience. They would like to be picked up from their doorstep and they would like to be dropped precisely in the place that they want to be.

These transformations have given rise to the business of ride-hailing. There are a lot of companies that provide the service of transportation by picking people from one place and dropping them in a place of their choice.

The constructive onslaught of Smartphones enabled with GPS made the business of ride-hailing efficient, profitable and delightful to the customer.

Blockchain oracles resolve the issue of delivering external data to the blockchain – but we still need to know which of them we can trust.

Earlier, I have introduced the Ontology Smart Contract in
Part 1: Blockchain & Block API and
Part 2: Storage API
Part 3: Runtime API
Today, let’s talk about how to invoke an Ontology native smart contract through the Native API. One of the most typical functions of invoking native contract is asset transfer.

You’re utterly focused. You’ve lost track of time. Nothing else in the world exists. You’re living in the moment.

While this might sound like meditation, it’s a description that can also be applied to the state of flow – the feeling of being so engaged by your work, that you lose yourself to it completely, while massively increasing your productivity in the process.

It’s the holy grail that we all strive for, whether it’s a hobby we’re passionate about, or a project at work. Achieving our best and utilising our maximum potential at all times, can however, be a struggle.
We had the pleasure of talking with Dr. Jack Lewis, a neuroscientist with a passion for exploring how our minds work, to see what motivates us to do our best work, and the important roles that workplace environments, culture, and technology can play.

Introduction

Earlier, I have introduced the Ontology Smart Contract in
Part 1: Blockchain & Block API and
Part 2: Storage API
Now when you have an idea about how to call the relevant API for persistent storage when developing Python smart contract on Ontology, let’s go on to Runtime API (Contract Execution API). The Runtime API has 8 related APIs that provide common interfaces for contract execution and help developers get, convert, and validate data. Here’s a brief description of these 8 APIs:

This is an official tutorial published earlier on Ontology Medium blog
Excited to publish it for Habr readers. Feel free to ask any related questions and suggest a better format for tutorial materials

Foreword

Earlier, in Part 1, we introduced the Blockchain & Block API of Ontology’s smart contract. Today we will discuss how to use the second module: Storage API. The Storage API has five related APIs that enable addition, deletion, and changes to persistent storage in blockchain smart contracts. Here’s a brief description of the five APIs:

What one gets easily and for free can be a real struggle for others — these are the thoughts every letter of the «Dobroshrift» [Kindness Font] evolves. It was developed for the World Cerebral Palsy Day with the participation of children with this diagnosis. So we decided to take part in this charity action and changed the website logo till the end of the day.

Microsoft researchers have developed an artificial intelligence (AI) system that has taught itself the intricacies of Mahjong and can now match the skills of some of the world’s top players.

The complex board game of chance, bluff, and strategy was invented in China thousands of years ago and remains a passionate pastime for millions of Asians today, with many dedicated competitors playing online.

Computers have learned to play Chess and another ancient Chinese game, Go, amid much fanfare in the past. But scientists at Microsoft Research (MSR) Asia see their achievement as far more than just a case of technology mastering yet another game.

The researchers – who named their system Super Phoenix, or Suphx for short – developed a series of AI algorithmic breakthroughs to navigate the uncertain nature of Mahjong. With more work, these could potentially be applied in real situations to solve problems thrown up by unknown factors and random events.

Celestia is a three-dimensional space simulator. Simulation of the space allows exploring our universe in three dimensions. Celestia is available on Windows, Linux and macOS. The project is very small and PVS-Studio detected few defects in it. Despite this fact, we'd like to pay attention to it, as it's a popular educational project and it will be rather useful to somehow improve it. By the way, this program is used in popular films, series and programs for showing space. This fact, in turns, raises requirements to the code quality.

We are excited to announce updates to Model Builder and improvements in ML.NET. You can learn more in the «What’s new in ML.NET?.» session at .NET Conf.

ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers.

ML.NET offers Model Builder (a simple UI tool) and CLI to make it super easy to build custom ML Models using AutoML.

Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more!..

This is an official tutorial published earlier on Ontology Medium blog
Excited to publish it for Habr readers. Feel free to ask any related questions and suggest a better format for tutorial materials

Foreword

In this article, we will begin to introduce the smart contract API of Ontology. The Ontology’s smart contract API is divided into 7 modules:

• Part 1: Blockchain & Block API
• Part 2: Storage API
• Part 3: Runtime API
• Part 4: Native API
• Part 5: Upgrade API
• Part 6: Execution Engine API
• Part 7: Static & Dynamic Call API

In this article, we will introduce the Blockchain & Block API, which is the most basic part of the Ontology smart contract system. The Blockchain API supports basic blockchain query operations, such as obtaining the current block height, whereas the Block API supports basic block query operations, such as querying the number of transactions for a given block.

Let’s get started!

First, create a new contract in SmartX and then follow the instructions below.

1. How to Use Blockchain API

References to smart contract functions are identical to Python’s references. Developers can introduce the appropriate functions as needed. For example, the following statement introduces GetHeight, the function to get the current block height, and GetHeader, the function to get the block header.

Introduction

Today, geolocation and geo-positioning have turned into a state that we did not dream about 60 years ago, during the first thoughts about creating an Internet network.

This article was prepared thanks to the IPGeolocation.io

Cartography has turned into a miracle — from insanely large sheets of paper to what fits in the palm of your hand.

Today, maps are used not only to search for a cultural center in Paris, but to be able to find everything:

• nearest hotel
• pharmacy or hospital
• grocery store
• university or school
• Pink Concert venue in San Francisco

The transformation of technology will not stop there. It will be better and more accurate than ever.

Evolution in time

Let's see how the evolution of geolocation began. The journey of this phenomenon begins in ancient times.

Excursion into the past

Every day we hear about new budding software programs. With the rapid advances in the software development industry, new players entering the market and have captured a large segment of the market. So, when you need to track business projects, handle the allocation of assets, and solve other IT challenges, you need help from the top software development companies. However, it doesn’t mean that you should select a software development company on the basis of how appealing it was once. Today, people prefer to check the full profile of a company first and then decide to get their service.

To ease your pressure down, listing here some of the internationally acclaimed software development companies from the development niche. This list has been curated on the basis of a variety of reliable resources like Clutch, GoodFirms, Google SERPs, user's feedback & reviews, different independent review firms including medium.com, themanifest.com, etc. Based on the collected data, I’ve assigned the total score for each of the 10 leaders, and here I represent a list of the top software development companies that are doing best in their domain.

List of the Top Software Development Companies

With the advent of mobile phones with high-quality cameras, we started making more and more pictures and videos of bright and memorable moments in our lives. Many of us have photo archives that extend back over decades and comprise thousands of pictures which makes them increasingly difficult to navigate through. Just remember how long it took to find a picture of interest just a few years ago.

One of Mail.ru Cloud’s objectives is to provide the handiest means for accessing and searching your own photo and video archives. For this purpose, we at Mail.ru Computer Vision Team have created and implemented systems for smart image processing: search by object, by scene, by face, etc. Another spectacular technology is landmark recognition. Today, I am going to tell you how we made this a reality using Deep Learning.

At the last Tech Train IT festival, we met the legendary John Romero, who designed and developed the iconic Wolfenstein 3D, Doom, and Quake. We talked about whether game developers need soft skills, which working tools to pay attention to, and which co-founder of Id Software's favorite toys are. Questions were asked by Nikita Tsaplin, the founder of RUVDS.


→ Text and video in Russian

[Because my extension got a lot of attention from the foreign audience, I translated my original article into English].

Limit

Being a top-rated professional network, LinkedIn, unfortunately, for free accounts, has such a limitation as Commercial Use Limit (CUL). Most likely, you, same as me until recently, have never encountered and never heard about this thing.



The point of the CUL is that when you search people outside your connections/network too often, your search results will be limited with only 3 profiles showing instead of 1000 (100 pages with 10 profiles per page by default). How ‘often’ is measured nobody knows, there are no precise metrics; the algorithm decides it based on your actions – how frequently you’ve been searching and how many connections you’ve been adding. The free CUL resets at midnight PST on the 1st of each calendar month, and you get your 1000 search results again, for who knows how long. Of course, Premium accounts have no such limit in place.

However, not so long ago, I’ve started messing around with LinkedIn search for some pet-project, and suddenly got stuck with this CUL. Obviously, I didn’t like it that much; after all, I haven’t been using the search for any commercial purposes. So, my first thought was to explore this limit and try to bypass it.

[Important clarification — all source materials in this article are presented solely for informational and educational purposes. The author doesn't encourage their use for commercial purposes.]

Deep Learning applications have changed a lot of things. Some which give hope for a brighter future, and some which raise suspicions. However, for developers, the growth of deep learning applications has made them more perplexed about choosing the best among so many deep learning frameworks out there.

TensorFlow is one of the deep learning frameworks that comes in mind. It is arguably the most popular deep learning framework out there. Nothing justifies the statement better than the fact that Tensorflow is used by the likes of Uber, Nvidia, Gmail among other big corporations for developing state-of-the-art deep learning applications.

But right now, I am on a quest to find whether it indeed is the best deep learning framework. Or perhaps find what makes it the best out of all other frameworks it competes against.

Nowadays, when VR helmets have become part of our reality and Tesla cars fly in space, you can use all the power of browser engines to create truly interactive, cross-platform and stylish presentations, rather than make a set of PPTX pages or, even worse, a PDF document in "illustrative material for explanatory and calculation report" style.

Since 2015, I have been trying to find the optimal presentation format for myself (apart from graduation projects). And now I think I have almost succeeded. It all started with PowerPoint, and ended with web frameworks based on JavaScript.

There are several JavaScript engines which can be used to create cool presentations: Marp, Reveal, landslide, hacker-slides, slidify and others. In some engines, you can use Markdown, some are embedded in an IDE, and some have their own editors. I have tried the first two engines.

As a demonstration, slide examples and video are available.