Welcome to my latest article. If you haven't caught my previous ones, I highly recommend checking them out for some really useful content. Today, I'm excited to dive into something special: my top 10 favorite VS Code extensions. These tools are incredibly popular among developers globally, and I believe they're essential for anyone serious about coding.
If you are at the beginning of your educational path in IT, or contemplating about making changes in your career and starting learning UX design, you might have some doubts and uncertainties on whether UX design is something worth studying and working in right now. We can dispel your doubts: UX designers are very required in the majority of companies, since they help to make products appealing to users, thus raising the companies income and enhancing their reputation. Professionals in this sphere are in high demand right now, and
In the age of information, the ability to accurately and quickly retrieve data relevant to a user's query is paramount. Traditional search methodologies, which rely on keyword matching, often fall short when it comes to understanding the context and nuances of user queries. Semantic search, which seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms, has emerged as a solution to these limitations. However, implementing semantic search can be complex, involving advanced algorithms and understanding of natural language processing (NLP).
Existing solutions such as Elasticsearch and Solr have been at the forefront of tackling these challenges, providing platforms that support more nuanced search capabilities. These tools use a combination of inverted indices and text analysis techniques to improve search outcomes. Yet, the advent of machine learning and vector search technologies opens up new avenues for enhancing semantic search, with solutions like OpenAI's Embeddings API and the pgvector extension for PostgreSQL leading the charge.
Trading robots are conquering the Wall Street! Learn how to create your first automated python trading bot.
We present a fully functioning trading bot pipeline on python using the Binance API. Starting with the general introduction, we provided a comprehensive overview of main API calls and their implementation on python. After this we show a fully functioning python code presenting a basic trading bot with core features using static channel breakout strategy.
How to design an economy for your game? The answer to this question might require a series of lectures or articles. The fundamental difference in the approach is based, first of all, on monetization model: F2P or B2P. The second thing that defines the approach to developing an economy system is game genre. This article reviews the case of designing the game economy for a B2P (premium) game, which doesn’t involve earning on microtransactions.
Referential transparency, a key concept in functional programming, is often associated with more reliable, easier to test, and safer software. This term refers to a principle in which a function, given the same input, will always produce the same output without producing any side effects.
In the real world of software development, side effects are inevitable. Programs are rarely useful unless they interact with the outside world. This interaction could be reading from or writing to the console, making network requests, querying a database, or modifying a variable.
Yet, despite the necessity of side effects, they introduce risks and complexities. Programs with side effects are harder to test, harder to reason about, and more prone to bugs. They can also make the system as a whole more difficult to understand and maintain, due to hidden dependencies between components.
Enter referential transparency - a concept that means a function, given the same input, will always provide the same output, without creating any side effects. A function that adheres to this principle doesn't read any global state or change any state outside of its scope. The result is code that is more predictable and easier to reason about.
In terms of software safety and reliability, the absence of side effects is not enough. Programs should also be free from external influence - their results should only depend on their arguments. That is, programs should not read data from the console, a file, network, database, or even system variables.
Let’s imagine you need access to the real-time data of some smart contracts on Ethereum (or Polygon, BSC, etc.) like Uniswap or even PEPE coin to analyze its data using the standard data scientist/analyst tools: Python, Pandas, Matplotlib, etc. In this tutorial, I’ll show you more sophisticated data access tools that are more like a surgical scalpel (The Graph subgraphs) than a well-known Swiss knife (RPC node access) or hammer (ready-to-use APIs). I hope my metaphors don’t scare you ?.
Generative AI is creating waves in the way we work, significantly revolutionizing the software development process. AI tools are appearing in various phases of software development, such as design, development, and testing. However, there aren't many tools specifically focused on software business analysis tasks.
But with a little creative thinking, we can put "one-size-fits-all" applications like ChatGPT to good use. It can definitely speed up execution of many typical tasks and free up analysts to focus on the more challenging, strategic aspects of the job.
Every day a new neural network appears and every day more opportunities are opened to designers to simplify their workflow. Someone fundamentally refuses to use them, because “there is no life in machinex and technologies”, and someone is only happy to find a way to reduce the amount of work. Personally, I belong to the second type and want to share the most detailed gait on neurons I have acquired lately.
TL;DR: All AI-based noise cancellations suck, only the physical cancellation technique works! And physical cancellation is implemented well only in Jabra devices yet, but other features suck them! All Bluetooth headsets suck too! So, no solution yet, just complaints!
The environment: I work in a pretty talky office room, where sit around 10 people, and all of them have many online conferences every day, including me. So, silence in the room is a rare situation.
The problem: The main problem is that most of the microphones pick up all side talk pretty loud, which makes it very unpleasant for other people to listen to my voice in meetings!
Hey there, my fellow crypto enthusiast! Are you looking to take your trading game to the next level and gain a deeper understanding of the market trends and behavior? If so, then onchain analytics is the perfect tool for you.
Do you need to produce satellite interferometry results for your work or study? Or should you find the way to process terabytes of radar data on your common laptop? Maybe you aren't confident about the installation and usage of the required software. Fortunately, there is the next generation of satellite interferometry products available for you. Beginners can build the results easily and advanced users might work on huge datasets. Open Source software PyGMTSAR is available on GitHub for developers and on DockerHub for advanced users and on Google Colab for everyone. This is the cloud-ready product, and it works the same as do you run it locally on your old laptop as on powerful cloud servers.
Video recording of our webinar about dstack and reproducible ML workflows, AVL binary tree operations, Ultralytics YOLOv8, training XGBoost, productionize ML models, introduction to forecasting ensembles, domain expansion of image generators, Muse, X-Decoder, Box2Mask, RoDynRF, AgileAvatar and more.
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The longer the format war goes on, the more opportunity smart players in the cable and IPTV and online spaces have to build market share.—Laura Behrens
Games can give positive emotions and provide relaxation to a person. However, the main purpose of my project is to get a better understanding of the Verilog language. In addition, coding a game into FPGA will help to define the bounds of this programming language.
Toloka is a crowdsourcing platform and microtasking project launched by Yandex to quickly markup large amounts of data. But how can such a simple concept play a crucial role in improving the work of neural networks?
There is one only too obvious problem with relativity of simultaneity in the way it is normally introduced, and I have never found an answer to it – what’s more, I never read or heard anyone formulate it. I will be grateful for an enlightening discussion.
The framework of the thought experiment introducing relativity of simultaneity is this. Two rays of light travel in opposite directions and reach their destination simultaneously in one frame of reference and at different moments in the other.
For example, in the Wikipedia article on the subject you can read:
‘A flash of light is given off at the center of the traincar just as the two observers pass each other. For the observer on board the train, the front and back of the traincar are at fixed distances from the light source and as such, according to this observer, the light will reach the front and back of the traincar at the same time.
‘For the observer standing on the platform, on the other hand, the rear of the traincar is moving (catching up) toward the point at which the flash was given off, and the front of the traincar is moving away from it. As the speed of light is finite and the same in all directions for all observers, the light headed for the back of the train will have less distance to cover than the light headed for the front. Thus, the flashes of light will strike the ends of the traincar at different times’.
I am always not a little surprised at the modesty displayed by the authors of such illustrations. If we grant the statement ‘the light headed for the back of the train will have less distance to cover than the light headed for the front’ to be true – how then do we evaluate the magnitude of the effect? Or, in other words, how much longer is one distance in comparison to the other?
I wonder why IT developer interviews are so strange most of the time. It feels as if the people are looking for computer science teachers, not engineers. All those theoretical questions that have no relation to the working reality. It is strange to be looking for eloquent teachers, who can perfectly explain any term or pattern, and then ask them to do the actual work. Maybe it is the imprint from the years spent in university when the teachers looked like all-knowing gods and seemed to solve any issue in your life. May be, may not. Anyway, these teachers stay in unis and don't do the work.
You know, what would be my universal answer to all interview questions? “I have no idea how and why it works, but I can use it, and I can use it for good”. This is the reality. Actually, no one knows exactly these hows and whys. What is a computer? What is electricity? What is an electron? No one knows for sure. But it works and we use it.
Imagine a famous author, like Stephen King, asked a question about the difference between deus ex machina and Mary Sue. Would his answer change the quality of his books? He may or he may not know all those scientific literature terms, but he can use the language and use it for good.
Every time I turn on my computer it is a wonder. I have no idea what is going on, but it awakes, it becomes alive, and I can communicate with it in its own sublime and subtle language.
Have you ever realised that all these electronic devices are monsters, Frankenstein's monsters? Some pieces of dead matter were put together, and then, with some electricity involved, it suddenly awoke. “It's alive!”. Had Frankenstein any idea why it turned alive? Of course not, or why he was so surprised? Every developer experiences this feeling almost every day. “It's working!”
Andreas Muller, 3D printing enthusiast, developed an open-source project that makes color printing with regular FDM 3D printers possible with the custom system of replaceable printheads and permanent color marker pens.
Engineers from the St. Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University) used 3D printed parts in the design of a six-axis robot featuring increased affordability.
