Napkin AI is an innovative artificial intelligence tool that transforms text descriptions into visual sketches and diagrams. Launched in 2023, this toolallows users to quickly convert their ideas into visual representations, similar to drawing on a napkin — hence the name. In this tutorial, I will walk you through its top features and provide a video demonstration.
When we go abroad for vacation or meet a foreigner on the street who doesn’t speak Russian but is trying to ask, “Where is the restroom? How do I get to…”, we wonder how to explain things to them in Russian in a way they would understand.
I asked myself a similar question when trying to explain something to a colleague using SQL while they were working with Java. The main goal of my work was to create a quality test model. Without it, there would be no proper regression testing later on.
I started by building a framework filling it with test cases. We held a meeting where we discussed priority of positive and negative test cases briefly. When developing the test scenarios, I used the incremental model, but as practice showed, this approach also required an iterative method. For example, it is like having the outline of the Mona Lisa first, then adding colors, painting the background, and so on.
It’s better to maintain the checklist in Excel format to add columns, write notes, and more. And let’s not forget that, as we take on the role of Leonardo da Vinci, we use different colors and get creative.
I am a manager by profession specializating in Production Management. My motivator is the Theory of Constraints (TOC) methodology, which focuses on identifying and managing the key constraint of a system to determine the efficiency of the entire system as a whole:
The explicit reparameterization trick is often used to train various latent variable models due to the ease of calculating gradients of continuous random variables. However, due to its peculiarities, explicit reparameterization trick is not applicable to several important continuous standard distributions, such as mixture, Gamma, Beta and Dirichlet.
An alternative method for calculating reparameterization gradients relies on implicit differentiation of cumulative distribution functions. The implicit reparameterization trick is much more expressive and applicable to a wider class of distributions
This article provides an overview of various reparameterization tricks and announces a new Python library, irt.distributions, for sampling from various distributions using the implicit reparameterization trick.
This article is the first in the series about the upcoming PostgreSQL 18 release. Let us take a look at the features introduced in the July CommitFest.
Planner: Hash Right Semi Join support
Planner: materializing an internal row set for parallel nested loop join
Planner support functions for generate_series
EXPLAIN (analyze): statistics for Parallel Bitmap Heap Scan node workers
Functions min and max for composite types
Parameter names for regexp* functions
Debug mode in pgbench
pg_get_backend_memory_contexts: column path instead of parent, new column type
Function pg_get_acl
pg_upgrade: pg_dump optimization
Predefined role pg_signal_autovacuum_worker
Have you ever felt overwhelmed trying to make sense of dense notes or wished you could listen to them like a podcast while on the go? In this article, I’ll share the tool that could help you turn text into engaging audio podcasts; I believe it could help you learn new things faster and more effectively.
Mobile developers can use Apple Wallet and Google Wallet integrations for different types of apps.
Once we already wrote our own library for Apple Wallet in React Native because we couldn't find anything ready to use. But now that Google Wallet is fully released, it's time to update it.
Now, this library provides integration with both Apple Wallet on iOS and Google Wallet on Android. It allows you to add, remove, and check for existing passes on iOS, and add passes to Google Wallet on Android.
In today’s article, I’m going to show you a simple, free tool that can help you create great prompts for any AI model you use—whether it’s ChatGPT, Midjourney, or something else.
OpenStreetMap (OSM) is a global project formed around a geographic information database which is being filled by all comers — both enthusiasts and interested companies. Anybody can contribute, but the openness has its downside: incorrect edits often get into the database. Hence plenty of validators of OSM data have been written which allow to maintain the data quality at an acceptable level.
Since 2016 there exists an open source subway preprocessor that validates (generates error reports) rapid transit routes in OSM for completeness and logical/topological errors, and converts them into formats that are suitable for routing and rendering, e.g. GTFS. Besides OSM data it takes a list of public transport (PT) networks which contains the checking information about the number of lines, stations etc. per a PT network. The preprocessor has successfully proven itself in the preparation of PT data for applications such as Maps.me and Organic Maps.
In this article, I would like to share an approach to detecting one of the types of errors that occur quite often in OSM data and automatic detection of which is somewhat challenging. It's an accidental loss of a station from a route. The source code of the validator and the described algorithm are open source. But first, let's define the concepts used to represent PT data in OpenStreetMap.
Every SEO professional has encountered CAPTCHA. If not, they are either not professionals, misunderstand the term SEO (possibly confusing it with SMM or CEO), or are very new to this challenging field.
One could endlessly deny and argue that CAPTCHA is overrated and does not deserve significant resources. But these arguments end the moment one needs data from a search engine result page, like Yandex, without knowledge of XML requests… Or, say, a client wants to scrape the entire Amazon (just because they do) and offers good pay...
Two powerful AI tools are making waves in the world: NEW ChatGPT Search and Perplexity AI. But which one is better for you? I've tested them and want to share my results.
I recently explored two powerful AI tools — NotebookLM and Perplexity Spaces — and want to share my comprehensive review of these research assistants.
In the last couple years Artificial Intelligence has taken the world by storm, starting to revolutionize the tech industry in many aspects, including UX. AI can help UX designers to automate some routine tasks that take a lot of time, providing more space for creativity and product quality improvement. It’s not likely that AI might replace human designers in the near future, but it can definitely ease the work of designers in some ways.
Let’s discuss how AI can be applied in UX Design these days:
So let’s talk about the word yes. Another way to say it is that, we need to give some positive statements to make a sale.
I said, in the last article that Russians and Eastern European people are much more reserved with giving the answer of yes. So in this culture, it is normal to wait to have more facts about what the work will be for giving a positive answer. But this creates real problem and an absolute block when it comes to selling your IT software and project development to western cultures!
Let’s break this down into understandable pieces.
In the world of blockchain, where the word "gas" is most often associated not with gas stations, but with fees for transactions in Ethereum, the search for ways to minimize these costs is becoming increasingly relevant. Less heartache for an empty wallet and more time to solve really important issues... for example, what to cook for lunch =)
Similar to the epic Batman vs Superman battle, compare GSN and Account Abstraction. As in any superhero story, each of them has its own unique abilities and disadvantages, which we will look at in detail.
Imagine: you've just created an incredible decentralized application, and it's so cool that even your grandmother wanted to try it. But once users are faced with the need to pay a commission, the entire UX (User Experience) rapidly slides down like a ball down a slide. Blockchain promises a bright future in which decentralization, transparency and security are our best friends, and it makes us pay for daily operations. Imagine if you had to pay every time you like on social networks or send a message in a messenger. It's terrible, isn't it? But dApps users face something like this every day.
But now, like a prince on a white horse, GSN (Gas Station Network) appears. With its help, developers can make their applications gas-less, and users will finally be able to forget about commissions like a nightmare.
In this article, we will look at what GSN is, how it works, and how to implement it into your projects to please users.
In today's world, where businesses increasingly rely on effective communication, cloud technologies offer a wide range of solutions to optimize interactions both within the company and with customers. Three of the most popular options are UCaaS, CCaaS, and CPaaS. While they all fall under the umbrella of cloud communications, their functionality and applications differ significantly. Let's delve into the details.
UCaaS (Unified Communications as a Service)
UCaaS unifies various communication channels, such as voice calls, video conferencing, messaging, and email, into a single platform. This allows employees to easily communicate and collaborate with each other, regardless of their location.
Benefits of UCaaS:
In this article, I'm going to show you how you can easily transcribe audio and video files on your own computer using Whisper WebGPU — without needing an internet connection.
Firebase Performance offers a free, comprehensive solution for tracking app performance. As part of the Firebase suite, it provides seamless integration with other Firebase services like Crashlytics, making it easier to manage all performance and crash data in one place. This not only simplifies access but also streamlines team collaboration without the need to manage multiple platforms.
OpenAI has recently launched a new feature called Canvas for ChatGPT, designed to enhance users' interactions with AI for writing and coding projects. This new interface aims to provide a more collaborative and interactive experience, moving beyond the traditional chat format. In this article, I'll cover the top features of ChatGPT Canvas and show how to use it.
While in 2024 using XML looks a bit outdated, it still happens. And sometimes it happens we are to deal with XML having "free-structure", i.e. it couldn't be parsed into tree of user-defined structs. For JSON there still is a way to parse it with a single call into map[string]any and work with it using careful type assertions. Regretfully, there is no similar feature for XML (in Golang). Here I'll draft suitable function and demonstrate it - both for others and for myself if I ever need this again (recreating it from scratch may be somewhat painful).