An application is running, the database is responding, and individual features seem to work as expected. But as the number of users and the amount of data grow, some pages start loading more and more slowly. The cause might not be an underpowered server or a poorly designed database. Sometimes, the problem is simply that the application is executing too many unnecessary database queries.

This issue is known as the N+1 Query Problem, and it can occur in Django applications as well. The good news is that, in many cases, it can be resolved with a relatively simple optimization.

Imagine your team has spent weeks developing a new app. The design looks great, all the buttons work, and the developers are happy with the result. Then the first user gets their hands on it. They cannot figure out where to create an account, overlook an important feature, and eventually close the app.

The problem may not be the technical implementation. The app might simply not be as intuitive as you expected.

That is exactly why UX testing matters. It helps you identify problems in how people use your product before they turn into costly fixes or reasons for users to switch to a competitor.

The good news is that you do not need an expensive research agency or a specialised testing lab to get started. You need to know what you want to learn, find a few suitable participants, and observe how they interact with your product.

Imagine asking a company chatbot: “What is our refund policy?” The chatbot responds fluently and confidently. The problem is that it uses general information that may not match your company’s actual rules.

How can you make sure AI answers questions based on your documents, internal policies, or product information rather than relying only on what it learned during training?

One solution is RAG (Retrieval-Augmented Generation). It is an approach that combines information retrieval with AI-generated answers. This allows a language model to work with a company’s own data without having to train a new model every time the information changes.

Imagine you’re riding the subway, open your favorite app, and realize you have no internet connection. You want to add a task, update your shopping list, or read a saved article. But the app keeps loading or displays an error message.

Imagine you have a company database containing thousands of documents. A user types the following into the search bar:

“How can I change my password?”

A traditional search engine will look for documents containing words such as change or password.

But what if the relevant document is called “Recovering Your Login Credentials”?

The meaning is obvious to a person. A traditional keyword search, however, may not recognize that both queries are about the same thing.

This is where vector search comes in. Instead of looking only for matching words, it compares the meaning of content represented as numerical vectors.

Push notifications look simple. Someone writes a message, clicks Send, and a few moments later a banner appears on the user’s phone.

But quite a lot happens between these two moments.

The message has to travel through the backend, the appropriate push service, the operating system, and finally reach the application on the device. And when something goes wrong, the problem may not be with the message itself.

So let’s take a look at what happens to a push notification from the moment it is sent until it appears on the user’s phone.

An AI agent can look like it is working perfectly. It receives a task, uses the right tools, and returns a result that looks correct.

But what if it just got lucky?

What if it used the wrong tool, made ten unnecessary API calls, or makes a completely different decision when given a similar task?

That is why evaluating AI agents is becoming less about asking “Does it work?” and more about asking “How well, consistently, and safely does it work?”

Evaluating AI agents means systematically testing not only the final result, but also the process that led to it. Modern evaluation approaches look at an agent’s decisions, tool calls, costs, safety, and behavior when things go wrong.

Developing an app can sound simple at first.

You have an idea, you roughly know what the app should do, you find a development team, and work begins. A few weeks later, however, the first questions start to appear: What are we actually supposed to build now? Which version of the design is final? Why are we working on this feature when we discussed something else? And why has the deadline moved?

Often, the problem is not the developers.

The problem may be how the entire project is being managed.

Good project management is not about constantly checking tasks and sending status updates. In digital product development, its main purpose is to create a system where everyone knows what needs to be done, why it needs to be done, and what is required to move the project forward.

An application can be slow even when you have a well-designed database and a powerful server. Sometimes the problem is not that the database cannot handle a query, but that the application keeps asking for the same data over and over again.

This is where caching comes in.

Caching allows frequently used data to be stored in a faster layer so it does not have to be retrieved or calculated from scratch every time. One of the most common tools used for this purpose is Redis.

When it comes to growing a mobile application, we often talk about sales funnels. A user discovers the app, signs up, starts using it, and eventually takes the desired action – such as making a purchase or subscribing to a service.

In recent years, however, another model has been receiving increasing attention: growth loops.

While a funnel describes the user's journey from the first touchpoint to conversion, a growth loop aims to create a cycle in which one user action helps generate new users or additional value.

So, what is the difference between the two, and when does each approach make sense?