Looking at software development before the cloud revolution, using logging frameworks was kind of common. They supported the dev in formatting the logs, buffering them and sending them to the correct sink such as Elasticsearch. With the rise of cloud platforms applications tended to get smaller units and direct integrations of cloud applications into observability or log archive platforms is now possible with a few lines of configuration. It's time to review if using logging frameworks pays off or a simple 'console.log' and forward the application/function output to some provider such as datadog will do the job.

In the previous blog post we have talked about basic principles of building AI assistants. Let's take them for a spin with a product case that we've worked on: using AI to support enterprise sales pipelines.

So you are building an AI assistant for the business?

This is a popular topic in the companies these days. Everybody seems to be doing that. While running AI Research in the last months, I have discovered that many companies in the USA and Europe are building some sort of AI assistant these days, mostly around enterprise workflow automation and knowledge bases.

There are common patterns in how such projects work most of the time. So let me tell you a story...

The advent of Artificial Intelligence (AI) in text-to-speech (TTS) technologies has revolutionized the way we interact with written content. Natural Readers, standing at the forefront of this innovation, offers a comprehensive suite of features designed to cater to a broad spectrum of needs, from personal leisure to educational support and commercial use. As we delve into the capabilities of Natural Readers, it's crucial to explore both the advantages it brings to the table and the ethical considerations surrounding voice manipulation in TTS technologies.

This report comprehensively analyzes the auto parts sales database. The primary focus is understanding sales trends, identifying high-performing products, analyzing the most profitable products for the upcoming quarter, and evaluating inventory management efficiency.

ChatGPT-4 enables you to analyze database contents with just two simple steps (copy and paste), facilitating well-informed decision-making.

In this blog, we'll explore the proper usage of data analysis with ChatGPT and how you can analyze and visualize data from a SQLite database to help you make the most of your data.

Analyzing and visualizing data from a SQLite database in Python can be a powerful way to gain insights and present your findings. In Part 2 of this blog series, we will walk you through the steps to retrieve data from a SQLite database file named gold.db and display it in the form of a chart using Python. We'll use some essential tools and libraries for this task.

In this new blog series we will give you an overview of how to analyze and visualize data, create code manually and how to make ChatGPT work effectively.
Part 1 deals with the following: In the data-driven era, businesses and organizations are constantly seeking ways to extract meaningful insights from their data. One powerful tool that can facilitate this process is ChatGPT, a state-of-the-art natural language processing model developed by OpenAI.
In Part 1 pf this blog, we'll explore the proper usage of data analysis with ChatGPT and how it can help you make the most of your data.

This article discusses five questions you should ask when hiring an AI. These questions are inconvenient for providers of AI products, but they are necessary to ensure that you are getting the best product for your needs. The article also discusses the importance of testing the AI system on your own data to see how it performs.

Beginning with the September Trustbit LLM Benchmarks, we are now giving particular focus to a range of enterprise workloads. These encompass the kinds of tasks associated with Large Language Models that are frequently encountered in the context of large-scale business digitalization.

In this first part, we explain the basics of machine learning pipelines and showcase what they could look like in simple form. Learn about the differences between software development and machine learning as well as which common problems you can tackle with them.

As the logistics industry becomes increasingly complex, businesses need innovative solutions to manage the challenges of supply chain management, trucking, and delivery. With competitors investing in cutting-edge research and development, it is vital for companies to stay ahead of the curve and embrace the latest technologies to remain competitive. That is why we introduce the Trustbit Logistics Simulator Framework, a revolutionary tool for creating a digital twin of your logistics operation.

For a long time, there was a consensus that Kafka and Event Sourcing are not compatible with each other. So it might look like there is no way of working with Event Sourcing. But there is if certain requirements are met.

The purpose of this blog post is to investigate the performance and prediction speed behavior of popular regression algorithms, i.e. models that predict numerical values based on a set of input variables.

To address common questions concerning the integration of Large Language Models, we have created an LLM Product Leaderboard that focuses on building and shipping products.

This blog discusses the rapid advancements in large language models, particularly highlighting the impact of OpenAI's GPT models.

In my journey to tackle a responsive layout problem, I stumbled upon the remarkable benefits of the :has() pseudo-class. Initially, I attempted various other methods to resolve the issue, but ultimately, embracing the power of :has() proved to be the optimal solution. This blog explores my experience and highlights the advantages of utilizing the :has() pseudo-class in achieving flexible layouts.

When it comes to advanced driver-assistance systems or self-driving cars, one needs to find a way of estimating the distance to other vehicles on the road.