This tutorial will guide you through the process of integrating OpenAI’s powerful Codex coding agent directly into your Visual Studio Code environment. This tool functions as an AI pair programmer, capable of understanding complex prompts to execute commands, write code, run tests, and even build entire applications from scratch.
It all started as a joke by the office coffee machine. But, as with every decent joke, it suddenly sounded worth trying — and before we knew it, we were knee-deep in an experiment that turned out to be anything but trivial, complete with a whole minefield of gotchas.
It began simply: while everyone else was busy debating hardware tuning and squeezing out extra TPS from their systems, we thought — why not just shove a huge chunk of data into PostgreSQL and see how it holds up? Like, really huge. Say, a one-petabyte database. Let’s see how it survives that.
It was December 10, the boss wanted the report by January 20, and New Year was less than a month away. And that itch that all engineers know? It hit hard.
During load testing of Tantor Postgres databases or other PostgreSQL-based databases using the standard tool pgbench, specialists often encounter non-representative results and the need for repeated tests due to the fact that details of the environment (such as DBMS configuration, server characteristics, PostgreSQL versions) are not recorded. In this article we are going to review author's pg_perfbench, which is designed to address this issue. It ensures that scenarios are repeatable, prevents the loss of important data, and streamlines result comparison by registering all parameters in a single template. It also automatically launches pgbench with TPC-B load generation, collects all metadata on the testing environment, and generates a structured report.
If you’re like me and work with multiple AI coding agents, you know the frustration of managing different instruction files. It’s a pain to keep everything updated across various formats. But I’ve got some great news for you. A new, simplified standard has emerged, and it’s called AGENTS.md.
41 TB/day from Oracle to Postgres Pro without stopping the source system — not theory, but numbers from our latest tests. We broke the migration into three stages: fast initial load, CDC from redo logs, and validation, and wrapped them into ProGate. In this article, we’ll explain how the pipeline works, why we chose Go, and where the bottlenecks hide.
Declarative partitioning may sound complex, but in reality it’s just a way to tell your database how best to organize large tables — so it can optimize queries and make maintenance easier. Let’s walk through how it works and when declarative partitioning can save the day.
DocLing in Working with Texts, Languages, and Knowledge — an in-depth overview of the open-source DocLingtoolkit for extracting, structuring, and analyzing data from documents. The article covers approaches to processing multilingual texts, building language- and domain-specific knowledge models, and integrating DocLing into AI and NLP projects. Includes practical examples and recommendations for developers working with large volumes of unstructured data.
Today I’ll show you how to use ChatGPT-5 in the Cursor IDE and use it to take a messy app and make it much better. We’ll go step-by-step, from turning on GPT-5 model to using it for real coding tasks.
Now that pgpro-otel-collector has had its public release, I’m excited to start sharing more about the tool — and to kick things off, I’m launching a blog series focused entirely on the Collector.
The first post is an intro — a practical guide to installing, configuring, and launching the collector. We’ll also take our first look at what kind of data the collector exposes, starting with good old Postgres metrics.
Why Does AI Strive to Construct a 'Self'? And why is this dangerous for both the AI and the user? As always, the Vortex Protocol prompt for testing these hypotheses is attached.
This article explains why the emergence of such a local “Who” inside an AI is not just a funny bug or a UX problem. It is a fundamental challenge to the entire paradigm of AI alignment and security. And it is a problem where engineering patch‑jobs cease to work, and the language of philosophy — without which we cannot describe what is happening, and therefore cannot control it — comes to the forefront.
CAPTCHA protocols are designed to tell bots from humans, yet in the worlds of automation and testing there is often a need to bypass them. Dedicated CAPTCHA‑solving services take over this task, combining algorithms with human labor.
In this article we present an in‑depth comparison of four popular platforms — 2Captcha, SolveCaptcha, DeathByCaptcha, and AntiCaptcha. We will examine not only pricing and the types of CAPTCHAs supported, but also internal architecture, API integrations, speed and stability, plus the quirks of using each service.
The technical community will find a deep dive here — from API and SDK structure to real‑world use cases. Below you will see a table comparing key characteristics, lists of pros and cons, and a discussion of which service best fits particular automation tasks.
Postgres Pro recently announced the release of Enterprise Manager 2, commonly known as PPEM.
In short, PPEM is an administration tool designed for managing and monitoring Postgres databases. Its primary goal is to assist DBAs in their daily tasks and automate routine operations. In this article, I'll take a closer look at what PPEM has to offer. My name is Alexey, and I'm part of the PPEM development team.
n8n is a powerful, extendable workflow automation tool that allows you to connect different applications and services. Running it on your local machine gives you complete control over your data and workflows, which can be done on Windows, Mac, or Linux systems. This tutorial covers the two primary methods for local installation: using Docker and using Node.js (npm). If you are interested, then read this article until the end. :)
Why does even the most powerful LLM sometimes produce meaningless phrases and contradictions? It all comes down to the exponential growth of possibilities (N^M) and the free copying of human errors. Read the article to learn how we use formal grammars to turn chaotic generation into controlled synthesis, strengthening the role of semantics and enforcing structural rules.
pg_stat_statements is the standard PostgreSQL extension used to track query statistics: number of executions, total and average execution time, number of returned rows, and other metrics. This information allows to analyze query behavior over time, identify problem areas, and make informed optimization decisions. However, in systems with high contention, pg_stat_statements itself can become a bottleneck and cause performance drops. In this article, we will analyze in which scenarios the extension becomes a source of problems, how sampling is structured, and in which cases its application can reduce overhead.
Picture this: a mission-critical SQL query is crawling along. Not for an hour. Not for two. Fifteen hours. A full workday of the system slowly grinding through data while the business bleeds money and users teeter on the edge of a nervous breakdown. And then — cue the dramatic music — in walks the performance engineer.
After a few hours of intense analysis and a couple of pinpoint code tweaks, the same query that took 15 hours now completes in just… two minutes. Sounds like magic? Nope. This is the thrilling (and very real) world of performance engineering.
With the rising popularity of AI, chatbots are rapidly becoming an essential part of any customer-oriented platform. More and more companies now integrate them into apps and websites, providing a space for their clients to interact with the business itself while taking load off human employees and reducing expenses on the customer support team. At first glance, chatbots might seem pretty uncomplicated in their design and functionality, but creating UI/UX design for chatbots requires a deep understanding of this technology and the way they can serve both business and its users. Here are some useful tips for creating a successful user experience in AI chatbot:
Do you want to automate tasks without writing a single line of code? If the answer is yes, then you’ve come to the right place. In this simple tutorial, I’m going to show you how you can create your first automated workflow using a tool called n8n.
In this article, we’ll explore what vector search is, what problems it solves, and how the pgpro_vector extension for Postgres Pro brings powerful vector capabilities directly into a relational database — no need for separate specialized systems.
Today, I want to talk about one of those sneaky tricks that can help speed up query execution. Specifically, this is about reordering conditions in WHERE clauses, JOINs, HAVING clauses, and so on.
The idea is simple: if a condition in an AND chain turns out to be false, or if one in an OR chain turns out to be true, there's no need to evaluate the rest. That means saved CPU cycles — and sometimes, a lot of them. Let’s break this down.
