Development

2025. Algorithms have seamlessly integrated into our lives—from work to education, creativity, and daily routines. They edit texts, select fonts, generate ideas, assist with coding, compose music, and more. Frankly speaking, the only thing they can’t do yet is brew your coffee. Although... that might just be a matter of time.

Just two years ago, we were amazed by neural networks hesitantly manipulating objects in photos. Who could predict back then that Will Smith’s spaghetti feast would mark the beginning of such a revolution?

With new opportunities come fresh challenges. How do you navigate this vast landscape? What tools are truly effective? Which ones fit your needs best? Where can you avoid paying, registering, or deciphering complex interfaces?

We’ve compiled a list of reliable and user-friendly neural networks ready for immediate use without unnecessary hassles. The services are categorized neatly: text generation, image creation, video production, music composition, presentations, and much more. Each category showcases three top-rated options!

Yes, many services offer paid subscriptions. But today, we're focusing solely on what works freely, no credit card required!

Have you ever been in the middle of a long coding session with an AI, only to lose everything because of a network glitch, a dead battery, or an accidental terminal close? It’s frustrating to start over from scratch.

Throughout their careers engineers build systems that protect data and guard it against corruption. But what if the right approach is the opposite: deliberately corrupting data, generating it out of thin air, and creating forgeries indistinguishable from the real thing?

Maksim Gramin, systems analyst at Postgres Professional, explains why creating fake data is a critical skill for testing, security, and development — and how to do it properly without turning your database into a junkyard of “John Smith” entries.

I got my Highlighter Extension a Featured badge by doing something surprisingly simple: I just asked for it.

The result? My daily installs roughly doubled. It took about 2–3 days (people report anything from a few days to a month).

За последние два месяца я написал несколько маленьких GLSL-демо. О первом из них, Red Alp, я написал статью. В ней я подробно расписал весь процесс, поэтому рекомендую прочитать её, если вам незнакома эта сфера.

Мы рассмотрим четыре демо: Moonlight, Entrance 3, Archipelago и Cutie. Но на этот раз я расскажу лишь о паре уроков, которые извлёк из каждого. Мы не будем углубляться во все аспекты, потому что это было бы излишне.

Technical support comes in many shapes. Sometimes it’s "try rebooting" or "check the cable." And sometimes it’s deep engineering work you wouldn’t mind dedicating your whole life to. Which version lives inside Postgres Professional, and what’s more important in this field — people or tech? We dig into this with Kamil Karimov, Senior technical support engineer at Postgres Professional.

In this article, I will show you how to build your first AI agent from scratch using Google’s ADK (Agent Development Kit). This is an open-source framework that makes it easier to create agents, test them, add tools, and even build multi-agent systems.

Hello, Habr! I'd like to share my experience developing such a system.
The defining parameters of a domain-specific system are:

My name is Anatoly Bobunov, and I work as a Software Development Engineer in Test - or SDET for short - at EXANTE. When I joined one of our projects, I discovered that several of our test suites took more than an hour to run - painfully slow, to the point where running them for every merge request was simply unrealistic. We wanted fast feedback on each commit, but at that speed, it just wasn’t going to happen.

Eventually, through a series of small but precise improvements, I managed to speed things up to 8.5× faster, without rewriting the tests from scratch. In this article, I’ll walk through the bottlenecks we found and how we fixed them.

What if a trusted application in your Microsoft Teams could silently access your most sensitive information? I found a flaw in the single-sign-on (SSO) mechanism that makes this possible.

In my previous article, I showed how researchers confused being 'aware' (signal registration) with being 'conscious' (subjective awareness). But this is no accident — it is part of a narrative being constructed by AI labs. Anthropic is leading this trend. Let’s break down their latest paper, where a "learned pattern" has suddenly turned into "malicious intent."

Micro frontends: hype or game-changer? How one team cut costs, sped releases, and built scalable apps without chaos.

Everyone knows great documentation makes or breaks a tech product — but few realize how much work goes into it. At Postgres Professional, the docs are written with the same discipline as the code. What’s even more impressive, all of it is done by a team of just ten people. We talked to senior technical writer Ekaterina Gololobova to see how it really works — from the first task to the final commit.

One of the open challenges in the database world is keeping a database consistent across multiple DBMS instances (nodes) that independently handle client connections. The crux of the issue is ensuring that if one node fails, the others keep running smoothly — accepting connections, committing transactions, and maintaining consistency without a hitch. Think of it like a single DBMS instance staying operational despite a faulty RAM stick or intermittent access to multiple CPU cores.

My name is Andrey Lepikhov, and I’d like to kick off a discussion about the multi-master concept in PostgreSQL: its practical value, feasibility, and the tech stack needed to make it happen. By framing the problem more narrowly, we might find a solution that’s genuinely useful for the industry.

In an era dominated by high-level abstractions and a focus on rapid development, the C programming language seems like a relic to many — an "outdated" tool with manual memory management and "dangerous" pointers. But what if these are its greatest strengths?

Maxim Orlov, a programmer at Postgres Professional with 22 years of experience, argues that C is not about quick wins and fast prototypes, but about fundamental control and a deep, philosophical understanding of how computers work. Join us for a journey from an initial frustration with Pascal to a profound appreciation for C, and learn why this "bastion of calm" is more relevant than ever.

I’ve been using the Gemini CLI a lot lately for my coding projects. I really like how it helps me work faster right inside my terminal. But when I first started, I didn’t always get the best results. Over time, I’ve learned some simple tricks that make a huge difference. If you use the Gemini CLI, I want to share my top 10 pro tips. If you are ready, then let’s get started.

When I started working with AWS SageMaker, one of the most common questions was: “Which inference type should I choose for my model?” SageMaker offers four different options, and at first glance, the differences between them aren’t always obvious. Let’s break down when and which approach to use.

In the big data era, data is one of the most valuable assets for enterprises. The ultimate goal of data analytics is to power swift, agile business decision making. As database technologies advance at a breathtaking pace in recent years, a large number of excellent database systems have emerged. Some of them are impressive in wide-table queries but do not work well in complex queries. Some support flexible multi-table queries but are held back by slow query speed.

Each type of data has a data model that best represents them. However, in real business scenarios, there is no such thing as ultra-fast data analytics under the perfect data model. Big data engineers sometimes have to make compromises on data models. Such compromises may cause long latency in complex queries or damage the real-time query performance because engineers must take the trouble to convert complex data models into flat tables.

New business requirements put forward new challenges for database systems. A good OLAP database system must be able to deliver excellent performance in both wide-table and multi-table scenarios. This system must also reduce the workload of big data engineers and enable customers to query data of any dimension in real time without worrying about data construction.

Investing in Application Security (AppSec) and DevSecOps is no longer optional; it's a strategic imperative. However, securing budget and justifying these initiatives requires moving beyond fear and speaking the language of business: Return on Investment (ROI).

This guide provides a structured framework for calculating the costs and benefits of embedding security into your software development lifecycle (SDLC). By understanding and applying concepts like Total Cost of Ownership (TCO), Lifecycle Cost Analysis (LCCA), and Return on Security Investment (ROSI), you can build a compelling financial case, guide your security strategy, and prove tangible value to stakeholders.

Go client for Gotenberg — document conversion service supporting Chromium, LibreOffice, and PDF manipulation engines.

Features

- Chromium: Convert URLs, HTML, and Markdown to PDF

- LibreOffice: Convert Office documents (Word, Excel, PowerPoint) to PDF

- PDF Engines: Merge, split, and manipulate PDFs

- Webhook support: Async conversions with callback URLs

- Stream-first: Built on httpstream for efficient multipart uploads