Python *

Wearable devices present a paradox: the band measures your heart rate, sleep, and activity, but the manufacturer doesn't provide a ready-made open API to integrate this data into third-party systems (like a home monitoring setup or a local database). The official Xiaomi Mi Fitness app shows beautiful graphs, but the data remains 'locked' within the mobile ecosystem.

The initial task was purely practical: to set up automatic collection of health data into a local SQLite database and display reports in a family Telegram bot. Since the band syncs with the app, which in turn syncs with the Xiaomi cloud, the data is guaranteed to be transmitted over the network. I needed to understand the format in which it's transmitted and how to retrieve it.

This article is a technical breakdown of the journey from analyzing network traffic and setting up trust for a custom CA to reverse-engineering Xiaomi's RC4 protocol, decrypting AES/CBC objects from FDS storage, and parsing the proprietary binary sleep format.

The modern world is saturated with all kinds of information, and in our difficult times, it's important to be able to not only find it but also to save it. Many have probably noticed that on YouTube, besides the junk, cats, and other useless things (which we sometimes don't mind watching), there is a lot of useful material on a wide variety of topics. And sometimes it would be nice to save this material for the future, so as not to depend on the changing moods in the world.

In this article, I want to explain how you can download videos, audio (Part 1 of the article), playlists, and entire channels from YouTube (Part 2 of the article) without using a VPN and in pure Python. A quick disclaimer: we won't need a VPN, but we will create our own tool that will solve the "problem with outdated and worn-out equipment Google Global Cache" (you know what I mean). I think this tool will be especially relevant today, when for many Russians, YouTube barely works or doesn't work at all.

Hello, Habr! My name is Nikita Pastukhov—author of FastStream, Principal Engineer, and maintainer of AG2 (a framework for developing agents). I’ve been in development for 8 years, and for the last year, I’ve been up to my ears in agents.

And I want to prove to you that writing your own agent is no more difficult than writing a CRUD

Why does this even need proving? Because there’s a noticeable gap between what’s happening with AI globally and what’s happening in the average Russian company. Globally—every company has an OpenAI subscription, there are a billion startups with AI products, and agents are deeply integrated into the back office. In Russia—it’s “dangerous, we host our own models,” “it’s unclear,” and support chatbots. Globally, engineers already know how to develop agents. In Russia—it’s “what even is that?”

So let’s break down how agents work using the example of OpenClaw—the most hyped “personal AI agent” right now. It lives in your messenger, sorts your email, manages your social media, writes code, and deploys services. Its popularity is a testament to how little people are currently using agents in their daily lives. For those in the know, OpenClaw hasn’t brought anything new to the table.

For over 10 years, C++ has had constexpr, which allows the programmer to cleverly offload some computations to the compiler. At the time, this blew my mind, because the compiler can calculate some rather complex things before the program is even run!

At some point, I thought: if the compiler can calculate everything for you, then why do you need a runtime at all? What are you going to do there—print the answer or something? That’s just silly. That’s unsportsmanlike.

This is where my challenge was born:

“No hands” or “don’t even think about running the exe file”

Every day, every single day, devices in fire suppression systems need to be checked! When these are enormous warehouses spread across vast territories, a security guard has to constantly walk around and inspect all of them — just for this one task. The pressure gauge has become a symbol of punishment in fire safety authorities.

Millions of analog sensors are also used in gas and water distribution systems, as well as in many other industries.

We are all fascinated by the ability of AI to describe what is happening in front of a video camera, but so far we have not seen anyone answer the question — why? Is there at least one truly practical use case?

At SpecLab, however, we came up with our own application...

In the previous article I described my “anime factory” in detail — a pipeline that automatically turns episodes into finished Shorts. But inside that system there is one especially important module that deserves a separate deep dive: a virtual camera for automatic reframing.

In this article, I will break down not just an “auto-crop function,” but a full virtual camera algorithm for vertical video. This is exactly the kind of task that looks simple at first glance: you have a horizontal video, you need to turn it into 9:16, keep a person in frame, and avoid making the result look like a jittery autofocus camera from the early 2010s.

But as soon as you try to build it not for a demo, but for a real pipeline, engineering problems immediately show up:

Hi, Habr!

Over the past few months, I have been building a system that I internally call an “anime factory”: it takes a source episode as input and produces a ready-to-publish YouTube Short with dynamic reframing, subtitles, post-processing, and metadata.

What makes it interesting is not just the fact that editing can be automated, but that a significant part of this work can be decomposed into engineering stages: transcription, audio and scene analysis, strong-moment discovery, “virtual camera” control, and a feedback loop based on performance metrics.

In this article, I will show how this pipeline is structured, why I chose a modular architecture instead of an end-to-end black box, where the system broke, and which decisions eventually made it actually usable.

Our test framework broke as services grew. We redesigned it with clear layers to scale tests, reduce coupling, and support SDKs and AI.

Hello, fellow developers!

Anyone who designs multilayer printed circuit boards in KiCad has faced the need, during routing, to leave large areas free of copper polygons (whether connected to nets or not) for schematic or other reasons. On outer layers, this isn't a big problem. On inner layers, it's a bit different. While it might be fine from a topology perspective, it's not ideal from a manufacturing point of view.

Today, we want to share a plugin we developed to solve this problem. It's a tool for automatically filling free areas on a PCB with non-current-carrying copper elements, either square or round, of a configurable size.

Why is this needed?

At first glance, an empty area on a board is just bare laminate without copper. But for the manufacturer and the end-user of the device, this has two important consequences.

The Chinese neural network DeepSeek has recently gained popularity. Today, we'll figure out how to work with its API, how to pay for it from Russia, and whether there are any difficulties.

Ollama has become the standard for running Large Language Models (LLMs) locally. In this tutorial, I want to show you the most important things you should know about Ollama.

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.

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.

The Problem: Traditional phishing emails are relatively easy to spot. AI-generated ones are not.

python.

Hello, Habr! Today I want to tell you about my project — “Game Engine 3”, a software shell for creating 2D games and applications...

START is an open-source LLM designed for precise calculations and code verification. It addresses two major issues that most standard models face: hallucinations and errors in multi-step calculations. This article explains why these problems arise and how START solves them.

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.