Open source *

Команда Python for Devs подготовила перевод статьи о том, как превратить код Python в элегантные математические формулы прямо в Jupyter-ноутбуке. Если вы строите финансовые модели, алгоритмы или научные расчёты — LaTeX поможет оформить их профессионально. В статье разбираем четыре инструмента: IPython.display.Latexдля ручного рендеринга, handcalcs для пошаговых вычислений, latexify-py для автоматического преобразования функций и SymPy для символьной математики.

Команда Go for Devs подготовила перевод k8s-1m — кейса о том, как не догадки, а измерения двигают пределы Kubernetes. Ключевые идеи: изоляция QPS по типам ресурсов, смягчение гарантий хранения для эфемерных данных, и шардирование планировщика. Полезно всем, кто проектирует крупные кластеры или хочет работать с ними.

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

Learning Shopify has been on my bucket list for a few years now. Plenty of people in my circle — friends, colleagues, and fellow devs — are all somehow involved with Shopify in one way or the other. Earlier this year, I finally had some breathing room between projects, so I figured it was the perfect time to give Shopify a proper look.

I started exploring the platform by setting up a dev store, poking around the admin, and skimming through the API manual. While this was a quick and easy start, it didn’t give me a deeper understanding of the platform. Plus, clicking my way through the UI felt repetitive and tedious.

That got me thinking: is there a more efficient, developer-centric way to manage a store? Something that I could run in a terminal, plug into a CI/CD pipeline, or script my way out of those mundane tasks.

This world needs a new theory — a theory that could describe all the theories on the planet. A theory that could easily describe philosophy, mathematics, physics, and psychology. The one that makes all kinds of sciences computable.

This is exactly what we are working on. If we succeed, this theory will become the unified meta-theory of everything.

A year has passed since our last publication, and our task is to share the progress with our English-speaking audience. This is still not a stable version; it’s a draft. Therefore, we welcome any feedback, as well as your participation in the development of the links theory.

As with everything we have done before, the links theory is published and released into the public domain — it belongs to humanity, that means, it is yours. This work has many authors, but the work itself is far more important than any specific authorship. We hope that today it can become useful to more people.

We invite you to become a part of this exciting adventure.

Author: Sergey Lukyanchikov, C-NLTX/Open-Source

Disclaimer: The views expressed in this document reflect the author's subjective perspective on the current and potential capabilities of jBPM.

This text presents jBPM as a platform for orchestrating external AI-centric environments, such as Python, used for designing and running AI solutions. We will provide an overview of jBPM’s most relevant functionalities for AI orchestration and walk you through a practical example that demonstrates its effectiveness as an AI orchestration platform:

Написал лонгрид на английском о текущем состоянии открытых средств проектирования ASIC-ов. Заодно познакомил англоязычных читателей с практиками шаманов Сибири и фигурой Ивана Сусанина. Упомянул планируемые семинары в Мексике и Армении.

A text on the current state of Open-source ASIC design tools. Includes side discussions of the upcoming hackathons in Mexico and Armenia, Docker and Python, Static Timing Analysis and RISC-V, Siberian shamans and treacherous swamps in Belarus.

Trends change rapidly in UI/UX, and new year means quite a few new emerging innovations that designers should be aware of in order to create relevant and engaging designs. Some trends stay on from previous years, some are completely new, and some might end up being the fleeting ones, so it’s important to pearl off those that truly elevate user experience and make a better impact on your product. Let’s discuss some of the most anticipated UI/UX trends of the coming year.

The first meetups of the Portable SystemVerilog Examples group at Hacker Dojo in Mountain View, California were a kind of brainstorming sessions. We discussed the electronic industry, the essence of modern chip design, and the challenges of educating new design engineers. Then we moved to a new mode of action. We started weekly meetings of the core R&D team with the goal to prepare educational materials for the events for a larger audience. The meetings are generally held on Sundays from 11 pm to 2 pm. If you cannot come to Mountain View, you can join online.

We are focusing on interview-level microarchitectural and CPU design examples, providing FPGA vendor-neutral infrastructure and compatibility with open-source ASIC design tools.

Ссылка на русскую версию / link to Russian version

Understanding valid/ready protocol is extremely important for every microarchitect.

Valid/ready is one of the main protocols used to organise flow-control inside a logic block as well as on inter-block (SoC) level.

In the last lesson, we explored FIFO buffer using hdlgadgets - human-in-the-loop HDL training tool.

This time we will take two FIFO buffers (which form a pipeline with valid/ready handshakes) and will experiment with it by changing flow-control logic of the pipeline.

We will show that valid/ready is not only a mechanism for transferring data from one FIFO queue to another, but also a method for organizing various kinds of logical functionality between queues.

If you have not worked with valid/ready protocol before, you will be surprised how easy it is to achieve desired functionality of the design by simply writing couple of lines of Verilog code in the handshaking logic block between two FIFOs.

We released the new version of Chipmunk, software for viewing/analyzing log files. V.3 is wholly reworked with an accent to performance and rethought considering usability. Below short list of the most important changes and a general description.

Ссылка на русскую версию / link to Russian version

FIFO is a key concept in hardware design. Understanding of FIFO is necessary for understanding the valid/ready protocol, which in turn is necessary for organisation of flow-control within a design.

Unfortunately, there are very few books on this topic, and to be fair, microarchitectural concepts are quite difficult to master from books, since understanding of these concepts are coming with practice. In other words it is more about developing hardware intuition.

The idea of the HDL training tool is that it can help develop a hardware intuition, providing the opportunity to explore ready-made scenarios in a step-by-step interactive way. The tool also provides detailed visualization of a simulated scenario.

Since the tool is a front-end for the HDL simulator, the real, synthesized SystemVerilog is executed on the simulator itself, which can be viewed and even modified.

So, the video of exploring FIFO on the training tool is here:

Currently I am investigating firmware development for STM32 microcontrollers and I would like to share with you my experience for doing it in Qt Creator IDE.

There are a lot of IDEs, which are used for firmware development of STM32. Some of them, being quite comfortable, have restrictions for trial license. For example, the one of the most known IDE, IAR Embedded, suggests either a limited amount of product usage time (30 days) or the limited firmware size of 32 MB, which is not too much.

Within this scope of the publication, we investigate the method of setting up an environment that allows one to develop the full value of the STM32 firmware in Qt Creator.

lsFusion platform is designed for rapid development of business applications. It is distributed under the terms of a Lesser General Public License (LGPLv3). The source code of the platform is available on Github.

lsFusion is best suited for creating complex systems with large numbers of entities and forms, where users need to input and process large amounts of data. However, the platform can also be used to quickly create simple applications instead of spreadsheets when Excel’s functionality is not enough.

At the same time the use of the platform will not give a great advantage when developing applications aimed at interaction with a large number of “external” users or without the need for any complex calculations. You should also take into account that the web interface is a single page application using JavaScript. Therefore, the lsFusion platform is not well-suited for creating websites, for example.

OpenTeleletry Collector is an open source data collection pipeline that allows you to monitor CPU, RAM, disk, network metrics, and many more.

Collector itself does not include built-in storage or analysis capabilities, but you can export the data to Uptrace and ClickHouse, using them as a replacement for Grafana and Prometheus.

When compared to Prometheus, ClickHouse can offer small on-disk data size and better query performance when analyzing millions of timeseries.

I will continue the story "How to put the whole world into a regular laptop: PostgreSQL and OpenStreetMap" with secrets about OpenStreetMap geodata, on which many companies have built their business, but not everyone shares the details... Well, today we will open crucial details.

The OSM database in PosgreSQL after loading from the dump takes up more than 587 GB. This is already a large database by the standards of a DBMS, and one huge table for each type of object will not work. For manageability, such data must be partitioned, it's good that PostgreSQL supports declarative data partitioning. It remains only to figure out how to split geographical data. After searching and comparing, the H3 hierarchical hexagonal geospatial indexing system came to rescue. All this was implemented in my openstreetmap_h3 project for fast processing and loading of the world dump into the PostGIS database.

I considered following options from geopartitioning systems...

When a person used to say that he controls the whole world, he was usually placed in the next room with Napoleon Bonaparte. I hope that these times are in the past and everyone can analyze the geodata of the entire Earth and get answers to their global questions in minutes and seconds. I published Openstreetmap_h3 - my project, which allows you to perform geoanalytics on data from OpenStreetMap in PostGIS or in any query engine that can work with Apache Arrow / Parquet.

First of all, I say hello to the haters and skeptics. What I developed is really unique and solves the problem of transforming and analyzing geodata using the usual and familiar tools available to every analyst and data science specialist without bigdata, GPGPU, FPGA. What looks easy to use and code now is my personal project where I have invested my vacations, weekends, sleepless nights and a lot of personal time over the past 3 years. Maybe I will share the background of the project and the rake that I went through, but first I will still describe the end result.

Today I will discover America to you based on OpenStreetMap data in PostgreSQL15/PostGIS and my project openstreetmap_h3. Let's run the query and compare its execution time on the Citus column store in PostgreSQL and on the standard 100GB database partitioned by H3 geoindex.

We will find the top15 buildable locations in North America and the total length of roads, as well as their type and surface. I will not overload the publication with program logs, let's focus on the data! You can easily repeat all requests yourself on your laptop/computer.

Do you need to produce satellite interferometry results for your work or study? Or should you find the way to process terabytes of radar data on your common laptop? Maybe you aren't confident about the installation and usage of the required software. Fortunately, there is the next generation of satellite interferometry products available for you. Beginners can build the results easily and advanced users might work on huge datasets. Open Source software PyGMTSAR is available on GitHub for developers and on DockerHub for advanced users and on Google Colab for everyone. This is the cloud-ready product, and it works the same as do you run it locally on your old laptop as on powerful cloud servers.