#python
This tool helps you easily download PDF textbooks from the National Primary and Secondary School Smart Education Platform by extracting the book URLs and saving the files automatically with correct names. Since February 2025, the platform requires login, so you must set an Access Token (login credential) in the tool to download books. It supports batch downloads, shows progress, works on Windows, Linux, and macOS, and saves your token securely on your device. This makes getting and managing digital textbooks much faster and more convenient for study or teaching.
https://github.com/happycola233/tchMaterial-parser
#python
fairchem is a centralized open-source library by FAIR Chemistry that provides advanced machine learning models, datasets, demos, and tools for materials science and quantum chemistry. You can install it via pip and use pretrained models through the FAIRChemCalculator with ASE, enabling tasks like catalysis, inorganic materials, molecules, MOFs, and molecular crystals. It supports simulations such as structure relaxation and molecular dynamics. Version 2 is a major update and not compatible with version 1 models. Using fairchem helps you quickly apply state-of-the-art AI models to accelerate research and discovery in chemistry and materials science[1][2][4][5].
https://github.com/facebookresearch/fairchem
#python
This library helps you test and compare language models by running standard benchmarks like math, reading, coding, and general knowledge tasks. It uses simple, clear instructions to measure how well models perform without complicated prompts, reflecting real-world use better. You can evaluate many models, including OpenAI’s and others, to see their strengths and weaknesses on tasks like problem-solving and factual accuracy. This transparency helps you pick the best model for your needs and understand their capabilities. The library supports easy setup and running of tests via APIs, making it practical for developers and researchers to assess model quality quickly and reliably.
https://github.com/openai/simple-evals
#python
Torchtitan is a PyTorch-native platform designed for easy and large-scale training of generative AI models like Llama 3.1. It supports advanced distributed training techniques such as multi-dimensional parallelism, activation checkpointing, and Float8 precision, enabling efficient use of many GPUs. Torchtitan is modular and cleanly coded, making it easy to extend and customize for different AI research and development needs. It also integrates with PyTorch’s latest features like torch.compile for faster training. This platform helps you rapidly experiment and scale AI model training with minimal code changes, boosting productivity and innovation in generative AI development[1][3][4][5].
https://github.com/pytorch/torchtitan
#python
FieldStation42 is a project that lets you experience old TV like it was in the past. It uses a Raspberry Pi to simulate multiple TV channels with shows and commercials. You can set up different channels, schedule shows, and even add seasonal content. The system supports multiple channels playing at the same time and can automatically insert commercials. This project is great for people who miss the old TV experience and want to relive it with a nostalgic feel. It requires some technical setup but offers a fun way to enjoy retro TV.
https://github.com/shane-mason/FieldStation42
#python
The Jelly Evolution Simulator is a program that lets you watch jelly-like creatures evolve over time. You can run it using a simple command in Python. The simulator allows you to control various features like closing the program, toggling markers, storing species, and changing colors. It also lets you scroll through different generations to see how the creatures change. This tool is useful for understanding how evolution works in a fun and interactive way. It helps users visualize how small changes can lead to different outcomes over time.
https://github.com/carykh/jes
Python bilan yo‘lingizni boshlayapsizmi? Mana sizga kerakli maslahatlar!
Python — oddiy sintaksis, kuchli kutubxonalar va keng imkoniyatlarga ega dasturlash tili. Yangi boshlayotgan bo‘lsangiz, quyidagilarni yodda tuting:
1. Har kuni oz bo‘lsa ham kod yozing
Python’da kuchayishning eng yaxshi yo‘li — amaliyot. Har kuni 30 daqiqa mashq qilish ham yetarli.
2. input(), if, for, def— bu sizning do‘stlaringiz!
Dasturlash asoslari — sizga har qanday murakkab loyihaga eshik ochadi.
3. Real project boshlang!
Masalan: kalkulyator, To-do ilova, Telegram bot yoki oddiy CRUD tizimi. O‘rganishdan ko‘ra, real loyiha qilish 3x ko‘proq foyda beradi.
4. error ko‘rsangiz — xafa bo‘lmang😁
Python xatoliklarni aniqlashni o‘rgatadi. Har bir xatolik — yangi bilim! 🔥
5. Ustozlar va hamjamiyatdan foydalaning
👉Stack Overflow
👉 YouTube’dagi Python kurslar
👉Exercism, Codewars, LeetCode — Python masalalar uchun zo‘r saytlar!
💡Esda tuting:
"Birinchi 100 ta kodlaringiz ishlamasligi normal holat. Muhimi — siz har kuni urinyapsiz."
#python
💻@dasturlash_hayoti— dasturchilar hayoti va dasturlash olami haqida qiziqarli loyiha!
👩💻 Python для сетевых инженеров.
• Python уверенно лидирует в рейтингах популярности языков программирования, и не зря — на этом языке можно решать самые разные задачи и при этом сильно экономить время. Я нашел очень полезную книгу, в которой рассматриваются основы Python с примерами и заданиями построенными на сетевой тематике. Надеюсь, что многим из Вас пригодится данный материал и поможет приступить к изучению этого языка программирования.
• Книгу можно читать в онлайне (по ссылкам ниже), либо скачать в удобном формате и на разных языках:
• Основы Python:
➡Подготовка к работе;
➡Использование Git и GitHub;
➡Начало работы с Python;
➡Типы данных в Python;
➡Создание базовых скриптов;
➡Контроль хода программы;
➡Работа с файлами;
➡Полезные возможности и инструменты.
• Повторное использование кода:
➡Функции;
➡Полезные функции;
➡Модули;
➡Полезные модули;
➡Итераторы, итерируемые объекты и генераторы.
• Регулярные выражения:
➡Синтаксис регулярных выражений;
➡Модуль re.
• Запись и передача данных:
➡Unicode;
➡Работа с файлами в формате CSV, JSON, YAML.
• Работа с сетевым оборудованием:
➡Подключение к оборудованию;
➡Одновременное подключение к нескольким устройствам;
➡Шаблоны конфигураций с Jinja2;
➡Обработка вывода команд TextFSM.
• Основы объектно-ориентированного программирования:
➡Основы ООП;
➡Специальные методы;
➡Наследование.
• Работа с базами данных:
➡Работа с базами данных.
• Дополнительная информация:
➡Модуль argparse;
➡Форматирование строк с оператором %;
➡Соглашение об именах;
➡Подчеркивание в именах;
➡Отличия Python 2.7 и Python 3.6;
➡Проверка заданий с помощью утилиты pyneng;
➡Проверка заданий с помощью pytest;
➡Написание скриптов для автоматизации рабочих процессов;
➡Python для автоматизации работы с сетевым оборудованием;
➡Python без привязки к сетевому оборудованию.
#Python