3️⃣ Dasturlash tillari jangi: PHP vs Python vs JavaScript
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🔠 → Vebsaytlar yaratish uchun ideal
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📱 Python → AI & Ma’lumotlar tahlili uchun zo‘r
📱 JavaScript → Frontendda hukmron 👑
📌 PHP ning afzalliklari:
✅ Ishonchli (23+ yillik tarix)
✅ Oson o‘rganiladi (C, Java, JS ga o‘xshash sintaksis)
✅ Hostingga o‘rnatish oson (Ko‘p serverlar tayyor)
🤔 Siz qaysi tillarni yaxshi ko‘rasiz? Izohlarda yozing! 📝
#PHP#Python#JavaScript#fast_coder
📱@Fast_Coder
#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
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Tip: You can use functools.reduce() in #Python to apply a function to all elements of the iterable set.
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#Python
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Test your typing knowledge in Python
Cool free project Python Type Challenger is a quiz where you have to write code. A good way to remember the basics of type hints (dictionaries, variables, return) and drill into the depths of the typing ocean (recursive, decorators, constructors, etc.).
🔗Link
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