Computer Science and Programming
Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Computer Science and Programming
تُعد قناة Computer Science and Programming (@computer_science_and_programming) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 142 827 مشتركاً، محتلاً المرتبة 814 في فئة التكنولوجيات والتطبيقات والمرتبة 86 في منطقة إيطاليا.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 142 827 مشتركاً.
بحسب آخر البيانات بتاريخ 11 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -1 293، وفي آخر 24 ساعة بمقدار -44، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 5.75%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.81% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 8 214 مشاهدة. وخلال اليوم الأول يجمع عادةً 2 581 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 15.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل sellerflash, github, developer, pricing, waybienad.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Channel specialized for advanced topics of:
* Artificial intelligence,
* Machine Learning,
* Deep Learning,
* Computer Vision,
* Data Science
* Python
Admin: @otchebuch
Memes: @memes_programming
Ads: @Source_Ads,
https://telega.io/c/computer_sc...”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 12 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
Structured prompting is becoming a crucial skill for developers, akin to traditional coding practices. Using AI effectively involves treating prompts as modular, testable components within software systems. Techniques like few-shot prompting, chain-of-thought reasoning, self-consistency, skeleton prompting, and configuration parameters improve AI's coding outputs. Developers should rigorously validate and maintain prompts, just like any other code, to ensure reliability and consistency in AI-powered features.
Task-based LLM routing directs incoming AI requests to the most suitable large language model based on the task. This approach improves performance, reduces costs, and enhances scalability by matching tasks with models optimized for those specific needs. For instance, simpler tasks can be routed to lightweight models like GPT-3.5 to minimize costs, while complex tasks are handled by more powerful models like GPT-4. This method also enhances reliability and latency, and is useful in diverse applications like customer support, content creation, code-related tasks, and multilingual processing.
iro.js is a JavaScript color picker widget that provides an SVG-based interface for selecting colors. It supports multiple color formats (hex, RGB, HSV, HSL, and kelvin temperatures) through a unified API, allows multiple colors for harmony selection, and requires no external dependencies. The library can be installed via NPM, CDN, or direct download, and offers customizable options, event handling, and easy integration with modern frameworks.
Google's Open Source Programs Office launches a weekly series highlighting open source news, events, and articles. This inaugural post covers upcoming conferences including Open Source Summit North America and SciPy, plus featured reads about a new TPDE compiler that outperforms LLVM, developer tool recommendations, GUAC 1.0 for software bill of materials management, and cloud-native AI workflows using Google's open source tools.
Discover the top 5 Udemy courses for learning AI Engineering in 2025. These courses cover essential skills like LLMs, MLOps, AI agents, and cloud-based AI services, making them perfect for aspiring AI Engineers. Learn from industry professionals at an affordable price and become job-ready without needing a PhD or expensive bootcamp.
Learn how to build a personal AI assistant using LangGraph, Vercel AI SDK, and Next.js. This guide walks through integrating various tools such as Gmail, Google Calendar, and Google Drive securely by leveraging Auth0 for authentication and token management. The tutorial covers both unauthenticated tools like calculators and authenticated tools for accessing personal data, exemplified by implementing a Gmail search and draft feature.
SOLID principles are five essential design rules that help JavaScript developers write cleaner, maintainable code. The guide covers Single Responsibility (one function per purpose), Open/Closed (extend without modifying), Liskov Substitution (subclasses must be replaceable), Interface Segregation (avoid bloated interfaces), and Dependency Inversion (depend on abstractions). Each principle includes practical JavaScript examples showing violations and proper implementations, plus real-world applications and common interview questions.
Canine is an open source deployment platform designed as a cost-effective alternative to Heroku. It offers GitHub integration, one-click deployments, automatic SSL certificate management, and Kubernetes simplification. The platform supports over 200 cloud providers to avoid vendor lock-in, includes autoscaling capabilities, and can deploy over 10,000 open source projects at no additional cost. Users can either self-host Canine or use the hosted version, with the core platform being free and open source.
Frontend engineering goes beyond styling buttons and layouts; it involves building systems that serve human experiences. Key aspects include data flow, state models, component architecture, user experience flow, and accessibility. It combines design with logic to create scalable and user-friendly products.
Learn how to build a full-stack React.js chat application using Vercel's AI SDK. This tutorial simplifies the process of creating chat UIs by abstracting complex tasks such as decoding text streams and managing state. It provides step-by-step instructions on installing and configuring the AI SDK, replacing manual API routes with streamlined one-liners, and using the useChat hook for front-end development. Enhance your coding efficiency and maintainability while keeping all essential functionalities intact.
Developers are increasingly using Tinybird to track LLM usage, costs, and performance in AI applications. A new app template called the LLM Performance Tracker allows users to generate real-time data visualizations. The core components include a Tinybird datasource, a Tinybird pipe, a React component, and an AI API route. The backend processes user input to generate chart parameters, while the frontend visualizes the data. This approach emphasizes the importance of performant analytics backends and cautious LLM usage for secure and scalable data visualization.
Literate is a modern literate programming tool that allows developers to write programs as narrative documents with embedded code blocks. It supports any programming language, uses Markdown syntax for easy readability, generates both executable code and HTML documentation, and includes features like syntax highlighting, error reporting, and automatic hyperlink generation between code sections.
Neodrag is a multi-framework JavaScript library that provides drag-and-drop functionality across React, Svelte, Vue, SolidJS, and vanilla JavaScript. It features a small bundle size (3.46KB), server-side rendering compatibility, TypeScript support, and consistent behavior across all supported frameworks through shared core logic.
متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
