es
Feedback
Computer Science and Programming

Computer Science and Programming

Ir al canal en Telegram

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

Mostrar más

📈 Análisis del canal de Telegram Computer Science and Programming

El canal Computer Science and Programming (@computer_science_and_programming) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 142 827 suscriptores, ocupando la posición 814 en la categoría Tecnologías y Aplicaciones y el puesto 86 en la región Italia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 142 827 suscriptores.

Según los últimos datos del 11 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -1 293, y en las últimas 24 horas de -44, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.75%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.81% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 8 214 visualizaciones. En el primer día suele acumular 2 581 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 15.
  • Intereses temáticos: El contenido se centra en temas clave como sellerflash, github, developer, pricing, waybienad.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
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...

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 12 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

142 827
Suscriptores
-4424 horas
-1897 días
-1 29330 días
Archivo de publicaciones
Prompt Engineering as a Developer Discipline Structured prompting is becoming a crucial skill for developers, akin to traditi
Prompt Engineering as a Developer Discipline
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: Optimizing LLM Performance for the Right Job Task-based LLM routing directs incoming AI requests to t
Task-Based LLM Routing: Optimizing LLM Performance for the Right Job
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.

⚠ Message was hidden by channel owner
⚠ Message was hidden by channel owner

iro.js iro.js is a JavaScript color picker widget that provides an SVG-based interface for selecting colors. It supports mult
iro.js
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.

⚠ Message was hidden by channel owner
⚠ Message was hidden by channel owner

This Week in Open Source - Inaugural Post Google's Open Source Programs Office launches a weekly series highlighting open sou
This Week in Open Source - Inaugural Post
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.

Best Udemy Courses to Learn AI Discover the top 5 Udemy courses for learning AI Engineering in 2025. These courses cover esse
Best Udemy Courses to Learn AI
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.

⚠ Message was hidden by channel owner
⚠ Message was hidden by channel owner

Build an AI Assistant with LangGraph, Vercel, and Next.js: Use Gmail as a Tool Securely Learn how to build a personal AI assi
Build an AI Assistant with LangGraph, Vercel, and Next.js: Use Gmail as a Tool Securely
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 Design Principles Every JavaScript Deveveloper Should Know SOLID principles are five essential design rules that help J
SOLID Design Principles Every JavaScript Deveveloper Should Know
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.

An open source alternative to Heroku Canine is an open source deployment platform designed as a cost-effective alternative to
An open source alternative to Heroku
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.

Essential Machine Learning Concepts Animated Understanding AI and machine learning is essential for developers. This visually
Essential Machine Learning Concepts Animated Understanding AI and machine learning is essential for developers. This visually engaging course on freeCodeCamp.org's YouTube channel by Vladimirs from Turing Time Machine simplifies over 100 core ML and AI concepts with animations and real-world analogies. It covers foundational terms, statistical methods, optimization techniques, evaluation metrics, various model types, practical workflow elements, and related disciplines like NLP and object detection.

⚠ Message was hidden by channel owner
⚠ Message was hidden by channel owner

⚠ Message was hidden by channel owner

Frontend Isn't Just UI Frontend engineering goes beyond styling buttons and layouts; it involves building systems that serve
Frontend Isn't Just UI
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.

Full-Stack React.js Chat with AI SDK Learn how to build a full-stack React.js chat application using Vercel's AI SDK. This tu
Full-Stack React.js Chat with AI SDK
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.

Using LLMs to generate user-defined real-time data visualizations Developers are increasingly using Tinybird to track LLM usa
Using LLMs to generate user-defined real-time data visualizations
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.

Zyedidia/Literate: A literate programming tool for any language Literate is a modern literate programming tool that allows de
Zyedidia/Literate: A literate programming tool for any language
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.

⚠ Message was hidden by channel owner
⚠ Message was hidden by channel owner

Neodrag: One draggable to rule them all Neodrag is a multi-framework JavaScript library that provides drag-and-drop functiona
Neodrag: One draggable to rule them all
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.