Coding_knowledge

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💡 Your Coding Journey Starts Here! Get free courses, coding resources, internships, job updates & much more. Stay ahead in tech with us! ❤️🚀 Join our WhatsApp group👇 https://whatsapp.com/channel/0029Vaa7CVhCRs1rxJzy1n3D

India3 813 Tecnologías y Aplicaciones1 554...

📈 Análisis del canal de Telegram Coding_knowledge

El canal Coding_knowledge (@coding_knwledge01) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 79 500 suscriptores, ocupando la posición 1 554 en la categoría Tecnologías y Aplicaciones y el puesto 3 813 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 79 500 suscriptores.

Según los últimos datos del 29 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -930, y en las últimas 24 horas de -14, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 8.66%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.65% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 6 886 visualizaciones. En el primer día suele acumular 2 106 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 q&a, goody, api, stack, analyst.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“💡 Your Coding Journey Starts Here! Get free courses, coding resources, internships, job updates & much more. Stay ahead in tech with us! ❤️🚀 Join our WhatsApp group👇 https://whatsapp.com/channel/0029Vaa7CVhCRs1rxJzy1n3D”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 30 agosto, 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.

79 500

Suscriptores

-1424 horas

-1667 días

-93030 días

6 886

Visitas de la publicación

~ 2 10624 horas

~ 3 04348 horas

8.66%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

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✅ Generative AI Interview Prep Guide ✨🧠 Want to crack roles in GenAI (Prompt Engineer, LLM Developer, Applied Scientist)? Here's what to focus on: 1️⃣ What is Generative AI? • AI that *creates* text, images, audio, or code • Powered by deep learning models (e.g., GPT, DALL·E, Midjourney) • Examples: ChatGPT, Claude, Bard, GitHub Copilot 2️⃣ Core Concepts to Understand • Language Models: GPT, BERT, LLaMA • Transformers: self-attention, positional encoding • Tokenization: BPE, WordPiece • Embeddings: how meaning is captured in vector space • Fine-tuning vs Prompt Engineering vs Retrieval-Augmented Generation (RAG) 3️⃣ Prompt Engineering Basics • Zero-shot, few-shot prompting • Chain of Thought (CoT) prompting • System vs user prompts (in APIs) • Prompt templates for summarization, Q&A, code generation 4️⃣ Key Tools & Frameworks • OpenAI API / Anthropic / Cohere • LangChain, LlamaIndex (RAG tools) • Hugging Face Transformers • Vector DBs: Pinecone, FAISS, Chroma 5️⃣ Must-Know Projects • AI chatbot with memory (LangChain + OpenAI) • PDF Q&A bot using RAG • Blog/article summarizer • AI code reviewer • Voice-to-text-to-response assistant (using Whisper + GPT) 6️⃣ Interview Questions to Prepare • How do LLMs understand context? • What is token limit & why does it matter? • Difference between RAG and fine-tuning? • What are hallucinations in LLMs? • How would you evaluate a GenAI system? 7️⃣ Practice & Learn From • OpenAI Cookbook • LangChain docs • Hugging Face Spaces • Build and share on GitHub 8️⃣ Pro Tips ✔️ Know how to reduce hallucinations ✔️ Build mini tools to showcase skills ✔️ Stay updated on new models & benchmarks ✔️ Prepare case studies for product thinking 💬 Tap ❤️ for more!

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🌐 Top Python Libraries for Web Development 🐍💻 Python isn’t just for data science — it's also a powerful tool for building modern, scalable web apps! 🧱 Web Frameworks: • Django — High-level framework for fast, secure, full-stack web apps • Flask — Lightweight & flexible micro-framework, perfect for APIs • FastAPI — Modern, fast (high-performance) framework for building APIs with Python 3.7+ 📦 Backend Utilities: • SQLAlchemy — Powerful ORM (Object Relational Mapper) for databases • Jinja2 — Templating engine for rendering HTML from Python • Celery — Task queue for running background jobs (e.g., email, scheduling) 🔐 Security & Auth: • Authlib — Easy OAuth and JWT integration • Passlib — Password hashing and authentication tools 💻 Frontend Integration: • WTForms — Form handling & validation • Flask-WTF — Seamless form support for Flask apps 💡 Learn these to start building real-world web applications with Python! 💬 Tap ❤️ for more!

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✅ Python Coding Interview Questions – Part 11 🐍💻 1️⃣ Q: Return the first duplicate in a list.

def first_duplicate(lst):
    seen = set()
    for x in lst:
        if x in seen:
            return x
        seen.add(x)
    return None

print(first_duplicate([3, 1, 3, 4, 2]))  # Output: 3

2️⃣ Q: Check whether a number is a palindrome.

def is_pal_num(n):
    return str(n) == str(n)[::-1]

print(is_pal_num(121))  # True  
print(is_pal_num(123))  # False

3️⃣ Q: Sort a dictionary by values.

def sort_by_value(d):
    return dict(sorted(d.items(), key=lambda x: x[1]))

print(sort_by_value({'a': 3, 'b': 1, 'c': 2}))  
Output: {'b': 1, 'c': 2, 'a': 3}

4️⃣ Q: Return all prime numbers in a given range.

def primes_upto(n):
    primes = []
    for num in range(2, n + 1):
        for i in range(2, int(num**0.5) + 1):
            if num % i == 0:
                break
        else:
            primes.append(num)
    return primes

print(primes_upto(10))  # [2, 3, 5, 7]

5️⃣ Q: Convert a list of numbers into a string.

def list_to_string(lst):
    return "".join(map(str, lst))

print(list_to_string([1, 2, 3]))  # Output: 123

💬 Double Tap ❤️ for Part-12

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✅ Complete Roadmap to learn Python Programming 🐍💻 Week 1. Python basics • Install Python and VS Code • Learn variables, data types, input, output • Practice arithmetic and string operations • Write 10 small programs Example. Calculator, temperature converter Week 2. Control flow • Learn if, else, elif • Learn for and while loops • Use break and continue • Solve 20 logic problems Example. Number guessing game Week 3. Data structures • Lists, tuples, sets, dictionaries • Indexing, slicing, methods • Loop through collections • Solve real problems Example. Student marks analysis Week 4. Functions and modules • Define functions • Use parameters and return values • Learn lambda functions • Import built-in modules Example. Reusable math utility Week 5. Strings and file handling • String methods and formatting • Read and write files • Handle CSV and text files • Build small file-based programs Example. Log file analyzer Week 6. Error handling and debugging • Learn try, except, finally • Understand common errors • Use print and debugger • Fix broken programs Example. Robust input validator Week 7. Object-Oriented Programming • Classes and objects • Constructors and methods • Inheritance and encapsulation • Build simple class-based apps Example. Bank account system Week 8. Standard libraries • datetime, math, random • os and sys basics • Work with JSON • Write utility scripts Example. Automated folder organizer Week 9. Working with external packages • Learn pip and virtual environments • Use requests library • Basic API calls • Handle API responses Example. Weather app using API Week 10. Data handling basics • Intro to NumPy • Intro to Pandas • Read CSV and Excel files • Basic data cleaning Example. Sales data summary Week 11. Mini projects • Build 2 small projects • Focus on logic and structure • Write clean, readable code Examples. • To-do list app • Expense tracker Week 12. Final project and revision • Build one end-to-end project • Revise core concepts • Practice interview-style questions Example projects. • Simple automation tool • Data analysis mini project Daily rule for you • Code at least 60 minutes • Solve 5 problems daily • Rewrite old code weekly Double Tap ♥️ For Detailed Explanation