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Discover powerful insights with Python, Machine Learning, Coding, and R—your essential toolkit for data-driven solutions, smart alg

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AI Engineering roadmap that beginners can actually follow. Everything is based on 100% free, open-source, and community resources

All resources can be found here: GitHub

👉  @codeprogrammer
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🖥 Want your Telegram bot to respond using ChatGPT?

Do it in a couple of minutes: just install the python-telegram-bot library, add your #OpenAI #API key and bot token, and the bot will start replying to all messages using #ChatGPT.

from telegram import Update
from telegram.ext import ApplicationBuilder, MessageHandler, filters, ContextTypes
from openai import OpenAI


Specify your keys
OPENAI_API_KEY = "sk-..." 
TELEGRAM_TOKEN = "123456789:ABC..."

client = OpenAI(api_key=OPENAI_API_KEY)

async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE):
    user_text = update.message.text

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": user_text}]
    )

    await update.message.reply_text(response.choices[0].message.content)

app = ApplicationBuilder().token(TELEGRAM_TOKEN).build()
app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message))
app.run_polling()


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🤖🧠 Sora: OpenAI’s Breakthrough Text-to-Video Model Transforming Visual Creativity

🗓️ 18 Oct 2025
📚 AI News & Trends

Introduction Artificial Intelligence (AI) is rapidly transforming the creative world. From generating realistic images to composing music and writing code, AI has redefined how humans interact with technology. But one of the most revolutionary advancements in this domain is Sora, OpenAI’s text-to-video generative model that converts written prompts into hyper-realistic video clips. Ithas captured global ...

#Sora #OpenAI #TextToVideo #AI #VisualCreativity #GenerativeModel
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🎓 Stanford has released a new course: “Transformers & Large Language Models”

The authors are the Amidi brothers, and three free lectures are already available on YouTube. This is probably one of the most systematic introductory courses on modern LLMs.

Course content:

• Transformers: tokenization, embeddings, attention, architecture
#LLM basics: Mixture of Experts, decoding types
• Training and fine-tuning: SFT, RL, LoRA
• Model evaluation: LLM/VLM-as-a-judge, best practices
• Tricks: RoPE, attention approximations, quantization
• Reasoning: scaling during training and inference
• Agentic approaches: #RAG, tool calling

If you are already familiar with this topic — it’s a great opportunity to refresh your knowledge and try implementing some techniques from scratch.

https://cme295.stanford.edu/syllabus/

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