🔥 Trending Repository: mcp-context-forge
📝 Description: A Model Context Protocol (MCP) Gateway & Registry. Serves as a central management point for tools, resources, and prompts that can be accessed by MCP-compatible LLM applications. Converts REST API endpoints to MCP, composes virtual MCP servers with added security and observability, and converts between protocols (stdio, SSE, Streamable HTTP).
🔗 Repository URL: https://github.com/IBM/mcp-context-forge
🌐 Website: https://ibm.github.io/mcp-context-forge/
📖 Readme: https://github.com/IBM/mcp-context-forge#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 22
🍴 Forks: 184 forks
💻 Programming Languages: Python - JavaScript - Makefile - HTML - Go - Shell
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: A Model Context Protocol (MCP) Gateway & Registry. Serves as a central management point for tools, resources, and prompts that can be accessed by MCP-compatible LLM applications. Converts REST API endpoints to MCP, composes virtual MCP servers with added security and observability, and converts between protocols (stdio, SSE, Streamable HTTP).
🔗 Repository URL: https://github.com/IBM/mcp-context-forge
🌐 Website: https://ibm.github.io/mcp-context-forge/
📖 Readme: https://github.com/IBM/mcp-context-forge#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 22
🍴 Forks: 184 forks
💻 Programming Languages: Python - JavaScript - Makefile - HTML - Go - Shell
🏷️ Related Topics:
#python #docker #kubernetes #devops #jwt #tools #ai #api_gateway #mcp #gateway #asyncio #federation #agents #observability #authentication_middleware #fastapi #prompt_engineering #generative_ai #llm_agents #model_context_protocol
==================================
🧠 By: https://t.iss.one/DataScienceM
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🔥 Trending Repository: bytebot
📝 Description: Bytebot is a self-hosted AI desktop agent that automates computer tasks through natural language commands, operating within a containerized Linux desktop environment.
🔗 Repository URL: https://github.com/bytebot-ai/bytebot
🌐 Website: https://www.bytebot.ai/
📖 Readme: https://github.com/bytebot-ai/bytebot#readme
📊 Statistics:
🌟 Stars: 1.4K stars
👀 Watchers: 9
🍴 Forks: 119 forks
💻 Programming Languages: TypeScript - Dockerfile - CSS - Smarty - Scheme - JavaScript - PLpgSQL
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Bytebot is a self-hosted AI desktop agent that automates computer tasks through natural language commands, operating within a containerized Linux desktop environment.
🔗 Repository URL: https://github.com/bytebot-ai/bytebot
🌐 Website: https://www.bytebot.ai/
📖 Readme: https://github.com/bytebot-ai/bytebot#readme
📊 Statistics:
🌟 Stars: 1.4K stars
👀 Watchers: 9
🍴 Forks: 119 forks
💻 Programming Languages: TypeScript - Dockerfile - CSS - Smarty - Scheme - JavaScript - PLpgSQL
🏷️ Related Topics:
#agent #docker #automation #ai #mcp #desktop #gemini #openai #desktop_automation #agents #cua #ai_agents #ai_tools #llm #anthropic #agentic_ai #computer_use #computer_use_agent #bytebot
==================================
🧠 By: https://t.iss.one/DataScienceM
✨ Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch ✨
📖 Table of Contents Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch Introduction What Will We Do in This Blog? Why Are We Using Vector Embeddings? What’s Coming Next? Configuring Your Development Environment Installing Docker (Required for…...
🏷️ #Docker #MachineLearning #OpenSearch #SearchEngines #SemanticSearch #Tutorial #VectorSearch
📖 Table of Contents Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch Introduction What Will We Do in This Blog? Why Are We Using Vector Embeddings? What’s Coming Next? Configuring Your Development Environment Installing Docker (Required for…...
🏷️ #Docker #MachineLearning #OpenSearch #SearchEngines #SemanticSearch #Tutorial #VectorSearch
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🔥 Trending Repository: winapps
📝 Description: Run Windows apps such as Microsoft Office/Adobe in Linux (Ubuntu/Fedora) and GNOME/KDE as if they were a part of the native OS, including Nautilus integration. Hard fork ofhttps://github.com/Fmstrat/winapps/
🔗 Repository URL: https://github.com/winapps-org/winapps
📖 Readme: https://github.com/winapps-org/winapps#readme
📊 Statistics:
🌟 Stars: 4.6K stars
👀 Watchers: 31
🍴 Forks: 155 forks
💻 Programming Languages: Shell - PowerShell - Nix - Batchfile
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Run Windows apps such as Microsoft Office/Adobe in Linux (Ubuntu/Fedora) and GNOME/KDE as if they were a part of the native OS, including Nautilus integration. Hard fork ofhttps://github.com/Fmstrat/winapps/
🔗 Repository URL: https://github.com/winapps-org/winapps
📖 Readme: https://github.com/winapps-org/winapps#readme
📊 Statistics:
🌟 Stars: 4.6K stars
👀 Watchers: 31
🍴 Forks: 155 forks
💻 Programming Languages: Shell - PowerShell - Nix - Batchfile
🏷️ Related Topics:
#windows #linux #docker #integration #kde #gnome #nautilus #qemu #xfce #wine #libvirt #freerdp #linux_app #cassowary #hacktoberfest #qemu_kvm #seamless #podman #nix_flake #winapps
==================================
🧠 By: https://t.iss.one/DataScienceM
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🔥 Trending Repository: mcp-context-forge
📝 Description: A Model Context Protocol (MCP) Gateway & Registry. Serves as a central management point for tools, resources, and prompts that can be accessed by MCP-compatible LLM applications. Converts REST API endpoints to MCP, composes virtual MCP servers with added security and observability, and converts between protocols (stdio, SSE, Streamable HTTP).
🔗 Repository URL: https://github.com/IBM/mcp-context-forge
🌐 Website: https://ibm.github.io/mcp-context-forge/
📖 Readme: https://github.com/IBM/mcp-context-forge#readme
📊 Statistics:
🌟 Stars: 1.7K stars
👀 Watchers: 21
🍴 Forks: 215 forks
💻 Programming Languages: Python - HTML - JavaScript - Makefile - Go - Shell
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: A Model Context Protocol (MCP) Gateway & Registry. Serves as a central management point for tools, resources, and prompts that can be accessed by MCP-compatible LLM applications. Converts REST API endpoints to MCP, composes virtual MCP servers with added security and observability, and converts between protocols (stdio, SSE, Streamable HTTP).
🔗 Repository URL: https://github.com/IBM/mcp-context-forge
🌐 Website: https://ibm.github.io/mcp-context-forge/
📖 Readme: https://github.com/IBM/mcp-context-forge#readme
📊 Statistics:
🌟 Stars: 1.7K stars
👀 Watchers: 21
🍴 Forks: 215 forks
💻 Programming Languages: Python - HTML - JavaScript - Makefile - Go - Shell
🏷️ Related Topics:
#python #docker #kubernetes #devops #jwt #tools #ai #api_gateway #mcp #gateway #asyncio #federation #agents #observability #authentication_middleware #fastapi #prompt_engineering #generative_ai #llm_agents #model_context_protocol
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: windows
📝 Description: Windows inside a Docker container.
🔗 Repository URL: https://github.com/dockur/windows
📖 Readme: https://github.com/dockur/windows#readme
📊 Statistics:
🌟 Stars: 37.7K stars
👀 Watchers: 198
🍴 Forks: 2.8K forks
💻 Programming Languages: Shell - Dockerfile
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Windows inside a Docker container.
🔗 Repository URL: https://github.com/dockur/windows
📖 Readme: https://github.com/dockur/windows#readme
📊 Statistics:
🌟 Stars: 37.7K stars
👀 Watchers: 198
🍴 Forks: 2.8K forks
💻 Programming Languages: Shell - Dockerfile
🏷️ Related Topics:
#windows #docker #docker_container #virtualization #windows_vm #windows_virtual_machines #windows_virtual_desktop #windows_virtual_machine
==================================
🧠 By: https://t.iss.one/DataScienceM
# Real-World Case Study: E-commerce Product Pipeline
import boto3
from PIL import Image
import io
def process_product_image(s3_bucket, s3_key):
# 1. Download from S3
s3 = boto3.client('s3')
response = s3.get_object(Bucket=s3_bucket, Key=s3_key)
img = Image.open(io.BytesIO(response['Body'].read()))
# 2. Standardize dimensions
img = img.convert("RGB")
img = img.resize((1200, 1200), Image.LANCZOS)
# 3. Remove background (simplified)
# In practice: use rembg or AWS Rekognition
img = remove_background(img)
# 4. Generate variants
variants = {
"web": img.resize((800, 800)),
"mobile": img.resize((400, 400)),
"thumbnail": img.resize((100, 100))
}
# 5. Upload to CDN
for name, variant in variants.items():
buffer = io.BytesIO()
variant.save(buffer, "JPEG", quality=95)
s3.upload_fileobj(
buffer,
"cdn-bucket",
f"products/{s3_key.split('/')[-1].split('.')[0]}_{name}.jpg",
ExtraArgs={'ContentType': 'image/jpeg', 'CacheControl': 'max-age=31536000'}
)
# 6. Generate WebP version
webp_buffer = io.BytesIO()
img.save(webp_buffer, "WEBP", quality=85)
s3.upload_fileobj(webp_buffer, "cdn-bucket", f"products/{s3_key.split('/')[-1].split('.')[0]}.webp")
process_product_image("user-uploads", "products/summer_dress.jpg")
By: @DataScienceM 👁
#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3
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In Python, building AI-powered Telegram bots unlocks massive potential for image generation, processing, and automation—master this to create viral tools and ace full-stack interviews! 🤖
Learn more: https://hackmd.io/@husseinsheikho/building-AI-powered-Telegram-bots
https://t.iss.one/DataScienceM🦾
# Basic Bot Setup - The foundation (PTB v20+ Async)
from telegram.ext import Application, CommandHandler, MessageHandler, filters
async def start(update, context):
await update.message.reply_text(
"✨ AI Image Bot Active!\n"
"/generate - Create images from text\n"
"/enhance - Improve photo quality\n"
"/help - Full command list"
)
app = Application.builder().token("YOUR_BOT_TOKEN").build()
app.add_handler(CommandHandler("start", start))
app.run_polling()
# Image Generation - DALL-E Integration (OpenAI)
import openai
from telegram.ext import ContextTypes
openai.api_key = os.getenv("OPENAI_API_KEY")
async def generate(update: Update, context: ContextTypes.DEFAULT_TYPE):
if not context.args:
await update.message.reply_text("❌ Usage: /generate cute robot astronaut")
return
prompt = " ".join(context.args)
try:
response = openai.Image.create(
prompt=prompt,
n=1,
size="1024x1024"
)
await update.message.reply_photo(
photo=response['data'][0]['url'],
caption=f"🎨 Generated: *{prompt}*",
parse_mode="Markdown"
)
except Exception as e:
await update.message.reply_text(f"🔥 Error: {str(e)}")
app.add_handler(CommandHandler("generate", generate))
Learn more: https://hackmd.io/@husseinsheikho/building-AI-powered-Telegram-bots
#Python #TelegramBot #AI #ImageGeneration #StableDiffusion #OpenAI #MachineLearning #CodingInterview #FullStack #Chatbots #DeepLearning #ComputerVision #Programming #TechJobs #DeveloperTips #CareerGrowth #CloudComputing #Docker #APIs #Python3 #Productivity #TechTips
https://t.iss.one/DataScienceM
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