Implementing Dijkstra's Algorithm with Heap in Python

🚀 Binary Search Implementation (Data Structures And Algorithms) This Python code demonstrates the binary search algorithm. It leverages the sorted nature of the input list to efficiently locate the target element. The algorithm repeatedly divides the search interval in half, comparing the middle element with the target. The code returns the index of the target if found and -1 if the target is not present in the list. Binary search is a preferred method for searching large, sorted datasets due to its logarithmic time complexity, which significantly reduces the number of comparisons needed compared to linear search. #Algorithms #DataStructures #CodingInterview #ProblemSolving #professional #career #development

Skill Seekers: It is a Python tool that scrapes documentation sites, GitHub repos and PDFs to automatically generate skills for the Claude AI platform. https://lnkd.in/eJtSae4Z

Gen AI Insight of the Day Coding Challenge: Build a Python class that wraps multiple LLMs (e.g., GPT, Claude, Gemini) and routes queries based on cost and latency. Hint: Use asyncio for parallel API calls and caching layers for frequent queries. Discussion: What are the trade-offs of using multiple model providers in a single application? Follow: Sarveshwaran Rajagopal for more such posts

Gen AI Insight of the Day Coding Challenge: Build a Python class that wraps multiple LLMs (e.g., GPT, Claude, Gemini) and routes queries based on cost and latency. Hint: Use asyncio for parallel API calls and caching layers for frequent queries. Discussion: What are the trade-offs of using multiple model providers in a single application? Follow: Sarveshwaran Rajagopal for more such posts

System Design with Python – Part 11 If your system crashes and you don’t know why — that’s not bad luck, that’s missing observability. Logs tell you what happened. Metrics show how often. Traces reveal where it broke. In this post: 🔹 Logging with FastAPI (structured + contextual) 🔹 Metrics with Prometheus & Grafana 🔹 Tracing with OpenTelemetry & Jaeger 🔹 How the 3 pillars work together for real observability 💡 Observability isn’t about data — it’s about insight before impact. 🧩 Secret note: Even ChatGPT uses a layered trace + metrics system to monitor latency across its model calls in real time. 💬 Ever fixed a bug just by reading logs at 3 AM? 😅 #SystemDesign #Python #FastAPI #Prometheus #Grafana #OpenTelemetry #Observability #BackendDevelopment #DevOps

🚀 Configuring Dead Letter Exchange in RabbitMQ with Python (System Design) This Python example shows how to configure a dead letter exchange (DLX) and queue in RabbitMQ. When a message is rejected or expires, it's routed to the DLX. The DLX then routes the message to the dead letter queue (DLQ). This setup allows you to capture and analyze failed messages, improving the reliability of your messaging system. The consumer code demonstrates how to set up the exchange and queue bindings. 💡 Learn smarter, achieve faster! ✨ Tech mastery made simple — 10,000+ concepts, 4,000+ articles, 12,000+ quizzes. Powered by AI! 👇 Links available in the comments! #SystemDesign #Architecture #Scalability #DistributedSystems #professional #career #development

🚀 Python's Core Operators: Arithmetic, Comparison, and Logical Python provides a rich set of operators for performing various operations. Arithmetic operators (+, -, *, /, %, **) are used for mathematical calculations. Comparison operators (==, !=, >, =, <=) are used for comparing values and returning boolean results. Logical operators (and, or, not) are used for combining boolean expressions. Understanding these operators is fundamental for writing conditional statements and performing data manipulation. #Python #PythonDev #DataScience #WebDev #professional #career #development

https://lnkd.in/gx7MyUvu Tom explained how to build a Chatbot that responds based on given document (rather than LLM's inbuilt knowledge) He outline the training part (with given document) consists of:- -Load -Split -Embed -Store Inference or generative includes -Question (by user of chatbot) -Retrieval -LLM to infer -Output To tell chatbot not to use its own LLM embedded knowledge simply includes the following in python :- "rag_prompt = f""" You are an assistent which answers questions based on knowledge which is provided to you. While answering, you don't use your internal knowledge, but solely the information in the "The knowledge" section. You don't mention anything to the user about the povided knowledge." There is even an video #Chatbot; #LLM; #OpenAI; #LangChain; #ChromaDB; #RAG https://lnkd.in/gJkYs9ba

Agentic AI with LangChain (Runnable Graph Example in Python) In this video, we walk through how to build a simple yet powerful Agentic AI system using LangChain’s modern Runnable Graph API. This example demonstrates how an AI agent can reason, decide, and use tools like a calculator — step-by-step — using clean, modular Python code. 💡 What You’ll Learn: What Agentic AI means and why it matters How to create a tool (like a calculator) the AI can call How the AI dynamically decides whether to reason or use a tool How to extend this into a multi-tool, production-ready agent ⚙️ Tech Stack: Python 🐍 LangChain (latest version) OpenAI API (GPT-4o-mini model) 📘 Source Code & References: Check the description links below for the full code, environment setup guide, and documentation. Source code: https://lnkd.in/gtyzrBVX YouTube link :: https://lnkd.in/gJFTRKig 🚀 Whether you're learning Agentic AI concepts or preparing to build your own AI-powered automation, this tutorial will help you understand the core building blocks of an intelligent agent. #AgenticAI #LangChain #OpenAI #Python #AIProgramming #RunnableGraph #AIAgent #GPT4 #VSCode #AIDevelopment

🚀 Set in Python - A Set in Python is a collection data type that is unordered, unindexed, and contains unique elements. It is mainly used when you want to store non-duplicate items and perform mathematical set operations like union, intersection, and difference. 🧩 Key Features: ▪️ Unordered: Elements have no defined order. ▪️ Mutable: You can add or remove items after creation. ▪️ No duplicates: Automatically removes repeated elements. ▪️ Supports set operations like union(), intersection(), difference(), etc. 💡 When to Use: 🔸 You need unique values. 🔸 You want to perform fast membership testing. 🔸 You need set-based operations (like finding common elements). #Python #PythonLearning #PythonBasics #DataStructures #Coding #LearnPython #SetInPython