Sameer Ur Rehman’s Post

You don't need a massive team to build smart applications. Sometimes, a clear problem and a few lines of Python are all it takes to leverage Large Language Models effectively. The real challenge isn't just generating text—it's getting structured, reliable data out of an LLM. Anyone who has tried to parse a model's free-form response into a software-friendly format knows the pain. We needed to consistently extract specific entities—like product names and mentioned issues—from customer feedback. The raw LLM output was too unpredictable for our systems. Here’s the Python snippet that solved it. We used the OpenAI API with structured outputs to define exactly what we wanted back. This simple approach gives us a guaranteed, validated Python object every time. No more regular expressions hunting for patterns in paragraphs of text. The model does the understanding, and our code gets a clean, predictable data structure. The result? We integrated a powerful LLM feature in hours, not days, with code that is simple, robust, and easy to maintain. #Python #LLM #AIEngineering #SoftwareDevelopment #OpenAI #APIDevelopment

Python Just Got Superpowers! Ladies and gentlemen… the day has finally come. Python 3.14 has officially removed the Global Interpreter Lock (GIL)! For over 30 years, the GIL has quietly held Python back — limiting performance and forcing us to use workarounds like multiprocessing, C++ extensions, or full frameworks just to use all our CPU cores. But guess what? That era is over. What’s new in Python 3.14? • ✅ True parallel threads in pure Python • ✅ Async + threads working hand-in-hand • ✅ Real multi-core performance for data, ML, and simulation workloads For AI & ML engineers, this means: • Faster data preprocessing and orchestration • Easier multi-threaded inference & agent coordination • No more dropping to C++ for CPU-bound tasks This isn’t just another update — It’s a historic milestone that transforms how Python scales and performs. Goodbye GIL 👋 — thanks for the memories (and the headaches). You won’t be missed. Note: Python 3.14 will ship with two builds — one standard and one “free-threaded” (GIL-free) version, so it’s not enabled by default yet. Have any “GIL horror stories” from your projects? Share them below — let’s laugh (and cry) together 😅 #Python #AI #MachineLearning #Developers #TechNews #Coding #ParallelProcessing #Innovation #OpenSource

Excited to share my latest project! I built a webpage summarization tool using Python and LLMs that can automatically condense website content into clear, professional summaries. Here’s what it does: Fetches content from any URL using a custom scraper. Sends the content to a local LLM (llama3.2 via Ollama) for summarization. Outputs a clean, readable summary in Markdown for easy reading. The workflow leverages: Python for scripting and automation Ollama and LLaMA 3.2 for local AI-powered summaries This tool is perfect for quickly digesting large amounts of web content, whether for research, competitive analysis, or personal learning. Check out the code here: https://lnkd.in/etKVFH_A #AI #MachineLearning #Python #LLM #WebScraping #ProductivityTools #Ollama #LLaMA3

How to Build a RAG Solution with Llama Index, ChromaDB, and Ollama Have you ever wanted to read through a ton of documents super fast or ask questions based on a particular knowledge or domain? That’s where RAG shines. RAG stands for retrieval-augmented generation, and it lets you combine a knowledge base, such as a PDF or a web page, with a large language model (LLM), such as Gemini or GPT, to get accurate, fast answers. So instead of relying solely on ChatGPT, which does not know your documents and would likely hallucinate (give incorrect answers), you could build and use a RAG solution. In this tutorial guide, we’ll build a very simple document search tool with Python, LlamaIndex, ChromaDB, and Ollama. To follow alongside this guide, you need to have the following installed on your laptop or PC. Have Python 3.10+, and Ollama installed. Downloading models on Ollama, Setting up a Llama index and Chroma database, Loading our documents to the Llama index, and Building a query engine. In case you are unfamiliar with Ollama. Ollama is an open source https://lnkd.in/gwRcb2X5

💡 LeetCode Learning Log — Two Sum II (Input Array is Sorted) 🔗 Problem: https://lnkd.in/gkcqGnfD Today I explored three different approaches to solve this problem: 1️⃣ 𝗕𝗿𝘂𝘁𝗲 𝗙𝗼𝗿𝗰𝗲 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 • Checked all possible pairs using nested loops. • 𝗧𝗶𝗺𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆: O(n²) 𝗦𝗽𝗮𝗰𝗲: O(1) • Simple but inefficient for large inputs. 2️⃣ 𝗕𝗲𝘁𝘁𝗲𝗿 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 (𝗨𝘀𝗶𝗻𝗴 𝗛𝗮𝘀𝗵𝗠𝗮𝗽) • Stored elements in a map while traversing. • For every number, checked if (𝘵𝘢𝘳𝘨𝘦𝘵 - 𝘯𝘶𝘮) exists. • 𝗧𝗶𝗺𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆: O(n) 𝗦𝗽𝗮𝗰𝗲: O(n) • Much faster, but takes extra space. • Great when index matters — because it keeps track of original positions. 3️⃣ 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 (𝗧𝘄𝗼 𝗣𝗼𝗶𝗻𝘁𝗲𝗿𝘀) • Since the array is sorted, used two pointers from both ends. • Moved pointers based on the sum comparison. • 𝗧𝗶𝗺𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆: O(n) 𝗦𝗽𝗮𝗰𝗲: O(1) • Clean, elegant, and efficient. 𝗥𝗲𝘀𝘂𝗹𝘁: ✅ All 24 test cases passed ⏱ Runtime: 4 ms 💾 Memory: 12.77 MB 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀: • Understand why 𝘁𝘄𝗼 𝗽𝗼𝗶𝗻𝘁𝗲𝗿𝘀 shine when arrays are sorted. • When 𝗶𝗻𝗱𝗲𝘅 𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁, sorting may not work. • Don’t just solve, explore multiple approaches to strengthen problem-solving intuition. #LeetCode #DSA #ProblemSolving #Python #TwoPointers #CodingJourney #DataEngineer #100DaysOfCode

🔐 LeetCode Problem #2125: Number of Laser Beams in a Bank Today’s Python challenge focuses on optimizing logic and pattern recognition in a matrix-based problem involving laser beams between security devices in a bank. 📄 Problem Summary: You are given a list of binary strings representing the floor plan of a bank. '1' → A security device '0' → An empty cell There is one laser beam between any two devices if: They are on different rows (r1 < r2), and Every row between them has no devices ('0's only). The task is to calculate the total number of laser beams in the bank. 🧠 Key Python Concepts Used: String Counting: Using row.count('1') to efficiently count devices in each row. Filtering Non-Empty Rows: Skipping rows without devices to reduce unnecessary computation. Mathematical Pattern Recognition: Realizing that beams only form between consecutive non-empty rows, allowing a simple multiplication-based approach. 📊 Example: bank = ["011001", "000000", "010100", "001000"] # Output: 8 Explanation: Devices per row: [3, 0, 2, 1] → Filtered: [3, 2, 1] Beams = (3×2) + (2×1) = 8 ⏱️ Complexity: Time: O(m × n) — count devices per row once. Space: O(m) — store counts for non-empty rows. 💡 Learning: This problem highlights the importance of: Recognizing patterns that simplify complex iteration. Transforming multi-layered conditions into clean, sequential logic. Writing readable and efficient Python code using list comprehensions and clean iteration. A great exercise in algorithmic optimization and elegant problem solving! 🧩 #Python #LeetCode #ProblemSolving #DataScience

Understanding Variables & Data Types in Python When I first started coding, I thought: "Why do we even need variables?" 🤔 Then I realized — variables are like containers. They hold the data that makes our programs do something meaningful. Imagine your brain remembering a name, an age, or a score — that’s exactly what Python does using variables 🧠 🧩 Step 1: What is a Variable? A variable is simply a name you give to a piece of data. Let’s see it in action 👇 name = "Keshav" age = 25 is_coder = True Here’s what’s happening: name stores a string (text) age stores a number is_coder stores a boolean (True/False value) Each piece of data you store has a data type — and that’s how Python knows how to treat it. 🧠 Step 2: Why It Matters Once you understand variables, you can: ✅ Store user data ✅ Perform calculations ✅ Build logic into programs This simple concept becomes the foundation of every project you’ll ever build — from chatbots to AI models. Today’s takeaway: “Variables make your code remember. Data types make it intelligent.” Now it’s your turn — 💬 Comment below: What’s the first variable you’ll create today? #PythonWithKeshav #Python #LearnToCode #Programming #CodingJourney #BeginnersInTech #PythonBasics #DataScience #AI

🚀 Just launched my first Python package — coding-agent-python! 🐍 An AI-powered coding assistant that can generate, read, write, and even run Python code — all from a simple natural language command. Just type 👇 > coding-agent "make a file for writing the roots of quadratic equation" — powered by Gemini AI 🤖 (Add your GEMINI_API_KEY in the environment) 💡 It can even generate LeetCode solutions by giving inputs and outputs ! This was my first deep dive into Python packaging, learning the role of core files like __init__.py, pyproject.toml, LICENSE, mkdocs.yml, and README.md while exploring: 🧩 uv by Astral — for building & publishing 📘 mkdocs — for simple documentation 📦 PyPI — for sharing with the world 🌍 Open source is the best way to learn — contributions are welcome! 🔗 GitHub: https://lnkd.in/gju9xqXe 🔗 Docs: https://lnkd.in/gejgdf32 🔗 PyPI: https://lnkd.in/gvdkCSFb #Python #OpenSource #AI #Gemini #DevTools #PyPI #Astral #mkdocs #uv

𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗮𝗺𝗯𝗱𝗮 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 — 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗢𝗻𝗲-𝗟𝗶𝗻𝗲𝗿𝘀 In Python, not every task needs a full-fledged function. Sometimes, all you need is a quick, one-line solution — that’s where 𝗟𝗮𝗺𝗯𝗱𝗮 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 come in. A lambda function is essentially a small, anonymous function designed for situations where defining a complete function would be unnecessary or too verbose. They’re concise, efficient, and ideal for short operations like mathematical expressions, filtering data, or transforming elements in a collection. 𝗪𝗵𝘆 𝗟𝗮𝗺𝗯𝗱𝗮 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗠𝗮𝘁𝘁𝗲𝗿: 🔹 They make code cleaner and more expressive. 🔹 They reduce the need for defining multiple short-lived functions. 🔹 They integrate seamlessly with functions like 𝗺𝗮𝗽, 𝗳𝗶𝗹𝘁𝗲𝗿, and 𝗿𝗲𝗱𝘂𝗰𝗲, which apply logic to multiple elements efficiently. 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗨𝘀𝗲 𝗟𝗮𝗺𝗯𝗱𝗮 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: ✅ When performing a simple operation once. ✅ When passing quick logic to another function. ✅ When you value readability and want to minimize boilerplate code. 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗔𝘃𝗼𝗶𝗱 𝗧𝗵𝗲𝗺: 🚫 For complex or multi-step logic — clarity always comes first. 🚫 When the function needs to be reused multiple times. Lambda functions highlight the beauty of Python — simplicity that doesn’t compromise power. They help you write code that’s both elegant and efficient. If you’ve ever used lambda functions in your workflow, how have they simplified your code? 📲 𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗴𝗿𝗼𝘂𝗽: 👉 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽:-https://lnkd.in/dTy7S9AS 👉𝗧𝗲𝗹𝗲𝗴𝗿𝗮𝗺:-https://t.me/pythonpundit 🔁 Share this with someone on a learning journey.