Wayand Bahramzy’s Post

How Pydantic AI Turned My Chaotic Data Into a Super‑Smart Python Model 🤖 I was juggling a legacy API that returned nested JSON like a tangled ball of yarn—lists inside dicts, optional fields, and a few hidden “type‑mismatch” bugs that broke the whole pipeline. Every time I wrote a new class, I added manual checks, and the codebase grew into a nightmare of try/except blocks. Enter Pydantic AI. I fed it a single example payload, and it instantly generated a hierarchy of BaseModel classes with proper type hints, default values, and validators for the edge cases. The next day, the same API response passed through the model without a single runtime error, and the auto‑generated docs showed exactly what each field meant. Adding a new optional field? Just update the example and let Pydantic AI regenerate—no more hand‑rolled parsing logic. Now my services serialize, deserialize, and validate data in one line, and the code reads like a story instead of a maze. #Python #PydanticAI #DataValidation

Most people try to scrape websites using random Python scripts and they work… until they don’t. Then come timeouts, bans, and broken selectors. That’s where Scrapy completely changes the game. It’s not just a library; it’s a framework built for automation at scale. It manages requests, handles errors, integrates proxies, processes items, and exports structured data — all out of the box. Once you learn Scrapy, you stop fighting your scripts and start engineering real data pipelines. It’s how I scrape thousands of pages efficiently, safely, and cleanly without touching a browser manually. If you’re serious about automation, learn Scrapy early. It’ll save you weeks of frustration and teach you what scalable scraping actually looks like. #Scrapy #webscraping #dataextraction #automation #dataengineering #bigdata #scrapingtips #webscraping #python 

🧠 Just tried out a really cool Python library — toon_format — and it’s a hidden gem for anyone working with LLMs or large data payloads. It’s a compact, human-readable serialization format that reduces context size by 30–60% vs JSON, while staying super easy to read and use. What makes it awesome: • YAML-like indentation • CSV-style tabular arrays • Minimal syntax, array validation • Python 3.8+ and battle-tested • Fully compatible with the official TOON spec ⚙️ Install it: pip install toon_format (or uv add toon_format) Quick example 👇 from toon_format import encode, decode encode({"name": "Alice", "age": 30}) # name: Alice # age: 30 encode([{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]) # [2,]{id,name}: # 1,Alice # 2,Bob We have been using it to trim LLM context payloads — super efficient and still human-friendly. 🚀 If you deal with JSON or token limits, give toon_format a try ! I have shared repository link in first comment. #Python #OpenSource #LLM #Serialization #AI #Developers #MachineLearning #GenAI

One of my favorite things about working with data is finding ways to make repetitive tasks simpler and more reliable. Recently, I built a Python script that automatically downloads and consolidates compliance data from publicly available sources, such as the FDA and other regulatory websites. The script then cleans and formats the information, saving it into a structured file that can be used for tracking and analysis. What used to take several manual steps can now be done in seconds, saving time and reducing the chance of human error. For me, it was a great opportunity to combine Python automation, data cleaning, and workflow optimization, skills I’m continuously developing in my data engineering journey. 🐍 Have you automated any manual task at work recently? What was the result? #Python #Automation #DataEngineering #DataCleaning #LearningInPublic #ContinuousImprovement

That ancient Excel file everyone's afraid to touch? The one with years of hidden formulas and tangled logic? You can now turn it into clean Python code. Automatically. Mito AI is here to save you from spreadsheet hell. Here's how it levels up your workflow: ✅ AI agent built specifically for Jupyter. ✅ Instantly translates all your Excel logic and formulas. ✅ Generates production-ready functions and tests. ✅ Delivers clean, reusable Python scripts in seconds. Check it out: http://bit.ly/3L8A1H6

Putting “Python” on your résumé is like saying “I know the internet.” Cool. But… what part? What corner? What battlefield? Python by itself doesn’t tell your future employer anything. Python is everything. – Web scraping – Data engineering – Machine learning – Automation – APIs – ETL – Video editing – And a thousand more lanes. What actually matters is the libraries and the problems you can solve. You don’t say: “I know Python.” You say: “I built a Selenium workflow that scrapes 10,000 records across paginated results.” “I automated daily reporting with Pandas + SQLAlchemy.” “I edited AMV videos with MoviePy and automated batch renders.” That shows skill. That shows thinking. That shows experience. Tools don’t get you hired. Proof does. #Python #TechCareer #DataEngineering #Automation #ProgrammingTips #CareerAdvice #AMVEdits #BuildersMindset

🌟 Understanding Algorithm Analysis — Mind Map for Beginners Data Structures & Algorithms (DSA) सीखते समय सबसे ज़्यादा confusion Time Complexity और Big-O पर होती है। To make it super simple, I created a Mind Map that covers: 🔹 Time Complexity 🔹 Space Complexity 🔹 Big-O Rules 🔹 Common Complexities (O(1), O(n), O(log n), O(n²)) 🔹 How to analyze an algorithm 🔹 Dry-run + operation counting method Mind Maps help in understanding DSA visually and quickly. #MindMap #DSA #Algorithms #BigONotation #TimeComplexity #SpaceComplexity #TechLearning #CodingJourney #Python #ComputerScience

🤖 𝐏𝐘𝐓𝐇𝐎𝐍 𝐈𝐍𝐒𝐈𝐆𝐇𝐓 𝐅𝐎𝐑 𝐀𝐈 𝐀𝐆𝐄𝐍𝐓𝐒 & 𝐓𝐄𝐗𝐓-𝐓𝐎-𝐒𝐐𝐋 𝐁𝐔𝐈𝐋𝐃𝐄𝐑𝐒 While working on a 𝐑𝐀𝐆-𝐛𝐚𝐬𝐞𝐝 𝐓𝐞𝐱𝐭-𝐭𝐨-𝐒𝐐𝐋 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐨𝐫, a subtle but powerful distinction in Python: 🔹 list() → a 𝐛𝐮𝐢𝐥𝐭-𝐢𝐧 𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐨𝐫 that actually 𝘤𝘳𝘦𝘢𝘵𝘦𝘴 a list at runtime. 🔹 List → a 𝐭𝐲𝐩𝐞 𝐡𝐢𝐧𝐭 from the typing module that 𝘥𝘦𝘴𝘤𝘳𝘪𝘣𝘦𝘴 what the list contains for tools and AI frameworks. When building 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 or 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬, this difference matters. - list() controls how your data structures behave during execution. - List defines how your system’s components (like retrievers, LLMs, or SQL generators) communicate type expectations. Clear typing helps your agents validate inputs, prevent errors, and maintain consistency across multiple asynchronous nodes — especially in complex 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐚𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐑𝐀𝐆) workflows. 𝘐’𝘷𝘦 𝘢𝘵𝘵𝘢𝘤𝘩𝘦𝘥 𝘮𝘺 𝘧𝘶𝘭𝘭 𝘔𝘦𝘥𝘪𝘶𝘮 𝘱𝘰𝘴𝘵 𝘣𝘦𝘭𝘰𝘸 𝘧𝘰𝘳 𝘮𝘰𝘳𝘦 𝘥𝘦𝘵𝘢𝘪𝘭𝘴. #Python #LangChain #AI #DataEngineering #MachineLearning #TextToSQL #SoftwareDevelopment #LearningEveryDay

My recent hands-on article on building an end-to-end machine learning model. It starts from data loading and preprocessing to model training, evaluation, and ends with deployment with FastAPI and Docker. It’s a simple, reproducible setup built entirely with Python scripts that runs locally and mirrors a real production workflow. #endtoendML #docker #fastAPI #ML #datascience https://lnkd.in/dzVcdrha

The AI that understands context best will win. While coding today, I gave two LLMs on different IDEs the same prompt: “Please read the content from /Users/deepak/Downloads/my_file.MD.” One instantly wrote a short 2-line Python script that did exactly what I wanted. The other thought I was asking it to access its own server’s files and refused, assuming it was an attack. Same prompt, very different results. Understanding what users actually mean, not just what they say, makes all the difference.