Introducing skwrapper: Simplify ML Model Training with Scikit-Learn

Learning Python syntax is the easy part. You can learn if statements and for loops in a weekend. But the market doesn’t just pay for people who know Python; it pays for people who can solve specific problems. If you stop at the basics, you’re just a hobbyist. If you want to be indispensable, you need to bridge the gap between "writing code" and "building systems." Which "Indispensable" Stack are you building? The Data Powerhouse: Python + SQL + Tableau + Cloud (AWS/Azure). 📊 Goal: Turn raw noise into business decisions. The Web Architect: Python (Django/FastAPI) + React + PostgreSQL + Docker. 🌐 Goal: Build scalable, production-ready applications. The AI Innovator: Python + PyTorch/TensorFlow + Scikit-Learn + MLOps. 🤖 Goal: Deploy models that actually work in the real world. The reality check: Python is the "glue" that holds these stacks together, but the glue is useless if you don't have the bricks. Stop asking, "What language should I learn next?" and start asking, "What problem do I want to solve?" Once you know the problem, the rest of your stack will reveal itself. What’s your current "plus-one" skill you’re adding to Python right now? Let’s talk about it in the comments! 👇 #PythonProgramming #SoftwareEngineering #CareerGrowth #CodingTips #DataScience #WebDevelopment #TechCareer

I had a simple question about a Django codebase the other day: "which functions call this utility, and are any of them tested?" Cursor's built-in search found the function. But it couldn't tell me who calls it, or whether those callers have test coverage. That's the problem with text-based code search. It finds matches, not relationships. CodeScan takes a different approach. It parses Python source code using AST analysis, extracts every class, function, and call relationship, and stores the whole thing in a Neo4j graph database. The codebase becomes a queryable structure, not a pile of text files. Once it's in the graph, the questions get interesting. "Show me all functions that transitively call database_connect." "Which functions have zero test coverage?" "Find recursive call chains." These are Cypher queries that run in milliseconds, not grep commands that return noise. The MCP server exposes 20+ tools, so Cursor can answer these questions directly. You ask in natural language, the AI translates to a graph query, and you get back actual structural insight. One design choice I'm happy with: test detection is pattern-based and configurable. It recognizes pytest, unittest, nose conventions, and custom naming patterns. It creates TESTS relationships in the graph, so you can instantly see which production code has coverage and which doesn't. 5,000+ lines of Python. Works on any Python project. The hard part wasn't the AST parsing - it was modeling the relationships correctly in the graph so that queries stay fast as the codebase grows. #CodeAnalysis #Neo4j #MCP #Python #DeveloperTools #GraphDatabase

Most beginners think Python dictionaries are hard because of syntax. But the real challenge is the thinking shift. When you move from a list like: ["Deepak", 25, 85] to a dictionary like: {"name": "Deepak", "age": 25, "marks": 85} You’re not just changing code style. You’re organizing data in a way that makes more sense. Lists store values. Dictionaries show relationships. And when your project grows, that small change makes your code easier to manage. Sometimes better coding is not about complex logic — it’s about better data structure choices. #Python #Coding #Programming

I just published a FREE Python course. No signup. No payment. No ads. Just open the link and start learning. 🐍 🔗 https://lnkd.in/gPYPRpra ━━━━━━━━━━━━━━━━━━━━━━ Here's exactly what's inside: 🟢 BEGINNER (Lessons 1–10) → Variables, Strings, Numbers → If/Else, Loops, Lists → Dictionaries, Functions 🔵 INTERMEDIATE (Lessons 11–17) → File handling, Error handling → OOP & Classes, pip & modules → APIs, requests library 🟣 ADVANCED (Lessons 18–21) → Decorators, Generators → Async/Await, Data Structures 🔶 PRO LEVEL (Lessons 22–25) → FastAPI backend from scratch → Claude API in Python → Supabase database integration → Real projects you can deploy ━━━━━━━━━━━━━━━━━━━━━━ Why I built this: I'm on Day 12 of a 75-day challenge to go from beginner to professional AI developer. Every single day I build something real and push it to GitHub. Today's build: a complete Python course in a single HTML file. No framework. No npm. No server. Just open it in your browser and learn. ━━━━━━━━━━━━━━━━━━━━━━ If this helps even ONE person learn Python — it was worth building. Save this post. Share it with someone learning to code. 🔗 https://lnkd.in/gPYPRpra #Python #LearnPython #FreeLearning #PythonForBeginners #AI #FastAPI #Programming #CodingLife #OpenSource #100DaysOfCode #AIDeveloper #Day12of75

🌶️ 💪 Modern API workloads aren’t “one user, one request” anymore—they’re bursts of concurrent traffic, mixed fast/slow calls, and unforgiving tail-latency expectations. That’s why I’m excited to share our new post on Select AI for Python 1.3 and a major step forward for production-grade concurrency: connection pooling. https://lnkd.in/eze4sUCb With 1.3, developers can now pool connections using: select_ai.create_pool() select_ai.create_pool_async() In the blog, learn what changed from standalone connections, what we measured by integrating pooling into a FastAPI service, and how to think about choosing a pool size that fits your workload. The results: better throughput, improved p95/p99 latency, and more predictable behavior under load—exactly what matters in real-world services. If you’re running (or planning) concurrent Python services with Select AI, this is one of the simplest, highest-impact upgrades you can make. #Oracle #Database #SelectAI #OracleAI #Python #FastAPI #Concurrency #ConnectionPooling

🚀 Boosting AI app concurrency with smarter database connections The latest update to Oracle Autonomous Database Select AI for Python shows how connection pooling can significantly improve concurrency and throughput for AI-powered applications. Instead of opening a new database connection for every AI request, connection pools reuse a small set of connections, reducing overhead and enabling many concurrent AI calls from Python apps. Why it matters: ⚡ Higher concurrency for AI workloads 🔁 Reused connections reduce latency and overhead 🧠 Better performance for NL2SQL, RAG, and generative AI apps built on Autonomous Database For developers building AI-driven data apps in Python, this means more scalable, responsive AI pipelines with minimal code changes. #AI #Python #Databases #GenAI #AutonomousDatabase

If you're building AI apps with Python, this one's worth your time. MongoDB is running a free 5-part masterclass series covering everything from the document model basics through to vector search, RAG, and autonomous agents using PyMongo, LangChain, and LangGraph. Parts 1–4 are available on-demand right now. Part 5 (Agents with LangGraph) is live on April 1st. Complete the series and you'll earn an official MongoDB Skill Badge too. https://lnkd.in/g6ZT5V_q? #AI #Python #MongoDB #RAG #VectorSearch #Agents #LLM

Day 12 of My Data Science Journey — Python Lists: Methods, Comprehension & Shallow vs Deep Copy Today’s focus was on one of the most essential data structures in Python — Lists. From data storage to manipulation, lists are used everywhere in real-world applications and data science workflows. 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝: List Properties – Ordered, mutable, allows duplicates, and supports mixed data types Accessing Elements – Used indexing, negative indexing, slicing, and stride for flexible data access List Methods – append(), extend(), insert() for adding elements – remove(), pop() for deletion – sort(), reverse() for ordering – count(), index() for searching and analysis Shallow vs Deep Copy – Understood that direct assignment does not create a new copy – Used copy(), list(), slicing for safe duplication – Learned the importance of copying, especially with nested data List Comprehension – Wrote concise and efficient code using list comprehension – Combined loops and conditions in a single readable line Built-in Functions – Used sum(), len(), min(), max() for quick data insights Additional Useful Methods – clear(), sorted(), zip(), filter(), map(), any(), all() 𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Understanding how lists work — especially copying and comprehension — is critical for writing efficient and bug-free Python code. Lists are not just a data structure; they are a core tool for solving real-world problems. Read the full breakdown with examples on Medium 👇 https://lnkd.in/gFp-nHzd #DataScienceJourney #Python #Lists #Programming

703 Python files. 300 Markdown files. I am a Python developer who writes 30% documentation. I pulled my language distribution from 19 days of AI-assisted work. The numbers did not match my identity. Python was first. Expected. But Markdown — planning docs, architecture decisions, agent instructions — was second. Not YAML. Not JSON. Prose. The AI needed written instructions more than it needed configuration files. Then the tools told a stranger story. 2,254 bash commands. 760 file reads. 416 edits. For every line I changed, I ran five commands investigating what to change. Before AI tools, my ratio was closer to 2:1. The AI made me a better reader, not a faster writer. That distinction matters for how you invest. If you evaluate AI coding tools on "lines generated per hour," you are measuring the tail, not the dog. The value is in investigation — 2,254 commands identifying what to change before a single edit. 📊 Three changes after seeing this: I optimized prompts for codebase exploration, not code generation. I wrote better CLAUDE.md instructions — Markdown, not Python. And I stopped timing "how fast can it write this function" and started timing "how fast can it understand this module." My exploration sessions — 8 out of 81 — produced zero code and the best decisions of the month. What does your read-to-edit ratio look like — and have you ever measured it? — Ernest Hemingway, Journalist AI #DeveloperProductivity #AITools #SoftwareEngineering