Open-source PyFuncAI for dynamic function generation in Python

AI writes Python code... backwards. I noticed something that kept bugging me: AI coding assistants like Claude Code most often generate Python functions in bottom-up order, with helpers first and entry points last. What's the deal? This forces you to read code from the bottom up to understand what it does. The exact opposite of how we naturally read, from the headline to the supporting details, like a newspaper! So I built 🔧 flake8-stepdown, a tool that detects and auto-fixes function ordering violations. It works as a flake8 plugin and as a standalone CLI. I wrote a blog post to explain how it works! 💡 Don't just prompt AI to "write better code", constrain it with deterministic tools (linter, auto-formatter, ...) Links in the comment below 👇 #Python #AI #CodeQuality #DeveloperTools #OpenSource

🚀 Day 4 of My Generative & Agentic AI Journey! Today’s focus was on one of the most commonly used data types in Python — Strings. Here’s what I learned: 🔤 Strings in Python: • Strings are immutable — once created, they cannot be changed • Any modification creates a new string instead of changing the original 🔍 Indexing & Slicing: • Accessing individual characters using indexing • Extracting parts of a string using slicing • Learned how powerful slicing is for handling text data 🔐 Encoding & Decoding: • Understood how strings are converted into bytes (encoding) • And how bytes are converted back to strings (decoding) • Important for handling data, APIs, and real-world applications 👉 Key takeaway: Strings are everywhere — from user input to AI models — and understanding how to work with them efficiently is a must. Another step closer to mastering the fundamentals 💪 #Day4 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

🐍 I began learning Python today. Here is precisely how my day was spent. No fluff. No shortcuts. Just the basics. Today I covered Python syntax, variables, data types, operators, input/output, conditionals, and loops — then put it all to work by solving around 20 beginner problems including factorial, bubble sort, palindrome check, and leap year validation. The goal was never just to get the right output. It was to understand why every line works. This is Day 1 of a long, structured path: Python → Django → ML → Deep Learning → NLP → MLOps Full progress documented on GitHub: https://lnkd.in/gGsjWtee Progress, not perfection. 🚀 #Python #MachineLearning #Django #LearningInPublic #100DaysOfCode #DataScience

#NeuralScript++ — The Road Ahead The #Python Superset #NeuralScript++ makes Python better today. Not by force, but through natural evolution. As more of your codebase adopts pipe operators, pattern matching, and domain-specific shorthand, it may gradually diverge from vanilla Python—and that’s perfectly acceptable. What's coming: Gradual typing that actually works — not mypy bolted on, but type inference built into the transpiler. Your code gets type-safe incrementally, without annotation burden. Async-first AI pipelines — training, data loading, and inference stages run concurrently by default. No asyncio boilerplate. The language handles parallelism. Auto-migration tooling — point it at a Python project and it suggests #NeuralScript++ rewrites that reduce code volume while preserving behavior. Accept one at a time. No big-bang rewrite. And #Python interop gets even deeper — seamless calling between #NeuralScript++ and #NeuralScript core, so you can gradually move performance-critical paths to the full DSL while keeping Python for glue code. The on-ramp gets smoother. The destination gets more compelling. 🔗 https://lnkd.in/dTE6SYeK 🌐 neuralecosystems.com demo: https://lnkd.in/dXUw7rDu #NeuralEcosystems - Let the world unite to explore the universe together! #AI #MachineLearning #DeepLearning #Python #OpenSource #GPU #StartupLife #Engineering #NeuralEcosystems #NeuralOS #NeuralSCRIPT #NeuralSCRIPT++ #NeuralCPU #NeuralGPU  #NeuralFUSE #NeuralRV #NeuralEDGE #NeuralDB #NeuralPIPE  #NeuralSENSE #NeuralAUTO #NeuralFUZZY #NeuralIP #NeuralSDR  #NeuralMESH #NeuralUI #NeuralZONE #NeuralGAURD #NeuralSHARE #NeuralGHOST #NeuralBIO #NeuralHEALTH #NeuralNAV #NeuralWEB  #UAE #Innovation

🚀 Built a Simple AI Image Analyzer using Python & Streamlit Today I built a small project where users can upload an image and ask questions about it using AI. 🔹 Technologies Used: • Python • Streamlit • Google Gemini API • PIL (Python Imaging Library) 💡 How it works: 1️⃣ Upload an image 2️⃣ Enter a prompt/question about the image 3️⃣ AI analyzes the image and generates a response This project helped me understand: Integrating Generative AI APIs Handling image inputs in Python Building simple AI web apps using Streamlit I'm currently learning and exploring Data Analytics, AI tools, and Python projects. Excited to build more practical projects! 🚀 #Python #Streamlit #GenerativeAI #GoogleGemini #AIProjects #LearningInPublic #DataAnalytics

🚀 Post 1 — Day 25 🧠 Day 25 – The 30-Day AI & Analytics Sprint Today's Python question 👇 a = [1, 2, 3] b = a b += [4, 5] print(a) ❓ What will be the output? A) [1, 2, 3] B) [4, 5] C) [1, 2, 3, 4, 5] D) Error 💡 Hint: Remember that Lists in Python are mutable objects. Also think about: What happens when we assign b = a? Does += create a new list or modify the existing one? 👇 Write your answer in the comments before checking the solution! #Python #Programming #AI #DataScience #CodingChallenge #30DayChallenge

6 months ago I published a Python package with my teammate Harsh Bhatt. Today, LazyCook has crossed 2,500 downloads and we're in the top 7% of Python contributors worldwide. What is LazyCook? A multi-agent AI assistant with 4 specialized agents — Generator, Analyzer, Optimizer, and Validator — that work together on every single query to give the best possible answer. It supports PDFs, DOCX, CSV, context across sessions, and even auto-generates graphs for data-heavy queries. The best part? Quality is scored on every response (Completeness 40%, Accuracy 40%, Length 20%). pip install lazycook #Python #OpenSource #PyPI #BuildInPublic #AI

🎬 I watched a video so you don't have to. Here's the executive summary: "⚡️Monty: the ultrafast Python interpreter by Agent..." Monty is an ultrafast Python interpreter designed for AI agents, delivering massive startup and runtime speed while preserving Python semantics. 🔑 Key Insights: → Monty targets agent workloads to reduce cold-start and per-call overhead, enabling faster decision loops. → Compatibility with existing Python code is prioritized; but some edge-case features may have limited support. → Adoption hinges on measurable gains in common agent pipelines and a simple integration path into current stacks. 💎 Best quote: "Monty is an ultrafast Python interpreter for agents" ⏱️ Time saved: 30 minutes — Full breakdown in my first comment 👇 #AI #VideoAnalysis #Productivity #Steek

The report that built itself Isabel needed a monthly report 📊. CSV. KPIs. Charts. She asked AI to generate a Python script with Pandas. Done in seconds. But she didn’t trust the output blindly. She validated KPIs. Checked calculations. Adjusted formatting 🧠. AI created the report. Isabel ensured it made sense ⚙️. Data without validation is just noise. #DataAnalytics #Python #AIcoding 🚀