PyFuncAI: LLM-Generated Python Functions at Runtime

Claude Add-In for Excel Apparently Claude for Excel is powerful because it uses python execution layer behind the scenes. Instead of forcing everything in a formula it translates everything into a python script. This gives it alot of flexibility to handle messier datasets than formulas and is definately more reliable for complex logic. Its like having a python engine for your spreadsheet, since its release about a month ago I was hooked and have not made another excel formula since. Give it a try its extremely powerful #Anthropic #Claude #Excel #AI #Automation

From zero to type-safe: How we brought static type checking to a large-scale Python codebase. What does it take to make a 4M+ line Python codebase safer - without slowing innovation? At Eightfold AI, we introduced gradual typing, automated type inference, and CI enforcement to catch type errors before they reach production. 👉 Read how we did it: https://lnkd.in/gcFPPDTR #EightfoldAI #Innovation #EngineeringBlog

Delighted to see this published! This was one of my favourite projects during my time at Eightfold AI. We tackled the challenge of migrating a 4M+ line Python codebase to static type checking using a gradual, CI-enforced approach. No "typing sprints", just a system that made the codebase safer with every pull request. Two things I'm particularly proud of in this approach: - The "Two-Tier" Strategy: Using Strict and Lenient modes to allow teams to opt-in at their own pace without breaking the build for everyone else. - Automated Inference: Leveraging MonkeyType to let production traffic do the heavy lifting of figuring out types. Setting up a production-ready Mypy config for a codebase of this scale is a notorious time sink. To help others skip the trial-and-error phase, we’ve included our actual configuration file in the blog. Check it out here: https://lnkd.in/grYP5-pP Huge thanks to Divyendra Mikkilineni Nachiket Agrawal Juthika Dabholkar Anurag Nilesh Thiyagaraj T (thiyaga) Varun Kacholia for helping with this!

Building an AI agent doesn't require a master's degree in computer science. It requires a fundamental understanding of how to guide a reasoning engine. When you build a ReAct (Reason + Act) agent from scratch in pure Python, you learn a profound truth: the magic isn't in the code. The code is just a simple while loop. The magic is in the System Prompt. 📌 Read More: https://lnkd.in/d-ZQNRGZ

512: Agentic loops are more than instructions. They're like installable packages for Python or Spark, capable of processing code and expounding patterns. Think of publishing to PDF using specific CSS and tools – it's a skill that deserves more credit. #AI #AgenticAI #MachineLearning #TechInnovation #FutureofAI

#story BREAKING: China has open-sourced a massive Python framework for building AI agents. It’s called AgentScope, a python framework built around Agent-Oriented Programming that lets you build AI agents visually with MCP tools, memory, rag, and reasoning capabilities. 100% Open Source. https://lnkd.in/gUw9d_ea

𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀, 𝗥𝗲𝗷𝗼𝗶𝗰𝗲! A full Python reimplementation of the Claude Code agent architecture is now open source! 🎉 Say goodbye to juggling npm/TypeScript/Rust just to dive into AI agent development. 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿? Python's simplicity and readability make it easier for developers to understand, modify, and run sophisticated AI models locally. This is a significant step forward for the Python community and AI innovation. 𝗪𝗵𝗮𝘁'𝘀 𝘆𝗼𝘂𝗿 𝘁𝗮𝗸𝗲 𝗼𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗮𝘀 𝘁𝗵𝗲 𝗴𝗼-𝘁𝗼 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗳𝗼𝗿 𝗔𝗜 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁? #AI #MachineLearning #Python #OpenSource #TechInnovation

Worked on a small project using LangChain to build a dynamic prompt system. Instead of hardcoded prompts, I used templates that adapt based on inputs like topic, audience, and tone. It helped me understand how real-world prompt engineering works. Still learning 👍 #GenAI #LangChain #Python

Python is often called slow. Mostly because it’s an interpreted language. But here’s what gets missed. Speed at the language level is only one part of the system. In real applications, performance depends more on architecture, I O handling, and how workloads are designed. That’s why Python continues to power AI systems, data platforms, automation tools, and large-scale backends. Heavy computation is often offloaded to optimized libraries or handled outside the main runtime. The rest is about writing efficient, structured code. So yes, Python is slower at a low level. But that rarely becomes the bottleneck in real systems. Time to break the myth. #MadForCoding #Python #SoftwareEngineering #BackendDevelopment #TechMyths #SystemDesign