Introducing MiniBot: A Bare-Metal Python AI Agent Framework

PyFuncAI Launches LLM-Generated Python Functions at Runtime 📌 PyFuncAI lets LLMs dynamically generate and run Python functions at runtime-no static toolsets needed. Developers can now build flexible AI agents that solve novel problems on the fly, reducing maintenance overhead while keeping code adaptable. Perfect for agentic systems craving real-time, adaptive logic. 🔗 Read more: https://lnkd.in/d_W49MNx #Pyfuncai #Llmgenerated #Pythonruntime #Naturallanguage #Functionsynthesis

LangChain is a powerful framework for Python and JavaScript, designed to simplify building AI-powered applications. Its core strength lay in its platform-agnostic approach, allowing developers to switch between different AI models without rewriting their entire application logic. It's composability makes LangChain a go-to for quickly prototyping applications, especially RAG (Retrieval-Augmented Generation) based ones. #LangChain #AI #GenerativeAI #Python #JavaScript #RAG

From Pydantic: “A minimal, secure Python interpreter written in Rust for use by AI. Monty avoids the cost, latency, complexity and general faff of using a full container based sandbox for running LLM generated code. Instead, it lets you safely run Python code written by an LLM embedded in your agent, with startup times measured in single digit microseconds not hundreds of milliseconds.” https://lnkd.in/gE_dPEp6

I crashed production on Day 1. Not bad code. Not the wrong model. I picked the wrong framework. Here's what nobody told me about LangChain vs LlamaIndex 👇 (Swipe through — slide 6 will save you weeks of refactoring) What are you building with these? Drop it in the comments 🔥 #AI #LangChain #LlamaIndex #RAG #LLM #AIEngineering #Developers #GenerativeAI #Python #BuildInPublic

Those cool times when we could have endless conversations about which language is better in a given area are finished. Now we just have benchmarks, so we don't have to argue. We can focus on the meaning of life or the existence of god. Because if it comes to discussing which languages are best for generating code using Claude in terms of cost, for now, we are settled.

We might see a spike in the Ruby and Rails communities as people discover how well-suited they are for AI. Excited to see what the future holds.

Hot take, possibly spicy. We're in such early days of using these tools that comparisons like this are best viewed as transient artifacts that help us think about the problem. "Types don't help AI, they actually hinder it" -- for now, in this situation, with the current state of both tooling and training codebases, .....

😵 Thread-local storage works great... until you move to async. Then the weird stuff starts. Request IDs bleeding between coroutines. Background tasks sharing state. “Random” bugs that disappear under logging. Sound familiar? Async doesn’t care about threads. It cares about execution context. In my latest article I explain: 🔍 Why threading.local() fails under asyncio 🧠 How ContextVars isolate state per coroutine ⚙️ Real examples with async tasks and request-scoped data 👉 https://lnkd.in/d_aVTDtW #python #softwaredevelopment #backend #engineering #asyncio

A hands-on collection of real-world implementations to build LLM-powered applications using Python, LangChain, and Hugging Face. What’s inside: • RAG pipelines (Chroma / FAISS) • Custom/opensource LLM integrations • Memory & prompt engineering • Autonomous agents (ReAct style) Built with 2026 best practices to help you get started with production-ready #AI apps. #LLM #LangChain #HuggingFace #Python #MachineLearning #RAG #GenerativeAI https://lnkd.in/gqQ5sZVc

Building AI apps with LangChain ❓ Then understanding its core components isn’t optional — it’s foundational. I’ve just published a hands-on guide explaining LangChain with practical Python examples, covering: → Models → Messages → Tools → Agents → Memory → Streaming → Structured Output → Middleware I break down each of these in a practical LangChain guide — with working Python code. This is implementation focused, not theory. Full article in the first comment 👇 #LangChain #AIEngineering #Python #LLM #GenerativeAI