Pydantic AI Framework for Python LLM Agents

Building with LLMs in Python involves a lot of moving parts. You need to call model APIs, write prompts that produce reliable results, set up retrieval pipelines, and eventually build agents that can reason and use tools. We put together a learning path that walks through all of it, step by step: - Call LLM APIs from OpenAI, Ollama, and OpenRouter - Write effective prompts that return structured output - Build RAG pipelines with LlamaIndex, ChromaDB, and LangChain - Create AI agents using Pydantic AI and LangGraph - Connect agents to external tools and data via MCP It's aimed at Python developers who are comfortable with the language and want to start building real applications on top of language models. https://lnkd.in/ggdqNgNu

OpenAI to buy Python toolmaker Astral to take on Anthropic OpenAI acquires Astral to boost AI coding tools. https://lnkd.in/e9nCyT_9 Permus Blog — The Gateway to the World of Technology and Software #şevketayaksız #permus

Curious how to integrate your Python code into a C++ project? I took some time to flesh out my previous article on calling Python scripts from with C++ with input- and output-arguments. I added figures, added content, made the text more readable, and added a section on multithreading. No generative models used. You can find the updated article here, and more posts to follow soon: https://lnkd.in/dWFngEvV

Users of Python Fundamentals, 2/e on O'Reilly: The new Lesson 5, Lists and Tuples, is live, and I am sending Lesson 06, Dictionaries and Sets, for processing right now. Should be live before the end of the week. https://lnkd.in/ePMpTP5t #Python Pearson Deitel & Associates

Hyperparameter Optimization Machine Learning using bayesian optimization #machinelearning #datascience #hyperparameteroptmization #bayesianoptimization Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. This is a constrained global optimization package built upon bayesian inference and gaussian processes, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions and situations where the balance between exploration and exploitation is important. Bayesian optimization works by constructing a posterior distribution of functions (gaussian process) that best describes the function you want to optimize. As the number of observations grows, the posterior distribution improves, and the algorithm becomes more certain of which regions in parameter space are worth exploring. https://lnkd.in/gq9d2Pi6

A 630-line Python script, one GPU, and zero human input overnight. Janakiram MSV breaks down how Andrej Karpathy’s AutoResearch system automated dozens of ML experiments.

🔹How to Build a General-Purpose AI Agent in 131 Lines of Python Implement a coding agent in 131 lines of Python code, and a search agent in 61 lines 🔹 In this post, we’ll build two AI agents from scratch in Python. One will be a coding agent, the other a search agent. Why have I called this post “How to Build a General-Purpose AI Agent in 131 Lines of Python” then? Well, as it turns out now, coding agents are actually general-purpose agents in some quite surprising ways. What I mean by this is once you have an agent that can write code, it can: Do a huge number of things you don’t often think of as involving code, and Extend itself to do even more things. It’s more appropriate to think of coding agents as “computer-using agents” that happen to be great at writing code. That doesn’t mean you should always build a general-purpose agent, but it’s worth understanding what you’re actually building when you give an LLM shell access. That’s also why we’ll build a search agent in this post: to show the pattern works regardless of what you’re building. #python #ai #claude #anthropic #llm #aiagent #gemini #git #batch Full Credit to Hugo Bowne-Anderson 👏 Read the full article here: https://lnkd.in/dtvhnmVu

Machine Learning Audio Data using python speech features #machinelearning #datascience #audiodata #pythonspeechfeatures SpeechPy is an open source Python package that contains speech preprocessing techniques, speech features, and important post-processing operations. It provides most frequent used speech features including MFCCs and filterbank energies alongside with the log-energy of filter-banks. The aim of the package is to provide researchers with a simple tool for speech feature extraction and processing purposes in applications such as Automatic Speech Recognition and Speaker Verification. Automatic Speech Recognition (ASR) requires three main components for further analysis: Preprocessing, feature extraction, and post-processing. Feature extraction, in an abstract meaning, is extracting descriptive features from raw signal for speech classification purposes. Due to the high dimensionality, the raw signal can be less informative compared to extracted higher level features. Feature extraction comes to our rescue for turning the high dimensional signal to a lower dimensional and yet more informative version of that for sound recognition and classification. Feature extraction, in essence, should be done considering the specific application at hand. For example, in ASR applications, the linguistic characteristics of the raw signal are of great importance and the other characteristics must be ignored. On the other hand, in Speaker Recognition (SR) task, solely voice-associated information must be contained in extracted feature. So the feature extraction goal is to extract the relevant feature from the raw signal and map it to a lower dimensional feature space. The problem of feature extraction has been investigated in pattern classification aimed at preventing the curse of dimensionality. There are some feature extraction approaches based on information theory applied to multimodal signals and demonstrated promising results. https://lnkd.in/gb9Ph5MX

🐍📰 In this tutorial, you'll learn all about the k-Nearest Neighbors (kNN) algorithm in Python, including how to implement kNN from scratch, kNN hyperparameter tuning, and improving kNN performance using bagging #python

🚀 Just published a new article on Generative AI in Python. I’ve covered how to build production-ready systems using concepts like FastAPI, RAG, and performance optimization. If you're into Python, backend development, or AI, this might be useful. #GenerativeAI #Python #SoftwareEngineering