AI agents have fundamentally changed the threat model of AI model-based applications. By equipping these models with plugins (also called tools), your agents no longer just generate text; they now ...

In this tutorial, we take a deep dive into the TaskTrove dataset on Hugging Face and build a complete, practical workflow to efficiently explore it. Instead of downloading the full multi-gigabyte ...

An agent harness is the scaffolding that lets an AI model operate autonomously on a real task: run tools, observe results, and loop until the job is done. Unlike a chat interface where you steer every ...

We begin this tutorial by showing how we can combine MLE-Agent with Ollama to create a fully local, API-free machine learning workflow. We set up a reproducible environment in Google Colab, generate a ...

Is your feature request related to a problem? Please describe. Pyright will report UnnecessaryIsInstance on isinstance (x, type (None)) but it doesn't report UnnecessaryComparison on x is None. I ...

Python 3.11 introduced the Specializing Adaptive Interpreter. When the interpreter detects that some operations predictably involve the same types, those operations are “specialized.” The generic ...

Large language models (LLMs) by themselves are less than meets the eye; the moniker “stochastic parrots” isn’t wrong. Connect LLMs to specific data for retrieval-augmented generation (RAG) and you get ...

Code embeddings are a transformative way to represent code snippets as dense vectors in a continuous space. These embeddings capture the semantic and functional relationships between code snippets, ...