article thumbnail

OpenAI Swarm: A Hands-On Guide to Multi-Agent Systems

Analytics Vidhya

OpenAI Swarm – launched in 2024, is an experimental framework designed to simplify the orchestration of multi-agent systems for developers. It aims to streamline the coordination of AI agents through scalable and user-friendly mechanisms, making it easier to manage interactions within complex workflows.

article thumbnail

Best Python Tricks in Jupyter Notebook

Analytics Vidhya

When it is combined with Jupyter Notebook, it offers interactive experimentation, documentation of code and data. Keyboard shortcuts, magic commands, interactive widgets, and visualization tools can streamline workflow […] The post Best Python Tricks in Jupyter Notebook appeared first on Analytics Vidhya.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

All About Google’s NotebookLM

Analytics Vidhya

Introduction Google’s NotebookLM, an experimental AI-driven notebook, is designed to transform the way we interact with and utilize LLMs.

article thumbnail

New AI from HyperWrite Can Browse the Web Like a Human

Analytics Vidhya

An experimental AI agent that can browse the internet and interact with websites much like a human user has been introduced by HyperWrite, a startup well-known for its generative AI writing extension.

article thumbnail

Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 364
article thumbnail

Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

ML apps needed to be developed through cycles of experimentation (as were no longer able to reason about how theyll behave based on software specs). The skillset and the background of people building the applications were realigned: People who were at home with data and experimentation got involved! How will you measure success?

Testing 168
article thumbnail

Pair Programming with AI

O'Reilly on Data

That cyclic process, which is about collaboration between software developers and customers, may be exactly what we need to get beyond the “AI as Oracle” interaction. Any writer, whether of prose or of code, knows that having someone tell you what they think you meant does wonders for revealing your own lapses in understanding.