Demonstration of building an agentic workflow in n8n that automates the entire process of delivering a daily newsletter, with technical pro-tips. Learn how to use Bright Data’s verified node in n8n to extract fresh content from Reddit, Hacker News, and Twitter, go beyond the front page for deep context, configure an AI Agent with precise prompts and tools, deliver an HTML newsletter to your inbox on a schedule, and publish the newsletter as a DEV article via the DEV API. Live requests from viewers will be taken and built on the fly.

Live demo showing how to build an agentic workflow in n8n that automates the creation of a daily personalized newsletter. Learn to use Bright Data’s verified node to extract fresh content from Reddit and Twitter (and beyond Hacker News) for deep context, configure an AI Agent with precise prompts and tools to tailor recommendations, and deliver an HTML newsletter on a schedule. The session will also include live viewer requests to illustrate real-time data-driven customization.

A demonstration of building an agentic workflow in n8n to automate a personalized daily newsletter powered by real-time data. Learn how to use Bright Data’s verified node in n8n to extract content from Reddit, Hacker News, and Twitter; configure an AI Agent with precise prompts; deliver an HTML newsletter; publish to the DEV community via the DEV API; and take live viewer requests during the show.

In this episode, we are partnering with the AI Advisory team in Microsoft for Startups to explore a unique use case by a leading startup in the program: OneAI. OneAI unique approach to Enterprise AI is to curate and fine-tune the world's top AI capabilities and package them as APIs, empowering businesses to deploy tailored AI solutions in days. During this episode, we will explore how their teams in building AI solutions are designed to ensure consistent, predictable output, and alignment with the source documents, bolstering trust and enhancing business outcomes. We will also, share some product demos around building their AI Agent, optimizing both the tuning process and long-term performance in terms of cost, speed, and carbon footprint, all while emphasizing transparency and explainability.