Explore resources by topic or category

Multi-agent orchestration is having its moment. The diagrams are everywhere now. Boxes for planners, boxes for hands, boxes for daemons, arrows to a shared brain, a human floating at the top. They keep getting prettier. The part where the web pushes back is still the part nobody draws.

Data-gathering doesn’t have to be memory-intensive. You can fit the world’s weather on a 9cm-square board, when you move the work to a web scraping API.

For the last 30 days, I did one thing almost exclusively: I built scraping systems with AI agents, from the ground up, across real targets, with real deadlines. Not prototypes designed to impress in a demo, not isolated experiments running against a toy website, but production-grade pipelines that needed to ship and keep running.

I've been running a series of conversations with developers at Zyte to understand what's actually changed in the way they work since LLMs showed up. Not the headlines. The day-to-day. What they delegate, what they don't, what they notice, what surprises them.
This one was different on two counts.

New models can process larger inputs, and confuse themselves in the process. Context management techniques can solve the problem.

Quickly compare e-commerce products across any site with an agent, a skill and an AI-powered web scraping API.

I built a mood board pipeline that starts with a screenshot. Claude Skills and Zyte API for search, product extraction, and image embedding at any scale.

From LLM-powered extraction to agentic pipelines, here's how AI is reshaping every stage of the web scraping workflow in 2026 -- and what it means for your stack.

AI coding tools like Copilot can suggest outdated code and practices. Learn why this happens and how developers can ensure they use the latest standards.

An interview with Scrapy maintainer Adrian Chaves on Zyte’s Web Scraping Copilot, AI-generated parsing code, and building reliable scraping workflows.

Discover how AI copilots like Zyte’s Web Scraping Copilot are transforming developer workflows—making code a commodity and shifting value to problem-solving and prompting skills.

A failed AI experiment reveals why adding more data doesn’t always improve LLM outputs. Learn when web scraping, RAG, and curated datasets actually make AI better.