Field notes from the world of data extraction.
Articles, interviews and analysis on how data is gathered, used and fought over — written by the people closest to it.
The harness matters more than the model - Podcast EP07
"The model is the engine — but the harness is everything else." In Episode 7, we dig into why the infrastructure layer around your AI model matters more than the model itself, rank the best models available right now, and ask whether the open-weighted revolution is about to make frontier subscriptions obsolete.
Zyte's first Developer Community Meetup: the recap, slides, and recording
AI agents can now write, run, and self-heal your web scrapers, and in Zyte's first-ever Web Scraping Community Meetup we show you exactly how. Live demos, a Claude Code plugin that turns a prompt into production-ready data, and a fireside chat on where AI is really heading.
How to run any model inside Claude Code
I run GLM 5.2 inside Claude Code with the same tools, the same skills, and the same agent loop. Three environment variables is all it takes.
The best agent skill is the one that says the least
More instruction, worse output. Zyte's head of R&D on why telling your agent exactly what to do can blind it to the obvious answer.
Why I'm adding GLM-5.2 to my agentic coding arsenal
Is GLM-5.2 really closing the gap to Anthropic - and at just a fraction of the cost - or is it just more AI hype? I think so, and let me show you why.
How to build your first Scrapy extension
My Scrapy project now plays a triumphant fanfare when a crawl finishes clean and a sad trombone when it doesn't, which is also how I finally learned what Scrapy's extension points are actually for.
My personal agent setup: the architecture that runs my household and my DevRel work
"Four people, four diets, two work schedules, and a baby who answers to nobody. That's what finally made me build a personal agent." A walkthrough of the actual architecture I run to hold my household and my DevRel work together — profiles, skills, memory, and the web-data layer that makes it all reach the live web.
Data is the new water
For 20 years, we were told “data is the new oil”. That’s no longer true. In the era of the fluid web, information is more fundamental, cleaner and abundant than that.
Building robust agentic AI workflows with rapid web data
AI agents need access to public web data, right now. Tools connected to web scraping APIs empower agents to return live data quickly.
Now what exactly is loop engineering? And where do Anthropic's Fable 5 model and web scraping fit in?
A viral clip from Claude Code's creator put a name to something a lot of us have been circling: loop engineering. Here's why web scraping may be its best-fit domain — and what that means in practice.
Enhancing AI model performance with fresh web data
No-one likes an out-of-touch AI assistant. Fortunately, rapid refreshing can keep AI models aware of the very latest public information.
What's becoming of web scraping developers in the age of AI agents?
AI agents can generate code, suggest selectors, and draft crawl logic. What they can't do is design the system that decides when to stop, what to trust, and how to recover when the web pushes back. That job still belongs to a human.
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