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A host of additions to Zyte’s agentic scraping toolbelt helps developers go from prompt to working spider code and beyond.

What's the smallest harness that still works? Turns out it's already sitting inside almost every coding agent you have installed — headless mode: same loop, tools, and reasoning as the interactive agent, minus the human in the chair. We point it at a web page and pull clean, structured data out the other end in about ten lines.

We ran nine models, including the new GPT-5.6, through our Zyte Scraping Code Benchmark. Claude Fable 5 puts Sol and others in the shade - but it’s pricey, and the best extraction code still depends on the best infrastructure.

When OpenAI shipped GPT-5.6 and Anthropic's Fable 5 was sitting at the top of the price list, my question was not "which one wins a leaderboard." It was the one I actually pay for: for the scraping I do, how much model do I need to buy?

Most AI skills are never tested — and it shows. Here's how Zyte evaluates scraping skills like real software, catching failures demos miss.

Point it at a website, tell it which fields you want, get back clean structured records. That's the agent we're designing in this post — and the interesting part isn't the model, it's the harness decisions that make it actually reliable at scale.

Same model, same weights, zero retraining — LangChain changed nothing but the scaffolding around a coding model and jumped it from 30th place to the top five on a benchmark. That scaffolding has a name: the harness. And it's the part you actually control.

"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.

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.

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.

"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.

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.