Large Language Models (LLMs)
Articles from the Zyte blog about Large Language Models (LLMs).
How to convert a website to Markdown for AI: 7 methods compared
Seven realistic ways to turn web pages into clean Markdown for AI - what each method covers, where each breaks, and how to choose for training, RAG and agent pipelines.
Train AI on industry-leading Markdown with Zyte’s new extraction mode
Zyte API can now extract a web page’s main content directly as clean, structured Markdown for AI systems, RAG pipelines and developer workflows.
Four in 10 sites block AI bots with robots.txt
Nearly four in 10 of the world's top sites are now closed to well-behaved AI crawlers. Here's what operators actually do with robots.txt, and which AI agents they block or welcome.
Claude Fable 5 is the new best model for writing scrapers
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.
Harness Engineering, part 2: harnessing a data extraction agent
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.
Harness Engineering, part 1: what is an agent harness and why it matters
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 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.
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
Ayan Pahwa ran GLM-5.2 against Claude Sonnet 4.6 and Opus 4.8 on real scraping work: parity on extraction, 8.5x cheaper on planning, slower and wordier.
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.
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.
Four sweet spots for AI in web scraping
Discover how AI and LLMs are enhancing web scraping with smarter crawling, fuzzy data extraction, automated spider generation, and intelligent QA.