Fine-tuning is expensive and stale. The fastest path to personalized AI is injecting the right web context at query time. Here's how to build it.
Coding agents hallucinate outdated APIs because they rely on training data. Give them real-time access to the latest docs — indexed from the actual documentation site.
A practical guide to building an automated competitive intelligence pipeline — scraping competitor websites, extracting pricing and product changes, and getting alerted instantly.
Support agents that only know your FAQ hallucinate. Support agents that extract and search your entire documentation site answer correctly — every time.
Build an open-source deep research agent in Python and Node.js. Search sources, scrape top results, synthesize a cited report. Cheaper than Perplexity's $5/1000 queries.
How to build an AI-powered e-commerce data pipeline — extracting products, prices, and reviews from any website, structuring the data, and making it searchable.
Replace keyword search with semantic product search — customers find what they're looking for even when they don't know the product name. Here's how to build it.
How to build an AI-powered job market intelligence platform — extracting job postings, analyzing hiring trends, identifying skill demands, and tracking company growth signals.
Using a 1M-token context window for every query is expensive. Web extraction + RAG delivers the same quality at a fraction of the cost. Here's the math.
Build an AI news monitoring system that tracks specific topics, extracts articles from multiple sources, and enables semantic search — using web extraction APIs and vector embeddings.
Build an AI-powered price monitoring system that tracks competitor pricing in real time and sends intelligent alerts — using web scraping APIs and webhooks.
Static user profiles go stale. Build AI agents that enrich user context with live web data — company news, product launches, hiring signals, competitive moves.
Build high-quality LLM fine-tuning datasets from web content. Full Python pipeline: crawl with KnowledgeSDK, filter, deduplicate, and export as JSONL for OpenAI and HuggingFace.
How to use web scraping APIs to collect, clean, and structure training data for LLM fine-tuning — with quality filtering, deduplication, and licensing considerations.
Solve the stale knowledge problem: build a pipeline that scrapes URLs weekly, diffs against previous versions, updates your vector store, and notifies your app.
Full tutorial: scrape competitor pricing pages, detect changes with webhooks, extract new prices, and send Slack alerts with before/after diffs.
Build a production-grade e-commerce price monitoring agent: scrape JS-rendered prices, store history in Postgres, trigger webhooks on price drops.
Build a financial monitoring agent that scrapes IR pages, earnings press releases, and public filings to alert on new disclosures and extract key metrics.
Scrape competitor job boards to understand their hiring plans, detect new AI teams forming, and get a weekly digest of competitive intelligence from job posts.
Build a lead enrichment pipeline that scrapes company websites, extracts structured data—description, pricing, tech stack—and feeds it directly into your CRM.
Build an AI news aggregator that scrapes any tech site, categorizes articles semantically, deduplicates stories, and delivers a daily brief—no RSS required.
Build a multi-step research agent using LangChain and KnowledgeSDK that takes a question, scrapes sources, searches semantically, and synthesizes answers with citations.
Build a competitor pricing monitor with webhooks in 50 lines of code. Full tutorial: scrape baseline, subscribe to changes, receive structured diffs, trigger Slack alerts.