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The article describes five common failure points in AI automations and shares practical patterns for avoiding issues like duplicate processing and silent errors, based on the author's experience with 30+ production systems.
The paper evaluates a configurable multi-agent system (nMAS) for extracting structured oncology data from fragmented clinical documents, achieving high performance compared to a baseline model.
The author is working on a Credit Underwriting AI Agent and is seeking open-source on-premise approaches to extract structured transaction tables from diverse Indian bank statement PDFs, facing challenges with layout variations and needing reliable extraction methods.
WaterCrawl is an open-source web application that crawls websites to transform extracted content into LLM-ready data structures, built with Python, Django, Scrapy, and Celery.
IBM launches Docling, an open-source Python library designed to convert documents into structured data.
This writeup describes how giving an AI agent typed tools per website, with extraction schemas derived and cached via an LLM, outperforms generic scrapers by making calls deterministic, cheaper, and more reliable, while grounding data in source HTML to prevent hallucinations.
This article presents a method to extract ideas from bloggers' content using AI tools and develop reusable skill workflows.
Rox uses Tavily's search and extraction services to power its AI agents for sales automation, significantly cutting down account research time from weeks to seconds. The case study highlights Tavily's reliability, cost-effectiveness, and accuracy in providing real-time data.
The article discusses challenges with browser agents in data extraction, such as incomplete tables, and introduces Thunderbit's solution that provides source URLs and reviewable tables for transparency and trust.
Mindcase is a tool launched on Product Hunt that enables quick data extraction from anywhere on the web.
Google's open-source tool 'langextract' uses LLMs to extract structured data from unstructured text with grounded character positions, crossing 37k GitHub stars.
This paper presents an automated method to extract quantitative techno-economic data from 76,000 energy system studies, compiling 3.2 million structured data points. The resulting FAIR database enables analysis of literature trends and provides input for energy models.
Introduces DECODEM, benchmark datasets for evaluating automated extraction of corporate governance variables from legal documents using large language models, showing high accuracy for many provisions.
Context.dev is a YC-backed API that allows developers and AI agents to scrape, crawl, and extract structured data from any website, with features like markdown, HTML, sitemaps, screenshots, and brand intelligence, aiming to simplify web data integration.
This article categorizes 14 web scraping tools into five groups: AI new paradigms, engineering-grade frameworks, browser automation, China-specific platforms, and modern lightweight tools, accompanied by real-world cases and selection recommendations.
Context.dev provides a single API for scraping, enriching, and extracting data from the internet.
Tweet de @ecommartinez que lista 10 repositorios de GitHub para hacer web scraping y extraer datos limpios de cualquier sitio web.
Datalab's balanced mode extraction achieves 95.9% accuracy in internal benchmarks, surpassing Reducto Deep Extract (95.1%) at less than half the price, with full verification including citations and reasoning.
Liquid AI releases LFM2.5-230M, a lightweight foundation model that runs on devices from cloud GPUs to CPUs and Raspberry Pi, with strong performance on tool use and data extraction tasks.
MinerU is a free, open-source tool that extracts text, tables, and equations from PDFs and scanned documents, supporting 109 languages and batch processing, saving hours of manual work.