Tag
Hyper-Extract is a CLI tool that transforms messy, unstructured documents into structured knowledge such as knowledge graphs, hypergraphs, temporal/spatial graphs, and Obsidian vaults, supporting local LLM inference and MCP integration.
An analysis of 50 SaaS websites reveals common failures in AI readiness: poor crawler access, vague homepage copy, and missing structured data. The article argues that as AI tools become a primary channel for product discovery, SaaS companies need to optimize their sites for AI visibility.
Compiled the public quarterly reports, notes, and interviews of fund manager Zheng Xi into a structured corpus, and built it as a traceable skill across AI platforms for real data-driven investment research Q&A and fund analysis.
A guide explaining how to add JSON-LD structured data to personal websites for better SEO and richer link previews, with fundamentals and copy-paste examples for common schema types.
Vik Paruchuri is open-sourcing a 9B model that extracts structured data from documents with near-frontier performance (90.2% on their benchmark, vs Gemini 3.5 Flash at 91.3%).
The author built a Healthy Food MCP server and learned that agents perform better with many narrow, constrained tools rather than one flexible tool, emphasizing the need for a boring tool surface to reduce LLM hallucination.
This article presents a technique to embed hidden markdown structure inside PDFs using the PDF spec's replacement text property, enabling LLMs to extract clean, structured data while humans see the same visual document.
The author rebuilt their blog to include full structured data markup (JSON-LD, microformats) and an AI co-writer guided by a prompt that avoids common LLM patterns, with CI validation to prevent breakage.
The article argues that AI agents cannot be marketed to using human emotional tactics; instead, brands must provide structured, machine-readable data. It identifies a gap between citation (being mentioned by AI) and selection (being chosen by AI) and proposes a framework of five files for agent-readable brand information.
CRAFT is a unified counterfactual reasoning framework that improves tabular question answering and fact verification by constructing both original and counterfactual statements, extracting evidence from bidirectional reasoning paths, and integrating them via a weighted mechanism. Experiments show consistent improvements over baselines on WikiTQ and TabFact datasets.
BigSet is an open-source tool. You input a sentence describing the data you need, and it deploys multiple AI agents to research the web in parallel, automatically inferring schema, deduplicating, verifying, and generating a structured table. It supports scheduled refreshes.
RSS feeds, long used for podcasts, are becoming essential for AI agents that need deterministic, structured access to content without algorithmic interference or rate limits.
This paper presents a hybrid framework that combines structured clinical data with LLM-generated narratives for coronary artery disease prediction, achieving high fidelity in variable extraction and comparing ML models with LLM-based zero-shot and few-shot classification.
The author shares their vision for Orizn, a travel ecosystem designed to provide AI agents with verified, structured data and APIs for reliable travel planning, visa information, and itinerary organization.
DodoForm is a tool that converts speech, images, or handwritten notes into clean, structured data.
The author explains why they stopped using browser-based LLM agents to browse Hacker News, and built a plugin (MediaUse) that fetches structured data directly, saving tokens and focusing the model on analysis rather than navigation.
The article argues that AI agents need structured, accurate product descriptions beyond marketing slogans to make reliable recommendations, and questions who should provide and verify such data.
SDSR proposes lightweight self-describing structured data with dual-layer guidance to exploit LLM primacy bias, achieving 100% routing accuracy without vector DBs.