
Most Americans don't trust AI. It's proven that it doesn't know what safe toppings for pizza are. People don't even want to listen to AI music. But none of that matters for some of America's wealthy, who are turning to AI to teach their kids instead of traditional schools. Companies like Forge Prep and Alpha School are charging families tens of thousands of dollars to turn their kids into beta testers for AI tutors and "interactive project-based workshops." Unsurprisingly, Sili
Will AI-focused private K-12 schools face formal regulatory scrutiny in any US state by January 2027?
Resolves by Jan 1, 2027
Some wealthy Americans are enrolling their children in AI-powered private schools that charge tens of thousands of dollars annually, using the students as beta testers for unproven AI tutoring technology and interactive workshops. These schools, which operate from kindergarten through high school in some locations, are being adopted particularly by Silicon Valley venture capitalists who believe traditional education is broken and see AI as a solution. However, the schools do not publicly share performance metrics to demonstrate whether AI-guided education actually improves student outcomes, and there are concerns about curriculum decisions such as excluding certain social and historical topics from the classroom.

LlamaIndex has published legal-kb, a public reference application on GitHub. It is described as a knowledge base for legal documents, powered by LlamaIndex Index v2 (the LlamaParse Platform). The project demonstrates a pattern the team calls a Retrieval Harness for agentic retrieval. The approach differs from single-shot retrieval. Instead of one embedding search per query, an agent is given filesystem-style tools. It can then crawl a large, evolving knowledge base to solve a task. The tools

Most enterprise data still sits inside PDFs, scans, and slide decks. Large language models and agents cannot use that data until it becomes structured JSON. Open-source document extraction has become the standard way to do that conversion on your own hardware. Two different problems hide under the phrase ‘PDF to JSON.’ The first is schema-driven extraction: you define fields, and a model fills them with values. The second is document parsing: a model reconstructs the page into st
Junyang Lin was the technical lead of Alibaba’s Qwen project. He announced he was stepping down on March 3, 2026. He now lists himself as an independent researcher on his personal site. In a talk titled ‘Qwen: Towards a Generalist Model / Agent,‘ he walks through the Qwen family. It ends on a single line: “Training models -> training agents.” He later expanded that line into an detailed post as an independent researcher. This article reads the talk and the detai
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