Daily AI Recap: Mar 21, 2026

Welcome to today's curated briefing of the most important AI developments.

🗞️ Top Stories

Publisher pulls horror novel ‘Shy Girl’ over AI concerns: Hachette Book Group said it will not be publishing “Shy Girl” over concerns that artificial intelligence was used to generate the text....
Delve accused of misleading customers with ‘fake compliance’: An anonymous Substack post accuses compliance startup Delve of “falsely” convincing “hundreds of customers they were compliant” with privacy and security regulations....
Why Wall Street wasn’t won over by Nvidia’s big conference: Despite investor fears of an AI bubble, Nvidia's latest conference shows that most in the industry aren't concerned by that possibility....
PageIndex vs Traditional RAG: A Better Way to Build Document Chatbots: What if the way we build AI document chatbots today is flawed? Most systems use RAG. They split documents into chunks, create embeddings, and retrieve answers using similarity search. It works in demo...

New court filing reveals Pentagon told Anthropic the two sides were nearly aligned — a week after Trump declared the relationship kaput: Anthropic submitted two sworn declarations to a California federal court late Friday afternoon, pushing back on the Pentagon's assertion that the AI c...
A Gentle Introduction to Nonlinear Constrained Optimization with Piecewise Linear Approximations: Piecewise linear approximations are a practical way to handle nonlinear constrained models using LP/MIP solvers like Gurobi. The post A Gentle Introdu...
Escaping the SQL Jungle: Most data platforms don’t break overnight; they grow into complexity, query by query. Over time, business logic spreads across SQL scripts, dashboards...
A Coding Implementation to Build an Uncertainty-Aware LLM System with Confidence Estimation, Self-Evaluation, and Automatic Web Research: In this tutorial, we build an uncertainty-aware large language model system that not only generates answers but also estimates the confidence in those...
Safely Deploying ML Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing): Deploying a new machine learning model to production is one of the most critical stages of the ML lifecycle. Even if a model performs well on validati...