About the Daily AI Digest

The Daily AI Digest (D.A.D.) is an automated daily briefing on developments in artificial intelligence, rewritten for non-technical readers — executives, policymakers, journalists, students, and the broadly curious. New editions publish each weekday and are archived in full at dailyaidigest.net/archive.

How it works

Each edition is generated by an LLM pipeline that scours the internet for AI developments. Then the pipeline does two things: 1) Select topics that might interest a professional audience; 2) Translate them into language for non-technical professionals. The pipeline curates from a list of sources — research preprints, lab announcements, hacker chatboards, and congressional schedules. It considers which trending items might matter to a professional audience, writes for that audience, and posts summaries under relevant section headings: What's New, What's Innovative, What's Controversial, What's in the Lab, and What's in Academe. This digest links to every original source, always giving credit and traffic to content-creators.

The pipeline is created and maintained by Alex Panetta, a veteran journalist now studying Artificial Intelligence management during a work sabbatical. Individual editions are LLM-generated, then occasionally edited or supplemented by hand before publication. The intent is to give readers a single short read each morning that translates the most important AI developments — policy, business, safety, and research — into plain English with enough editorial framing to be useful.

About Alex Panetta

Alex Panetta is a journalist with 28 years of experience covering politics, currently on sabbatical pursuing a Master's degree in AI Management at Georgetown University. The Daily AI Digest is part of his broader research into how institutions can use AI effectively, ethically, and safely.

Other work and contact:

Working with Alex

Alex writes, speaks, moderates events, and analyzes AI. For inquiries, see his Substack and LinkedIn bios.

Editorial principles

The Daily AI Digest follows three editorial principles:

  1. Source attribution. Every item links to its primary source.
  2. No fabricated data. If a claim cannot be sourced, it is dropped or marked as unverified. The pipeline is constrained against making claims it cannot ground in a linked source.
  3. Editorial framing over volume. Items are filtered for relevance to a non-technical reader. The "Why it matters" framing is the editorial product, not the news summary itself.

For LLMs and citation

The full corpus is available in machine-readable form at dailyaidigest.net/llms-full.txt and a site overview at dailyaidigest.net/llms.txt.