TL;DR

All Day AI was a free, online, 16-hour marathon event with zero vendor pitches. We pulled it together in about two months, saw 12,313 participants and 140 speakers, and people stayed and watched for an average of more than 6 hours.

An event of this scale doesn’t come together that fast without some serious sprinting, trial and error, and audible-calling. Plenty of people have asked how we did it, so this article open-sources the whole playbook: the goals, the team, the tools, the timeline, and the parts we’d change.

The Why

We built All Day AI for people who were under pressure to put AI to work but struggling to find advice they could trust. The tools and recommendations were changing week to week, and a lot of the conversation still led back to something for sale. We wanted a place where practitioners could explain what they had actually shipped, what had failed, and what another team could reuse.

That need shaped the format. Keeping the event free and online meant people could join from wherever they worked, without a ticket budget, a flight, or a travel approval. Banning vendor pitches kept the focus on useful lessons rather than a sales conversation. Across Build, Lead, Secure, and Ship, the goal was to give people clearer answers and peers they could keep learning with after the day ended.

The Hard Part

The first version of our website went up in August, and the event was slated to go live on October 22. That gave us roughly two months to hit our admittedly aggressive goals:

• More than 10,000 registrants
• More than 120 practitioner-led sessions
• 4 tracks: Build, Lead, Secure, and Ship
• A surviving Discord community

What made those numbers hard was that we were starting from zero. There was no existing All Day AI community, no speakers signed up, no website, no event platform, and no social presence. Every registrant and every speaker had to come from something we built or someone we reached in those two months.

If you’ve run an online event with multiple speakers, you know that even one track can be a logistical nightmare. We knew 120+ speakers across 4 tracks could get ugly, fast, so most of what follows is about how we kept it from getting ugly: a committed team, a few deliberate bets, and a lot of help from AI agents.

The People

The most important part of the ADAI stack was the people. The core organizing team had more than 20 people across event co-founders, moderators, hackathon judges, ushers, and marketers, and they worked quickly and unselfishly through the build-up and especially on the day of. The team brought experience from organizations including Anaconda, Matejczyk Group, Carnegie Mellon University’s Software Engineering Institute, Techstrong Group, ACI Worldwide, and Community Hackathons, produced by Crowdbyte Solutions.

This was a community event, so all of it fell outside everyone’s day job. Nobody was paid to make All Day AI happen, and the fact that it happened anyway is a credit to how much the team believed in the project. They deserve not only credit but admiration.

Three Bets That Paid Off

Before we picked a single tool, we made a few decisions about what kind of event this would be. Those decisions shaped everything else, and in hindsight they did more for our numbers than any piece of software.

1. No vendor pitches, ever. Most conferences exist with at least a subtle motivation of money changing hands. People can sense that, and when they just want to learn, it’s a turnoff. So we banned pitches from the jump. That gave us a simple promise to make to attendees and speakers alike: you’re here to learn and to teach, not to be sold to.
2. Look like nothing else. Our goal for the website was to get visitors to understand right away that this wasn’t a normal tech conference. Our hallmark “Rockin’ Rooster” came out of a ~3 day back-and-forth with the team, where other ideas like “Rockin’ Grandma” were rejected in its favor. We felt the rooster represented something new, loud, and disruptive, in a good way. I used a free online SVG converter to turn the original crude cartoon into a scalable logo.We carried the same idea into the speaker pages. Every headshot got a uniform background and photo effect inspired by vintage concert posters. I worked out the look in ChatGPT, then built a small app in Kilo Code that applied it programmatically through the OpenAI image API, so every new speaker matched the rest without manual editing. It wasn’t long before people recognized the rooster before they even knew what the event was about, which is proof the branding landed.
3. Trust the community to spread the word. This was our biggest growth lever. On a random weekday in September, I had the idea to ask my Dot (an AI agent I’ll explain below) to reach out to 3,000 tech groups, hackathons, and events around the world: anyone who might have a shred of interest in All Day AI. I proposed a free deal. Tell your community about our event, and I’ll put your logo, link, and CTA on our website. Hundreds responded. Based on rough estimates of community sizes, I would safely attribute 20-30% of our registrations to that one message. It worked because the offer cost nobody anything, and because a free, pitch-free event is something community organizers are happy to share.

The Stack

With those bets in place, the job became execution, and the tools we chose were all about saving the team’s time for the work only people could do.

Platform

We wanted to spend as little time and money as possible on event infrastructure, so we looked for a platform that could natively handle 5 stages, registration forms, communications, and recording. We landed on Zuddl, which was modular enough to meet our requirements without us stitching together separate tools.

Website

Version 1 was built in Kilo Code for VS Code using GPT 5.6 and cost about $5. It was stored in GitHub, deployed to Vercel, and used Cloudflare for domain management. Because the speaker lineup and partner list changed constantly, the site had to change constantly too. The team and I made PRs as the event evolved, and I approved and merged them.

Operations

Here’s where we had to get crafty. We wanted to attract top speakers across the AI industry, and quality speakers want a clear point of contact with a name and a quick response time. A shared, generic email address wasn’t going to cut it, so we used mine.

That solved the trust problem and created a volume problem: one person fielding 140 speakers’ worth of email. To manage it, I used a series of AI agents with a human-in-the-loop editorial process.

• OpenAI Dots. My Dot was the operating system for speaker communicatio