Status: private beta. AILunchroom.com is in active development with around 50 private-beta users. The figures below are current build scope — 49 practice modules, 158 hands-on labs, across 10 departments — not a finished-product claim.
Why I built it
AILunchroom.com started from a simple frustration: a lot of AI training sounds polished in a presentation and then falls apart when someone tries to use it at work. I wanted a product that treats AI practice like a real work session, not a lecture.
Product thesis
People learn faster when the examples feel close to their actual job. AILunchroom uses realistic prompts, guided labs, role-aware paths, and exercises that leave the user with a concrete takeaway.
How it works
- Practice tied to real work. The 49 modules and 158 hands-on labs are organized around the jobs people actually do, across 10 departments, rather than generic "intro to AI" material.
- An intent-based knowledgebase. Instead of a glossary, learners pick the question closest to what they need and move straight into relevant practice.
- Concept guides that connect to practice. Plain-English explanations link directly to the labs where the idea gets used, so understanding and doing stay together.
- A usable takeaway. Each lab is built to leave the learner with a prompt and a final output they can bring back to their own work.
My role
- Product concept and positioning.
- Training structure, lab flow, and prompt design.
- Frontend implementation and deployment.
- Ongoing copy, curriculum, UX, and operating decisions.
Design choices
The product is for people who are curious about AI but not trying to become AI specialists. That shapes the interface: clear steps, visible outputs, less jargon, and prompts that sound like workplace requests rather than platform instructions.
Operating boundaries
The product has to be careful with privacy, user expectations, and overpromising. AI practice can be powerful, but the product should still make room for review, judgment, and the limits of model output.
Related
See Practical AI Implementation for the broader adoption frame, and ITLunchroom.com — the security-and-tech sibling — for the same hands-on approach applied to everyday workplace tech and security.
Flight log
- 2026 Q1Build starts. The trigger: executives racing to deploy AI while frontline teams — the widest spread of skill levels — had no real path in. Training only the top felt unfair.
- 2026 Q1Department-first curriculum locked in. The core context is the job being done — blanket training dilutes what each team actually needs, and maintenance does not need healthcare examples.
- 2026 Q1Killed in-site AI integration early. The training has to pay off for any organization, whatever tool set they already run.
- 2026-04First private-beta users arrive — friends, colleagues, and public outreach on Reddit and social.
- nowFree with a login, deliberately — individual practice stays free as the funnel, while organization contracts unlock deeper department-level personalization.