wvlen
pronounced “wavelength”
I build AI fluency by working beside people on their actual work.
I work beside your people, each on one of their real problems, until building with AI is how your team works. They leave knowing how to operate the tools and workflows they built themselves, and bring those capabilities to all their future work.
Get your team buildingScrollI need my team to adopt AI in their work, and I don't know how to make it happen.
You've probably already done the obvious things. You've purchased licenses, scheduled trainings, and appointed AI champions, but the work still looks the way it did before, except for a few people who quietly got it.
Access is not adoption or impact. What's missing is each person building something real of their own, in the context of the actual work, guided by someone who has done it before.
Everyone here has an AI license and almost nobody is building anything with it.
Access is not adoption. Each person brings a real problem, and the people who get it become the ones who can help teach others.
Our whole process lives in a Google Sheet and everyone knows it's held together with duct tape.
Migrating to a real tool has been “next quarter” for a year. Instead, the person living in that sheet builds the replacement themselves over a handful of sessions.
We've paid this vendor for years and the tool still doesn't do the thing we bought it for.
A surprising share of niche software is now replaceable by a tool your team builds around its exact process, often for less than a year of the subscription.
I've watched people do incredible things with AI and I can't get it to do the one thing I actually need.
The gap is almost never effort. It's the flip from asking AI to do the task to building the tools and processes that do it. We work on their real problems until that clicks.
Step 1
We map each person's actual job, name what better looks like, and choose the one high-leverage problem worth solving.
Step 2
We turn each person's conversation into a plan. Then we get your people access, connections, and environments ready to build.
Step 3
They drive on their screens, in your environment, with your real data. I steer the moments that matter until we have a working first version.
Step 4
Your team uses what we built, finds the edge of what they can do alone, and comes back to expand it, productionize it, or start something new.
Teams and functions · scoped to the group
We find each person's real problem, then build in live sessions where they drive and I guide. What one person figures out becomes a pattern the next person starts from, so the team's fluency compounds.
Your team comes away with working tools and workflows, and the people who know how to build the next ones.
Nobody learns a new way of working from a recorded demo. Your people's hands are on the keyboard the entire time. I'm beside them to say “stop, ask it like this instead” or “why don't we try this?”
The hard part is not finding the right prompt. It is articulating what you actually want and how you will know it is right. We start by identifying the actual goal and purpose of the work, then work from there to a solution we can validate.
One person, one real problem, until it clicks. I've tried many other methods of teaching AI fluency and this one gets results.
The measure of success is your team's ability to continue working independently. I help establish a solid platform for your people to build on. When the next idea comes, they'll know how to build it.
In just a few hours, Dan completely changed how I work with AI. I went from only trusting the technology for trivial tasks to running many parallel agents at once, relying on it for all of my day to day coding work. He taught me how to plan with, provide effective feedback for and invest in my agent.
I worked with Dan at Block, where we co-hosted AI office hours for the company. Before those sessions, I had no real path into agentic coding -- it felt out of reach. Dan taught me one-on-one how to set up and actually use the tools, and now it's part of how I work every day. What stuck with me most, though, was watching him do the same for designer after designer. He had a gift for unlocking creativity and potential in people across the whole company, patiently and without ego. He elevated our entire design team through his mentorship, and that impact has far outlasted any single session. Anyone who gets to work with Dan is lucky.
[Dan will] perform magic in front of you and then show you how to do it. He's a fantastic teacher and will meet you wherever you are at. If you have any interest in improving you or your team's ability to leverage AI in a meaningful way, he's the coach you want. Not just a demo, or the latest hot new skill, but solving whatever weird problems you have (where being right matters). If you want confidence and reliability in the tools you are building, he will get you there. And he will leave you with the ability and wisdom to keep building. You will not regret working with him. I promise.
Dan came into the project without deep content expertise, and he worked hard to understand the full scope and nature of the problem we were trying to solve. His strength is finding responsive, creative technical solutions using software development, machine learning, and AI, and he helped us create a workflow to conduct analysis at scale in a highly secure environment, something that simply was not possible before in our work. He worked with our non-technical team collaboratively, with a great deal of patience and curiosity, and helped us understand our own research questions in a new way.
I have found tremendous educational value in working with Dan on several projects across disciplines. He models a unique ability to elicit, frame, and target the core features of any problem. Dan taught me to build such a skillset and use it, with the help of AI tools, to find elegant and rigorously validated solutions using powerful computational approaches outside my domain of expertise.
Dan is truly incredible. I spent a few two-hour sessions with Dan and he was able to teach me AI all while learning how to apply it to a real problem that was super abstract. The real value is in the one-on-one, hands-on teaching style that creates a premier experiential learning experience. No lazy PowerPoints or generic discussion. He jumps right into the problems and you can tell he’s extremely passionate about his work. Can’t possibly recommend him enough.
Dan is technically exceptional, thoughtful, and curious when it comes to solving AI problems. I’ve been working with Dan on a project for my startup and have been impressed with his knowledge in AI.
I've spent more than a decade as a software engineer and thousands of hours working with AI. That depth lets me show you what is actually possible for your situation, not just the few ideas already floating around you.
I've built software and product inside companies operating at scale, with challenging constraints. I'm tool-agnostic, with nothing to sell you but the work, and I know how to slow down and make the technical parts make sense.
And I've taught this way of working to researchers, designers, operations professionals, program leaders, and engineers, from people writing their first line of code with an agent to a CTO sharpening how they already build. Meeting you where you are is a skill of its own, and I've had a lot of practice.
Productionized AI workflows for disputes intelligence and restaurant menu extraction, built internal LLM tooling, and coached teams on coding-agent workflows.
Built micromobility systems at Uber app scale, including search, aggregation, IoT firmware rollouts, payments, and fleet-management services.
Built member-support systems across chatbot, voice, and email workflows, with a focus on reliability, performance, and platform architecture.
Built prototypes that brought the best of open source to enterprise, turning emerging tools and ideas into proofs-of-concept that clients could run with.
Real people, real work, and the kinds of things that become possible when domain knowledge meets hands-on technical guidance.
Letting the biology define the statistical methods, then using coding agents to move quickly from scientific questions to working analysis.
Read the case studyMentoring a designer with no coding experience from fundamentals to independently building complex applications, including a personal agent they run themselves.
Read the case studyPairing with a classically trained CTO on their real work: planning with an agent, implementing, and reviewing a PR together.
Read the case studyWhether it's a workflow that is slowing you down or a team that has the licenses and none of the practice, tell me a little about your work.