The AI-Native Engineer
You're not being replaced. You're being promoted.
Live training that turns strong engineers into better ones. Your team learns to ship faster with AI, not get replaced by it.
Stop typing. Start directing. Watch what your team can actually ship.
// remote or on-site · 3-15 engineers
Your Team Has AI Tools. Now What?
Leadership bought the licenses. Everyone has Copilot. Maybe someone tried Cursor. But research shows experienced developers are actually 19% slower when they bolt AI onto existing workflows, while believing they got 24% faster. The gap between "we have AI tools" and shipping faster with them is bigger than anyone wants to admit.
That's the J-curve. Adding AI to old workflows makes you worse before it makes you better. The breakthrough doesn't come from better tools. It comes from redesigning how your team works.
The Job Changed. The Craft Didn't.
AI writes the code now. So what do you do? The same things that always mattered: decide what to build, design how it fits together, and make sure it actually works. That's engineering.
The bottleneck isn't implementation speed anymore. It's how precisely you can describe what should exist. Your ten years of experience? That's the advantage. You know what good looks like. That judgment is exactly what AI doesn't have.
Honestly? It's the most fun I've had writing software in years. Your team should be having this much fun too.
GitHub contributions in one year
Lines of code written by hand in 6 months
Repos maintained solo with AI workflows
Output tripled. Quality held. Staff Engineer, still shipping every day.
I built a production AI agent that configures and ships SDK integrations on its own. I maintained 20+ repos as a solo DX engineer using these same workflows. This is how I actually work. I want to show your team how to do it too.
What Your Team Will Learn
Five modules that take your team from autocomplete to agentic engineering, tailored to your stack, your tools, and your actual codebase.
The Mindset Shift
Where is your team today? We start with an honest self-assessment, then show, with a live side-by-side demo, what changes when you stop typing and start directing.
- The 5 Levels of AI Coding: give your team a shared vocabulary
- Why experienced engineers have an advantage, not a disadvantage
- The J-curve: why bolting AI onto old workflows makes you slower first
- For leaders: spec quality is the new bottleneck, not implementation speed
Context Engineering
Agents are only as smart as the environment they operate in. Your team learns to optimize their codebase for machines, not just humans, and audits their own repo live.
- The right amount of context: not more, not less
- Pull tribal knowledge out of Slack and into structured files
- Agent instructions that actually work (and the antipatterns that don’t)
- Ruthless consistency: agents magnify every inconsistency in your codebase
Build It Live
The centerpiece of the workshop. We pick a real problem and build it start to finish using agentic workflows. Not slides about best practices. Actual code, actual shipping.
- Direct, iterate, and course-correct in real time
- Everyone codes along with their own tools (Claude Code, Cursor, Copilot, etc.)
- Build agent tooling that removes friction so the agent is faster next time
- See what "fast" actually looks like when you’re working with agents
Review, Validate, Ship
Reading code is the job now. Your team learns to build automated trust: mechanical guardrails, self-validating agents, and a testing paradigm shift that changes how you think about quality.
- Code review patterns for AI-generated code
- Enforce architecture mechanically with linters and structural tests
- Agents that validate their own work before you ever look
- Scenarios vs. Tests: why external specs beat internal test suites when AI writes both
Working at Scale
How to work differently starting Monday. Run parallel workstreams, build compound returns into your codebase, and keep AI-generated code from turning into unmaintainable slop.
- Running multiple agents on different tasks simultaneously
- Compound engineering: every automation makes the agent faster tomorrow
- Entropy management: background validation that catches drift early
- The generalist advantage: systems thinking matters at every experience level
For Engineering Leaders
You got the mandate: adopt AI. But what does that actually mean for your team? How do you measure it? What does "good" look like? This isn't just training for ICs.
We cover how to think about AI adoption as a leader: setting expectations, measuring impact, building conventions that stick, and knowing what to watch out for.
Team adopts new workflows. You see AI usage shift from autocomplete to agentic coding. PR velocity starts climbing.
Engineers are self-sufficient with AI tools. Internal conventions are established. The team has a shared language for how they work with AI.
Shipping velocity is measurably higher. Engineers are tackling projects they would’ve deprioritized before. The team wonders how they worked without this.
Go Deeper Where You Need It
Everyone gets the core modules. These are examples of add-ons we've built before, or we can create something new based on what your team actually needs.
Tool Selection & Evaluation
Cutting through the noise. Which tools actually work, which are hype, and how to evaluate them for your stack.
Internal Conventions & Guardrails
Setting team standards for AI usage. Style guides, review policies, quality gates.
Something Else Entirely
Have a specific challenge? We’ll scope a custom module together on the intro call.
How It Works
Live, Not Pre-Recorded
Real-time instruction with Q&A and pair programming. Remote or on-site, whatever works for your team.
Your Stack, Your Problems
I adapt the curriculum to your languages, frameworks, and the projects you’re actually working on.
Flexible Format
One intensive day, a series of sessions over a week, or something in between. We scope it on the intro call.
What Your Team Walks Away With
AI Workflow Playbook
A written guide covering the workflows, patterns, and prompts covered in training, tailored to your stack.
Working Examples
The code your team built during the live session. Real examples to reference, not throwaway demos.
Team Conventions Template
A starting point for internal AI usage guidelines: review standards, quality gates, and tool recommendations.
Session Recording
Full recording of the training for team members who couldn’t attend or anyone who wants a refresher.
Optional Follow-Up Session
30-60 days after training, we regroup. Address new questions, reinforce what stuck, and troubleshoot what didn’t.
Meet Your Instructor
I'm a Staff Engineer. I haven't written a line of code by hand in six months and my output has never been higher. I built the WorkOS CLI AI Installer (a production agent that ships SDK integrations on its own), I've given 50+ conference talks, and I was a host on JS Party.
Is This Right for Your Team?
- Teams of 3-50 engineers shipping a product
- Organizations where leadership said "use AI" but nobody explained how
- Engineers using Copilot for autocomplete but not much else
- Eng managers who need to measure and report on AI adoption
- Looking for a course on building AI/ML models or agents
- Non-technical teams or individuals looking for 1:1 coaching
- Teams already shipping with agentic workflows daily
Common Questions
What makes this different from AI/ML courses?
We don’t build AI models. We teach engineers how to use AI to ship software faster. No RAG pipelines, no fine-tuning. Just practical workflows you’ll use on Monday.
How long is the training?
Depends on your team. Could be a day, could be a week. We figure out the right scope together on the intro call.
What’s the ideal team size?
3-15 engineers. Small enough that everyone gets hands-on time, big enough for good discussion.
Do we need AI experience?
Nope. You need to be a solid engineer. That’s the prerequisite. We handle the AI part.
What tools do you cover?
Claude Code, Claude Desktop, Cursor, GitHub Copilot, and others. Every team’s toolset is different, so we’ll focus on what your company already has access to and what makes sense for your stack.
Can we customize the curriculum?
That’s the whole idea. The five core modules are the baseline. We go deeper wherever your team needs it.
What's the investment?
There's a pricing calculator on this page for a ballpark. Exact numbers depend on team size and what we cover. Let's talk about it on a call.
Let's Talk About Your Team
30 minutes. No pitch deck. We'll figure out if this is a good fit and what your team actually needs.