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.
Your team has AI tools. Now what?
The ground is shifting under every engineering team. Leadership bought the licenses. Everyone has Copilot. Maybe someone tried Cursor. But the gap between "we have AI tools" and actually shipping faster with them? Bigger than anyone wants to admit.
Most teams are stuck on autocomplete. Meanwhile the engineers who've figured this out are building in a week what used to take a quarter. The difference isn't the tool. It's how you work with it.
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.
Ten years of experience didn't stop being useful. You just apply it differently. Less typing, more thinking. Less grunt work, more building.
Honestly? It's the most fun we've had writing software in years. Your team should be having this much fun too.
Receipts.
Nick's numbers. Output tripled. Quality held. Staff Engineer, still shipping every day.
Nick built a production AI agent (the WorkOS CLI AI Installer) that configures and ships SDK integrations on its own, and maintained 20+ repos as a solo DX engineer using these same workflows. This is how we actually work — and what we want to show your team how to do.
What your team will learn
A baseline curriculum tailored to your team's stack, experience, and goals.
- 01
The AI Mindset
Before touching any tools — how to think about working with AI. What changes, what doesn't, and why your engineering experience matters more now, not less.
- From writing code to directing agents — mental models that work
- Addressing the fear and FUD head-on
- Where AI excels vs. where you still need a human in the loop
- Why senior engineers have an advantage, not a disadvantage
- 02
Context is Everything
The difference between "make this work" and "build this well" is the context you provide. Whirlwind tour of AI coding tools — we focus on whatever your team has access to.
- Prompt engineering for real codebases, not toy examples
- Structuring context so the agent builds what you actually want
- Hands-on with your team's tools (Claude Code, Copilot, Cursor, etc.)
- When to use chat, inline, and agentic modes
- 03
Build It Live
We build something complex together, in real-time. Not slides about best practices. Actual code, actual problems, actual shipping.
- Pick a real feature or project and build it start to finish
- Watch the workflow in action — how to direct, iterate, and course-correct
- Everyone codes along and builds the same thing with their own tools
- See what "fast" actually looks like when you're working with agents
- 04
Architecture-First Thinking
When code is cheap, design matters more. How to think about systems, tradeoffs, and the decisions that agents can't make for you.
- Designing systems when generating code costs nothing
- Tradeoff analysis and making decisions agents can't
- How to spec work so agents produce better output
- Avoiding the "it works but it's a mess" trap
- 05
Review, Verify, Ship
Reading code is the job now. How to evaluate what AI wrote, catch the subtle bugs it introduces, and test code you didn't write.
- Code review patterns for AI-generated code
- Common failure modes — what agents get wrong and how to spot it
- Testing strategies when you didn't write the implementation
- Building confidence in code you didn't type
- 06
Working in Parallel
Nick tripled his GitHub contributions in one year. Here's how to run multiple workstreams at once with agents doing the heavy lifting.
- Running multiple agents on different tasks simultaneously
- Managing context across parallel workstreams
- When to parallelize and when to focus
- Practical workflows for juggling projects without losing quality
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.
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Tool Selection & Evaluation
Cutting through the noise. Which tools actually work, which are hype, and how to evaluate them for your stack.
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Internal Conventions & Guardrails
Setting team standards for AI usage. Style guides, review policies, quality gates.
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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
We 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.
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Working Examples
The code your team built during the live session — real examples to reference, not throwaway demos.
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Team Conventions Template
A starting point for internal AI usage guidelines — review standards, quality gates, and tool recommendations.
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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
Calculate your training investment.
Priced based on team size with volume discounts for larger teams.
- → Volume discounts
Lower per-head rate for larger teams.
- → Follow-up sessions
Optional reinforcement after training.
- → Satisfaction guarantee
Your team ships with confidence or we make it right.
Training cost calculator
A follow-up session 30-60 days after training to reinforce learnings and address new questions.
Larger team? Let's talk — we'll put together custom pricing.
Schedule a consultationCommon questions.
What makes this different from AI/ML courses?
How long is the training?
What's the ideal team size?
Do we need AI experience?
What tools do you cover?
Can we customize the curriculum?
What's the investment?
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.