Be the most dangerous developer in the room.
AI solved the easy half of your job, now what?
Using AI and using it well are not the same thing
You're shipping faster than ever. AI writes the boilerplate, scaffolds the features, and handles the patterns you used to type out by hand.
It's not just you, 84% of developers use AI tools regularly now, and the ones who pushed early are getting more done with less effort.
But AI doesn't level the playing field. It tilts it.
The developer who knows how to feed AI the right context and build systems around it now has a brutal advantage, because AI amplifies whatever you bring to it.
Strong engineering instincts scale.
Weak ones scale too...
but in the wrong direction.
There's a growing gap between developers who use AI and developers who've built the systems to make it work at their level.
Ships a full feature in a day with AI handling the implementation
Same tools, half the time fighting hallucinations and re-prompting from scratch
- No context files that teach AI your codebase
- No guardrails that catch mistakes before they ship
- No proper rules, hooks, or MCPs that make the output production-ready
The tools are there, you just haven't built the system around them yet.
This gap isn't new. Every time software added a new layer of abstraction, the same thing happened.
Every abstractionin software was once someone else's job
Assembly gave way to compilers, compilers gave way to frameworks. Each layer automated work that developers used to do by hand, and every time, the same argument started about whether the old skills still mattered.
They did. But the job changed around them, and the developers who figured out each new layer fast became the ones everyone else relied on.
Punch Cards
“We wrote in 1s and 0s”
Assembly
“We named the operations”
High-Level Languages
“We described what to do”
Frameworks & OSS
“We composed what others built”
AI
“We direct the machine”
“We wrote in 1s and 0s”
“We named the operations”
“We described what to do”
“We composed what others built”
“We direct the machine”
AI is the current layer. It won't be the last.
But this one is different. Previous layers automated tedious work. AI automates the part that felt like your job. The output looks like something a developer wrote.
That changes what you need to be good at, and what you can finally let go of.
Worth unlearning
- The reflex to write every character yourself
- Prompting from scratch instead of building systems that compound
- Measuring value by lines of code, not decisions made
- Prompting without leveraging your technical edge
Never unlearn
- Knowing when to trust AI and when to take over
- Architecture decisions that hold up when AI writes the code
- Knowing that what we feed AI matters more than how we prompt it
- How the system fits together, what runs where, what breaks
That all sounds reasonable on paper. But it hits different when you see it in your own code.
Let's play a game
Here's a simple permissions check AI generated. Six lines. It compiles, it passes linting, and it looks production-ready. See what you catch.
HoverTap the underlined code to see why each one matters.
Six lines of code, three issues. A real PR has hundreds of decisions just like these.
Every one of them is context the model didn't have. You do.
And catching bad code is just the beginning. The same kind of judgment matters when you're scoping features, steering AI through an implementation, or building the guardrails that prevent these issues in the first place.
$299 $219/yr · 30-day full refund
You learn something actionable every 15 minutes you spend here.
Went through the Unlearn material and it's easily the best I've seen on architectural thinking in an AI workflow. Really good stuff.
That's why we built unlearn
Most AI education teaches you how to use a tool.
We teach you how to become the developer nobody wants to compete with.
- Makes the architectural calls AI will never be qualified to make
- Ships in a day what used to take a sprint
- Builds systems that make every output production-grade
Unlearn is a living platform, not a course you finish and forget. Workflows, courses, workshops, and tools that evolve together, shaped by what 30,000 developers told us and built to keep up with how fast AI moves.
AI changes fast. You can have a system that changes with it, or you can keep figuring it out alone.
The skills that make you essential
The tools that make you resourceful
Tools you plug into your editor today, updated the same week a new model ships.
Designed to work with your stack and tools
Frameworks & Technologies
AI Coding Tools
Your full build cycle, covered
We're launching with 8 core workflows, each one built for how the job actually works now that AI writes the code.
You watch a short lesson, grab a prompt or an MCP server or a skill file, and use it on your actual project that same day. Fifteen minutes from learning something to shipping with it.
Spec a Product Before AI Builds It
AI knocked down the walls. You can build a SaaS, ship a mobile app, or automate an entire backend without needing a full team behind you. But the developers who actually finish what they start are the ones who plan before they prompt. So you set constraints, define what AI is NOT allowed to build, and use AI itself to poke holes in your plan before writing a single line of code.
From vague idea to actionable product plan
- Why Planning Beats Vibe Coding (4m)
- Turn a Vague Idea Into a Structured Spec (6m)
- Shaping the Specification (2m)
- Give AI Technical Constraints from Day One (3m)
- Tell AI What It's Not Allowed to Build (2m)
- Use AI Mockups to Expose Gaps in Your Spec (2m)
- Have AI Poke Holes in Your Plan (2m)
- What Might We Have Missed? (2m)
- From Spec to Features (2m)
- Scaffolding the Boilerplate (5m)
Design a Feature AI Can Execute Without Guessing
AI is fast at building but terrible at deciding what to build, so the precision of your instructions determines everything. You break a feature down into tasks so clear that AI has nothing to interpret, with acceptance criteria it actually follows and a sequence that keeps scope from creeping into places you never intended.
From specification to feature-ready plan
- From Specification to Feature Plan (3m)
- Decompose Features Into Tasks AI Can Handle (4m)
- Write Acceptance Criteria AI Will Actually Follow (3m)
- Estimate Effort and Flag Risks Early (4m)
- Sequence Tasks for Incremental Delivery (3m)
- Create the Implementation Checklist (4m)
- Validate the Plan Before Handing Off to AI (3m)
Build Features with an AI Agent
Most developers hand the agent a prompt and hope it figures things out. The ones shipping clean code every day have a loop instead. Feed context before you ask, let the agent take a first pass, review without rewriting everything, then handle edge cases and write tests together before going from branch to production.
From plan to production-ready code with AI agents
- The AI Implementation Loop (5m)
- Set Up Your Agent Environment (8m)
- Feed Context Before You Prompt (6m)
- First Pass: Let the Agent Build (10m)
- Review What the Agent Wrote (8m)
- Iterate: Refine Without Starting Over (7m)
- Handle Edge Cases and Error Paths (10m)
- Write Tests With the Agent (12m)
- Integration and Regression Checks (8m)
- Clean Up Before Commit (6m)
- Document What Changed and Why (5m)
- Ship It: From Branch to Production (10m)
Reviewing AI-Written Code
AI-generated code looks clean, passes lint, passes tests, and then breaks in production because nobody traced through the logic that only fails under real conditions. This is where you build the review instincts of a senior engineer, catching the patterns that no linter or test suite will flag for you.
- Why AI Code Fails (and Where to Always Look First) (8m)
- Structure a Code Review So Nothing Slips Through (9m)
- Break Code Before Users Do (9m)
- Reverse-Engineer Code You Didn't Write (8m)
- Decide What Not to Review (5m)
- Identify Dangerous Side Effects (5m)
- AI Code Security: The Five Checks That Matter (5m)
- AI-Generated Tests: When Green Doesn't Mean Good (11m)
- Reviewing AI's Dependency Choices (6m)
- Automate Your PR Reviews (6m)
- Turn Reviews Into Better Future Output (5m)
- Close the Loop: One PR, Every Technique (17m)
Merging and Deploying
The tests pass and the PR looks right, but there's still that feeling in your gut because you didn't write every line yourself. The gap between "it looks correct" and "I'd bet production on it" is real, and closing it takes a deploy process built specifically for code you directed but didn't type.
Debugging and Performance
Something breaks at 2am and half the codebase was AI-generated, which means you can't grep your own memory for context that was never yours. So you turn AI into your debugging partner instead, tracing through its own output, isolating the root cause, and fixing it without making things worse.
Refactoring with an AI Agent
Refactoring is where AI gets dangerous, because one wrong instruction and the agent rewrites half your codebase with changes nobody asked for. You keep refactors incremental and test-covered at every step, so you actually improve architecture instead of gambling on whatever the agent decides to touch.
Getting Up to Speed on Any Codebase
New job, new repo, 200k lines of code and zero documentation to go with it. AI can map the whole thing faster than any onboarding doc ever could, but only if you know how to ask the right questions. You turn it into a codebase guide that builds your mental model, surfaces the entry points, and gets you making safe changes in days instead of weeks.
Every workflow ships with
$299 $219/yr · 30-day full refund
These eight are the starting line. We're building in parallel to cover more of the AI spectrum, and the developer stays at the center of all of it.
Five courses, and they're all yours from day one
We've prepared 5 full courses alongside the workflows. Which tools to actually commit to, how to hand off work to AI without losing control, and how to trust your own review when the code isn't yours.
The Future Proof Dev
Most developers installed Copilot, accepted a few suggestions, and called it a day. But AI coding tools go way deeper than autocomplete, and the gap between using them casually and using them well is where most of the value lives. You build a real mental model of what these tools can and can't do, so you stop guessing and start directing.
Mastering Reusable AI Workflows
You figured out a good prompting pattern for code review. Then you did it again for bug fixing. Then documentation. At some point you realize you're rebuilding the same process from scratch every time. This course teaches you to turn those patterns into reusable agents that run on autopilot, connected to GitHub Actions and your actual error monitoring.
Optimizing Productivity with AI Tools and Agents
There are dozens of AI coding tools and every week someone launches a new one. The real question isn't which tool is best, it's which combination actually fits how you work. You learn to pick the right editor, the right agent, configure rule files and MCP servers, run agents in parallel, and build a personal setup that compounds over time instead of fighting you.
RAG for Real-World AI Applications
Every developer wants to connect AI to their own data, but most RAG tutorials stop at "put your docs in a vector database and query them". The real work is chunking strategies, embedding model selection, reranking, evaluation, and knowing when your pipeline is actually returning good results versus confident garbage. You build a production RAG system from scratch and learn to measure whether it works.
AI for UI Design and Automated Testing
You can ask AI to build a component, but what you get back usually looks like every other AI-generated interface on the internet. This course is about using AI as a real design partner, from generating image assets and brainstorming layouts to building animated hero sections and creating spritesheets, all while keeping your brand intact and the code maintainable.
Most developers installed Copilot, accepted a few suggestions, and called it a day. But AI coding tools go way deeper than autocomplete, and the gap between using them casually and using them well is where most of the value lives. You build a real mental model of what these tools can and can't do, so you stop guessing and start directing.
New courses and workflows ship every week.
Your path, not someone else's
Answer a few questions about how you work. We'll show you exactly what Unlearn looks like for you.
Your primary stack?
AI coding tool you use?
Biggest challenge right now?
Your recommended path
The bottleneck isn't Claude. It's the workflow around it.
Claude Code is one of the highest-leverage tools in React / Next.js development right now. This path tightens the loop — from MCP-powered planning through automated deployment checks — so every session compounds.
Your workflow path
Use CLAUDE.md and slash commands to write specs Claude executes reliably.
Chain Claude commands in small, safe PRs with test coverage at each step.
Use MCP servers to validate CI and automate pre-merge checks.
Use CLAUDE.md and slash commands to write specs Claude executes reliably.
Chain Claude commands in small, safe PRs with test coverage at each step.
Use MCP servers to validate CI and automate pre-merge checks.
A taste of where we're headed.
Early access members experience it first.
I've mass-ignored everything out there on AI for developers because it all looks great in a demo and falls apart on anything real. But Unlearn actually changed how I build things.
Become dangerous
AI raised the bar.
It's time to sharpen your technical edge and make AI your multiplier
$299/yr
$219/yr
The only subscription a developer needs for the AI era, and whatever comes after it.
conventions
If this doesn't change how you ship, you get every cent back within 30 days.
Cancel anytime.
Get your whole team on one system, not six different ways of using AI.
- Team management and seat administration
- Custom workflow builder for your stack and
conventions - Shared context files, guardrails, and skill files across the team
- Volume discounts that grow with every seat
- Priority onboarding and dedicated support
- Centralised billing
- Customisable invoice
Use the toggle above to switch between Individual and Team pricing.
Have questions, want to lock in this price while you wait for approval, or need a tailored training track? Contact us
You already know AI isn't the hard part
- →You ship production code and you're responsible for what goes out the door
- →You've outgrown random tips and tricks and want a system you can rely on
- →You care about understanding what you ship, not just shipping it
- ×You want to hand everything to AI and approve whatever comes back
- ×You're not building production software
- ×You think AI replaces the need to be a good developer
Not for everyone. That's the point.
We can make you two promises
Your price. Permanently.
This is the lowest price Unlearn will ever be sold at. As an early access member, your price stays for as long as you do.
It's an early access rate, not a discount.
We keep shipping, you keep getting it
New workflows, updated prompts, and new MCP servers ship every week. The subscription covers everything we'll ever build.
You pay once a year. We ship every week. That's the deal.
Once you're in
- 1
We email you when access opens
You'll hear from us about a day after you check out, or when this sale closes. Every course, workflow, and tool we ship is yours from the start.
- 2
Take the 30-second path quiz
We show you which workflows matter for how you work.
- 3
Start your first workflow
Most people finish module 1 in 15 minutes.
Alex Garrett-Smith
Technical Education Lead
Alex started recording screencasts in 2009 while he was still learning to code.
He built Codecourse into a 300,000-subscriber YouTube channel while holding down a full-time dev job. What started as screencasts grew into a full learning platform, then got acquired in 2025.
Then he joined the BitterBrains team to take on a bigger challenge, figuring out how developers actually work with AI. He now leads the curriculum and judgment frameworks behind unlearn.
Sixteen years teaching developers how to build. Now writing the playbook for what comes next
0+
Developers trained
Still here? Good.
500 engineers become dangerous at this price. When these seats are gone, the price goes up.
$299 $219/yr · 30-day full refund
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