Zero to AI-native pro. One program, one price, no gatekeeping.
This is the whole climb. Whether you’ve never written a line of code or you’ve shipped production systems for a decade, AI-Native Software Engineering takes you from real fundamentals through senior-level AI-native practice — one program, one price, one enrollment decision. Just the hours, and the will to climb.
Why this course exists
We used to split this into two purchases: an entry ramp, then "the real thing" gated at two years of experience. That was backwards on both counts. Years-of-experience was never the right admission signal — it measures tenure, not whether someone can verify what a machine hands them. And asking a motivated beginner to clear one purchase just to qualify for the next was a friction problem dressed up as a curriculum decision.
What actually predicts whether someone thrives here is commitment: will they show up, ship every week, and sit through a Waypoint Review that says "revise and resubmit" without quitting. That’s a much better filter than a résumé line.
So the flagship became the whole thing. If you’ve never coded, the first eight weeks build the mental models that make AI leverage usable at all — the same fundamentals we used to sell separately. If you’ve been shipping production code for years, those weeks move fast: you’ll clear the early Waypoints quickly and spend most of your time in the sixteen weeks that follow, where the agentic, architecture-heavy AI-native craft lives. Same program, same standard, different pace through the parts you’ve already earned.
Fit matters
Small cohorts mean we’d rather turn you away than waste your next 24 weeks. Read both lists honestly.
Expected outcomes
Outcomes are written as capabilities, because that’s what reviews test and what employers probe.
Read, write, and debug real programs — and explain them line by line, not just prompt them into existing
Decompose ambiguous problems into buildable, delegable pieces — the core skill AI can’t do for you
Design systems and make architecture calls you can defend — the judgment layer AI can’t supply
Run an agentic development workflow: specs, decomposition, parallel agents, and review gates that scale
Land substantial changes in large, unfamiliar codebases with confidence
Verify AI output systematically — testing strategy, debugging AI-era failure modes, security review
Ship a production-grade capstone system solo, reviewed like a real team would review it
Leave with a public build log and a defended portfolio case study — proof of the whole climb, not just the end of it
The learning journey
Every phase has an entry state, an exit state, and a Waypoint Review gating the way forward — so progress is never a feeling, it’s a fact.
Weeks 1–3
Programming mental models, problem decomposition, and tool fluency — with AI used only as an explainer while your own models form.
Waypoint 1 · Waypoint 1 · The Glass-Box Build
Build a command-line tool, test it, and walk a mentor through every line. Nothing in it may be a mystery to you.
Weeks 4–6
How the pieces of an application fit: HTTP, data, APIs, and interfaces — the map every engineer carries.
Waypoint 2 · Waypoint 2 · The Working System
A working API and database behind a simple interface, demoed live — including tracing one request through every layer, out loud.
Weeks 7–8
The deliberate inversion: now that you can verify, you learn to delegate. AI-paired building of a full project, with supervision discipline as the graded skill.
Waypoint 3 · Waypoint 3 · The Supervised Build
A full-stack app built AI-first, deployed, and defended: you present what the AI did, what you corrected, and how you knew.
Weeks 9–10
Install the full AI-native workflow and find out, concretely, what changes once you’re building daily instead of just learning it.
Waypoint 4 · Waypoint 4 · The Rebuild Diff
Rebuild the Supervised Build (or another project) AI-first at speed. Present the diff: where the machine multiplied you, where it misled you, and what that means for how you’ll work from here.
Weeks 11–14
When code is cheap, design is the bottleneck. Four weeks on architecture, data, and API judgment — the decisions AI executes but can’t make.
Waypoint 5 · Waypoint 5 · The Architecture Defense
Design and build a service end-to-end, then defend the architecture in review: every trade-off questioned, every alternative considered.
Weeks 15–18
Scale your delegation: agentic workflows, parallel agents, verification at scale, and the hardest arena — large codebases you didn’t write.
Waypoint 6 · Waypoint 6 · The Legacy Landing
Take a substantial feature from issue to merged in a large open-source codebase you’ve never touched, driven by an agentic workflow you can explain.
Weeks 19–21
Speed without trust is a liability. Three weeks on testing strategy, debugging what AI breaks, security, and reviewing at the pace machines produce.
Waypoint 7 · Waypoint 7 · The Hardened Release
Take a working system through audit: test suite, security review, load test, and a written risk assessment. Ship the hardened release.
Weeks 22–24
The capstone: a production-grade system, built solo with your full AI-native workflow, reviewed like a real team would review it.
Waypoint 8 · Summit Defense
Present the finished system: live demo, architecture walk-through, AI-workflow retrospective, and questioning from mentors. Then publish the case study.
The curriculum, week by week
Lesson-level detail is refined with each cohort; the weekly structure below is the commitment.
Wk 01
How programs actually run: values, control flow, and the machine’s point of view
Ships: First working programs + terminal and Git set up for real
Wk 02
Decomposition: turning fuzzy problems into steps a program can take
Ships: A small tool built from your own written plan
Wk 03
Debugging as method: hypotheses, evidence, and reading error messages
Ships: The Glass-Box Build, ready for Waypoint 1
Wk 04
The web’s contract: requests, responses, and what a server really is
Ships: A tiny server you built and can interrogate
Wk 05
Data: modeling the world in tables, and talking to a database
Ships: Your API storing and serving real data
Wk 06
Interfaces: enough frontend to make a system usable
Ships: The Working System, ready for Waypoint 2
Wk 07
AI-paired building: prompting, reviewing, and correcting generated code
Ships: Project core built with documented AI collaboration
Wk 08
Shipping: deployment, polish, and the honest retrospective
Ships: The Supervised Build deployed and defended; build log complete
Wk 09
The AI-native workflow: editor agents, terminal agents, and the new inner loop
Ships: Your environment rebuilt and a first AI-first feature shipped
Wk 10
What changes at speed: fundamentals that matter more now, rituals that are dead
Ships: The Rebuild Diff project, ready for Waypoint 4
Wk 11
Architecture as decision-making: boundaries, coupling, and the cost of change
Ships: System design doc for your phase project, reviewed
Wk 12
Data modeling: schemas that survive requirements you haven’t met yet
Ships: Data layer built, with a written model rationale
Wk 13
API design: contracts, versioning, and designing for consumers
Ships: Public API shipped with contract tests
Wk 14
Specs machines can build from: turning design into delegable work
Ships: Service completed via spec-driven AI build, ready for Waypoint 5
Wk 15
Agentic workflows: decomposition, delegation, and review gates
Ships: A multi-step feature landed through an agent pipeline
Wk 16
Context engineering: giving machines the map of a system
Ships: Agent-ready onboarding docs for an unfamiliar repo
Wk 17
Reading at scale: archaeology in codebases too big to read
Ships: Architecture recovery report on your target codebase
Wk 18
Landing the change: tests, review, and the merge
Ships: Feature merged or in review upstream — Waypoint 6
Wk 19
Testing strategy: what to test when a machine wrote it
Ships: Test suite with a written strategy, not just coverage
Wk 20
Debugging AI-era failures: plausible-looking wrongness and how to catch it
Ships: Bug hunt: find and fix seeded defects in generated code
Wk 21
Security and review culture: threat modeling, review at speed
Ships: The Hardened Release, ready for Waypoint 7
Wk 22
Summit design: scope, spec, and the plan of ascent
Ships: Approved capstone spec and architecture
Wk 23
The build: full workflow, mid-week design reviews
Ships: Working system, feature-complete
Wk 24
Hardening, defense, and the case study
Ships: Summit Defense passed; portfolio case study published
Projects
Every phase produces something reviewable; the Summit produces something you defend.
Phase 1
A real command-line tool where every line is yours to explain. The anti-black-box exercise.
Phase 2
An API, a database, and an interface that fit together — and a map of how.
Phase 3
A deployed full-stack app built with AI leverage and human verification — your first act of AI-native engineering.
Phase 4
The same build (or a new one), rebuilt AI-first at full speed — with an honest analysis of where the leverage and the traps are.
Phase 5
An end-to-end service where you make and document every architecture call, then build it via specs an agent executes.
Phase 6
A real feature landed in a large open-source codebase you’ve never seen — the closest thing to a new job’s first month.
Phase 7
A system taken through tests, security review, and load — then shipped.
Summit project
A production-grade capstone of your own design, built solo, defended in review, published as a case study.
Mentorship
Small pods, senior mentors, and promises with numbers attached — mentorship here is a system, not a Slack channel.
You’re placed in a pod of six in week one and climb with them to the end. Small enough that your absence is noticed; strong enough to survive a hard week.
A live session with your pod mentor every week: demos, unblocking, and group review of real work.
Thirty minutes with your mentor, agenda owned by you — career, code, or the thing you’re stuck on.
Every weekly deliverable gets a real review — line comments and questions — within one day. That’s a promise, not an aspiration.
Twice a week, drop in with anything. No booking, no agenda.
Weeks 1–2 add guided pairing sessions so absolute beginners never stall at setup.
Mentors are practicing senior engineers who run AI-native workflows in production teams — not teaching assistants.
Phase 6 pairs you with a mentor who knows your target open-source codebase’s ecosystem.
Summit reviews are run as real design reviews: your mentor recruits a second senior engineer so the questioning is fresh.
The Ship Week Rhythm
Courses fail when weeks drift. Ours can’t: every week ends with something shipped, and going quiet triggers a human, not a reminder email.
You post the week’s targets to your cohort space. Everyone sees them.
Three times a week, two sentences: what moved, what’s stuck.
The week’s increment goes up for review and gets demoed in your pod. Shipped beats perfect.
Silent for two days? A mentor reaches out personally. Not a bot — a person who knows your project.
Your public record of the whole journey — and the proof of work employers actually read.
Waypoint Reviews
No exams. Each phase ends with a Waypoint Review — you present working software against a rubric published on day one, the way real engineering teams review real work.
Forty-five minutes: you demo the milestone, walk the code, and answer questions about why it’s built the way it’s built.
Functionality, code quality, AI-collaboration quality, and reasoning under questions. No surprises, no trick questions.
Prompt hygiene, verification discipline, knowing when not to use the tool — assessed explicitly, because that’s the craft now.
Miss a waypoint and you get a week to close the gap, like a returned pull request. Rigorous, not punitive.
From the trail
“I came in convinced AI tools were making me a worse engineer. Sixteen weeks later I design more carefully than I ever have — and ship about three times faster. The Waypoint reviews were the hardest technical conversations of my career, in the best way.”
Backend developer, 4 yrs experience · AI-Native Software Engineering
Pricing
No fake discounts, no upsells inside the course, no “premium tier” of attention. Everyone gets the whole thing.
AI-Native Software Engineering
₹1,15,000
Or 6 monthly installments of ₹19,200
One-time, all-inclusive. One price for the whole climb — zero experience to AI-native pro. No separate prerequisite course to buy first.
Every cohort reserves need-based scholarship seats. If the price is the only thing stopping you, apply anyway and say so.
Questions, answered straight
Yes — the first eight weeks assume zero experience and build the real mental models (programming fundamentals, how the web fits together) before any heavy AI delegation. That sequencing is deliberate: you can’t supervise what you don’t understand. Beginners who put in the hours pass Waypoint 1–3 the same way Foundations graduates always did — because this is that program, folded in.
They’ll move fast for you, not waste your time. Experienced engineers typically clear Waypoints 1–3 well inside the eight weeks, since the pacing is built to let genuine competence show up quickly in the reviews. What you actually came for — architecture, agentic workflows, verification at scale — starts in week 9 and runs for sixteen weeks.
Because years of tenure was never actually what predicted success here — commitment was. Gating admission by resume line while claiming to teach judgment was inconsistent, and it meant beginners had to buy a separate course just to qualify for this one. We’d rather assess you on whether you finish Waypoint 1, not on what your LinkedIn says.
The program is tool-current but not tool-dependent: you’ll work with the leading editor and CLI agents in use at the time your cohort runs. The workflow, decomposition, and verification skills transfer across tools — that’s deliberate, because the tools will change and the craft won’t.
The program is designed for exactly that: 10–15 focused hours a week (heavier in the first eight weeks, lighter once you’re past Waypoint 3), with live sessions scheduled for working professionals and everything recorded. What it doesn’t survive is zero hours — the Ship Week Rhythm assumes something ships every Friday, for all six months.
You get specific feedback against the rubric and one week to revise and resubmit — like a returned pull request. Mentors work with you on the gap. Waypoints exist to keep the standard real, not to filter people out.
No. If you’re starting from zero, the first eight weeks teach Python for the thinking and backend phases, plus enough HTML/CSS/JavaScript to ship a real interface. If you’re already writing code, you’ll clear those weeks quickly, and the stack is flexible from week 9 on — you’re reviewed on design, workflow, and verification quality, not framework choice.
No, and we’d be suspicious of anyone offering one. What you leave with is better: a public build log spanning the whole climb, a defended production-grade capstone, and interview-ready judgment — the things hiring processes for AI-era engineering roles actually probe.
Every session is recorded and available the same day. Live attendance is strongly encouraged — the studio format is interactive — but the program is built to survive real life and real time zones.
Cohorts are scheduled around Indian evenings and weekend mornings (IST), which also works for the Gulf and Southeast Asia. Everything is recorded, and async reviews mean your work never waits for a meeting.
Full refund within the first two weeks of the cohort, no questions and no forms-designed-to-exhaust-you. After that, fees are non-refundable but you can defer to a later cohort once, free.
Yes — installment options are listed with each course’s pricing, and every cohort reserves need-based scholarship seats. If price is the only thing stopping you, apply and say so plainly.
Graduates receive a completion credential — but the artifacts that actually open doors are the ones you build: a public build log, defended projects, and a published case study. We optimize for those.
Working developer → AI-Native Software Engineering. New to code → Software Foundations. Already AI-native and choosing a depth → the specializations. Bringing a whole team → For Teams. Still unsure? Write to us with two lines about where you are; a human replies.
Cohorts are capped at 24
Applications take ten minutes and are read by an engineer. If a different course fits you better, we’ll tell you straight.