What we’re building, and what we’re not.
neoG Camp is rebuilding its full-stack curriculum for the AI era. This is how we think about it, so you can decide whether it is for you.
What should be taught when AI can code?
AI writes more code every month. The demand for engineers who understand systems and business context grows with it.
Developer demand isn’t going down. What it asks for is changing. So we teach how real systems are built and run, not syntax.
Prompt engineering is not the job.
Prompts are fragile. A model update can break them overnight.
Real products don’t rely on prompts, they rely on systems. No company hires someone to “write good prompts” in isolation.
Scaling takes code, evals, guardrails, memory and infrastructure.
lookup_orderok212 ms
- call
- { "order": "A-1042" }
- result
- { "status": "packed" }
- elapsed
- 212 ms
send_updateok96 ms
- call
- { "to": "customer" }
- result
- { "sent": true }
- elapsed
- 96 ms
Prompts don’t solve latency, cost, reliability or safety. A prompt is one input to one component, inside an app with users, data, failure modes and a bill.
Prompts are a UX detail. Engineering is the job.
A six-month ML course is not the path to building models.
Modern ML research takes years, not months.
You can’t compress math, statistics and systems thinking into a bootcamp. Training frontier models takes large teams, compute and research depth.
Most courses teach simplified notebooks, not real ML pipelines. Graduates are left between roles: not researchers, not engineers. We respect that path and we don’t pretend to shortcut it.
Serious ML is a long-term path. Applications are where impact happens.
System > Prompts
Watch a prompt find its size.
What we are building: engineers who ship.
A software engineer who ships full-stack products where AI is one part of the system.
Make the checkout show delivery dates
Done. Each item now shows its date.
That engineer is comfortable with the whole system around a model, and it sits on solid React, API and database fundamentals.
- Start with React. Every application has a frontend. State, effects and component architecture.
- APIs and databases. What makes a website an app, and enterprise-grade projects to prove it.
- Advanced patterns. Sockets, OAuth, JWT and file storage: the production knowledge behind real products.
- Getting ready for AI. Python, Postgres and vector databases, plus APIs built to serve models.
- Everything agents. Planning, tool calling, RAG and vector search, built on the layers below.
How that shapes the course.
For six years we have taught full stack as the foundation, with projects, mentors and reviews. That does not change.
AI goes in where it earns its place, one module at a time. Each module builds on the one before it, in the same order every engineer meets these problems: frontend, backend, production patterns, then AI.
Current neoGrammers get it first. If you are on the roadmap now, you stay on it, and new modules reach you before anyone else.
Want to build this way?
Admissions for the next cohort open with the new curriculum. Join the waitlist for first access.
Join the waitlist