Independent coding
Where AI is not permitted, practise solving problems and writing code independently. Explain your approach, analyse complexity and test your solution.
Where the AI generation learns to build.
Build products with AI. Learn to understand, debug and test what you build—with engineers to guide you and peers to build with.
Exclusively for first & second-year college students.
NYC / Teaching approach
Learn to build
real software with AI.
Build products with AI from the start. Learn concepts, debugging, testing and system design through the software you build.
01 / Development workflow
Start building
before you know everything.
Instructors build with AI in live sessions. You use the same workflow in your own projects: define the problem, break down the implementation, inspect the output and iterate.
You can start building before mastering every language. Hand-writing every implementation is not a prerequisite.
What you’ll practise
- Task breakdown
- Turn a requirement into smaller, testable changes.
- Context & prompting
- Give AI the relevant code, constraints and expected behaviour.
- Model selection
- Choose by task, output quality, speed and credit cost.
- Implementation review
- Read the changes. Check the result. Refine the instructions.
02 / Engineering fundamentals
Understand how
your app works.
Learn concepts through the features you’re implementing. Trace how the interface, APIs and database work together. Explain the architecture and the trade-offs behind it.
AI can generate the implementation. You still need to understand its behaviour and limitations.
From interface to infrastructure
Client/API/Database
- Application logic
- State, data flow and how a request moves through the system.
- Security & access
- Authentication, permissions and input validation.
- System design
- Performance, architecture and what changes as usage grows.
03 / Debugging & testing
Learn to find
what AI got wrong.
Reproduce bugs, trace the cause and test the fix. Check edge cases and failure states. Learn to use AI during debugging without accepting every suggestion as correct.
A working screen is the starting point. Correctness, reliability and performance need evidence.
Your engineering checks
- 01Reproduce
Identify the input and conditions that cause the failure.
- 02Diagnose
Use errors, logs and system behaviour to find the cause.
- 03Validate
Test the fix and check that existing behaviour still works.
04 / Engineer feedback
Get your project
reviewed by engineers.
Submit your working product and explain how it works. Review the code, decisions and failure cases with an engineer. Apply the feedback to improve your implementation.
Watch an NYC project review
What the review examines
- Correctness
- Does it solve the problem it was built to solve?
- Engineering decisions
- Can you explain the approach and its trade-offs?
- Failure handling
- What breaks, and how does the system respond?
- Next iteration
- What needs to change, and how will you verify it?
05 / Collaborative development
Build projects
with other students.
Work on individual and team projects throughout the programme. Discuss implementations, review each other’s work and solve technical problems together.
Progress towards useful products, hackathons and real open-source contributions. Collaboration is part of the learning process—not something reserved for a final project.
See NYC community projects
Work beyond an assignment
- Shared builds
- Break down the work and integrate contributions.
- Hackathons
- Work with a team, a deadline and a problem to solve.
- Open source
- Understand an existing codebase and work towards useful contributions.
- Technical network
- Build relationships through shared work and engineering discussions.
06 / Targeted internship preparation
Prepare for the interviews
you’re applying to.
For students targeting Microsoft, Amazon and other companies, preparation follows the role and its assessment rules.
AI-assisted assessments
Where AI is permitted, practise directing the tools, reviewing generated code and validating the result. Explain and defend your technical decisions.
Available through the internship-preparation add-on.
Explore courses & preparationNot Your CollegeLearn alongside college
NYC Accelerator Programme
Learn by building real projects.
You don’t have to master every language before making something useful. Build with AI from the start and learn the concepts through your product: how it works, where it breaks and how to improve it.
Explore NYC AcceleratorHow we teach engineering
AI can write the code.
You need to understand it.
Choose AI tools by the task, quality, speed and cost. Give clear instructions. As you build, learn to read the code, debug failures, test behaviour and decide what needs to change.
Built in the NYC community01 / Notch
A first-year student built this Windows app.
Akshit used AI for research and debugging while making the decisions, testing the app and packaging it for Windows.
AI: research & debuggingAkshit: decisions, testing, deployment.
Built by Akshit
Your first tech internship
What you need to get a tech internship.
Coding, concepts and AI tools.
Learn how code works, how to guide AI and how to check what it produces.
You can learn this from 
YouTube.
Then use it on real projects.
At NYC, you build with peers, get engineering feedback and develop work you can explain. Add focused interview practice for the internships you’re targeting.
02
Solve problems you haven’t seen before.
Break a problem into smaller parts. Compare approaches, check the AI’s suggestions and explain why your solution fits. Learn the concepts through the decisions your product needs.
Learn through engineer feedback03
Build projects you can explain.
Use AI to turn an idea into a working product. Trace how the code, APIs and data fit together. Test failures and improve the system before it goes live.
See what our community builds04
Work with people who build.
Build with peers who take their work seriously. Share ideas, review each other’s work and find teammates for hackathons, open source and the next product you want to make.
HackathonsGoogle Summer of CodeOpen source
Meet the community 05
Be ready for the interview.
Targeting Microsoft, Amazon or another company? Practise independent coding where required and AI-assisted problem solving where permitted. Learn to explain and test your solution in both formats.
Explore internship preparation Available with the internship-preparation add-on.Understand the work behind every project.
Leave each build knowing more: how the system works, what you changed and why your solution is better.
Talk to an advisor
Course plan, fees and internship preparation, on one call.
How you learn
Get your code reviewed by top engineers.
Bring the product you built with AI. Work through the bugs, tests and design choices with an engineer. Understand their feedback and use it to make your next version stronger.
01BuildBuild with AI
02ReviewQuestion the decisions
03ImproveTest the next version
The NYC community
Your college isn’t
your only network.
Build with other students, get help when you’re stuck and work on projects people actually use.
Inside the NYC communityChallenge / 01
Can you find
the bugs?
Debug Battle
Find the bug, fix it and check that the fix works.
Open the challenge Meet other buildersCollaboration / 02
Your next teammate could be here.Build
Photo illustrationBuild
together.
Build with people
who care about the work.
Find a team on Discord Conversations from the NYC Discord
Beyond campus
Opportunities beyond
your college.
Join hackathons.
Build a working prototype with your team and present it before the deadline.
Contribute to open source.
Work on real codebases and prepare for programmes like Google Summer of Code.
Get noticed for your work.
Share what you build and help others. Good work gets noticed.
Challenges are open to NYC members. Activity dates are shared with each batch.
The NYC community
Join the NYC community.
Join the NYC developer community.Try a quick project review
Your app runs.
What happens when it fails?
AI can help produce a working first version. You still need to test what can go wrong. Try this library search, spot the missing case and see how a review improves it.
Test this library app
- 1. Test your app
- 2. Get a review
- 3. Improve it
Introduction to Python
Working with databases
Web development basics
Engineer feedback · Example review
“Say the book isn’t available. Give the user a way to browse other books.”
The app now handles a missing result.
You tested a failure, understood the feedback and improved the product. That’s engineering judgement in practice.
See how NYC helps you improveExample app · Illustrative engineer review.
See what students are building
Projects built by
NYC students.
See what students in the NYC community have made. Open the demos and hear the decisions behind the software and hardware they’re building.
Watch the project breakdownThis student built
This student built
his own deployment platform.
See how a student-built deployment platform works. Follow the technical decisions that turn a working demo into a system the builder can explain.
Student project featured on the NYC channel.More from the community
Explore projects shared by NYC community members.Hardware prototype
AgroBot
A smart-farming rover prototype, still in development.
Built by Akash & team
Web project
DevConnect
An interface for developers to share profiles and projects.
Built by AryanExplore project
Explore project Browser experiment
Bluetooth VZ
A 3D Bluetooth spectrum visualiser.
Built by Anish
Projects by NYC community members, credited with each build. AgroBot is an in-progress prototype. These examples show community work, rather than results from the Accelerator course.
Software Engineering + AI
NYC
AcceleratorProgramme
Build applications with AI and learn full-stack engineering through the work. Understand your interface, backend and data, test what can fail and deploy a product you can explain.
Full stack · AI · DSA · System design
See the syllabus and fees
<your first page />01 / Build with AI
Make your first app.
HTML · CSS · JavaScript
Interface ↔ API ↔ Database02 / Understand the system
Connect the backend.
React · Servers · Databases
Connect your app to AI.03 / AI in your product
Add AI features.
LLM APIs · Retrieval · Agents
Build Test Deploy04 / Test & deploy
Put it online.
Testing · System design · Cloud
Programme scenes are illustrative, based on the NYC moodboard.
Software Engineering + AI / Syllabus
Learn concepts through your projects
See how each part helps you build, understand and improve a product.01Web foundationsUnderstand the web through your first build.
Use AI to build your first interface, then work through the HTML, CSS and JavaScript behind it. Make changes, trace what they do and use Git to track your versions.
HTML · CSS · JavaScript · Git · GitHub02Full stack developmentUnderstand how the whole application works.
Build with AI across React interfaces, servers and databases. Follow a request through the system, understand APIs, authentication and payment flows, and debug the connections between them.
React · Next.js · Node.js · Express · MongoDB · PostgreSQL03AI in applicationsChoose models and check their output.
Choose models for the task, quality, speed and cost. Connect them to your app’s data and tools through retrieval and agents. Test the answers, permissions and failure cases behind each feature.
LLM APIs · RAG · Vector databases · LangChain · LangGraph04Data structures and system designKnow why one solution fits better than another.
Use data structures, algorithms and core computer science to reason about your builds. Compare solutions, understand their costs and explain design choices as your system grows.
DSA · System design · Core CS · Aptitude05Deployment and infrastructureTest what happens beyond your laptop.
Deploy your app and learn how it behaves outside your laptop. Work with containers, deployment pipelines, monitoring and communication between services to diagnose failures and improve reliability.
Docker · CI/CD · Kubernetes · Microservices · Message queues06Showing your workShow the product and explain the decisions.
Demo the product, walk through how it works and explain what you changed after testing and feedback. Share your work on LinkedIn and in developer communities so people can see what you can do.
Project presentation · LinkedIn · Developer communities07Internship preparationOptional add-on
Prepare for the roles and assessments you’re targeting, including internships at companies such as Microsoft and Amazon. Practise coding independently when required and using AI when permitted. Work on applications, technical reasoning, testing and explaining your projects.
Internship applications · Interview preparation08Keep learningKnow when a new tool is worth using.
Explore new frameworks and AI tools through updates, guest sessions and community events. Compare what they help you do, where they fail and whether they fit your next build.
Industry updates · Guest sessions · Community eventsStart building with NYC.Get your course plan
Mentors & teaching assistants
Learn from engineers at these companies.
Amazon
Netflix
OpenAIMentors
Bring the product you’re building with AI and the decisions you’re unsure about. Learn how engineers check code, investigate failures and choose between solutions. Apply their feedback to your own work.
Project feedbackEngineering guidanceFrom a real community projectThey ask
They ask
the hard questions.
When peers asked how AgroBot worked, Akash explained the ESP32, obstacle detection and the integration still ahead.
Built by Akash & teamyou don’t understand.Support illustration
Teaching assistants
Bring a confusing AI suggestion, a broken feature or a concept you haven’t understood. Work through the problem with a teaching assistant and learn why the fix works.
Coding doubtsConcept supportSupport that keeps your work moving
Understand the fix before you move on.
Trace what went wrong and check the change, so you can handle a similar problem in your next build.Company names describe mentors’ workplaces, not company sponsorship. Mentor assignments vary by batch.
NYC Accelerator
Choose what
you want to build.
Each course combines AI tools, subject fundamentals and feedback on your work. Choose your field, then add focused internship preparation for the roles and assessments you’re targeting.
Software
Software Engineering + AI
Build full-stack apps with AI. Understand and test the system behind them.
Explore the course AIData Analytics + GenAI
Use AI to analyse data. Check the numbers and explain what they mean.
Explore the course AIData Science & Machine Learning
Build models, evaluate their results and explain where they succeed or fail.
Explore the course AIAI Engineering
Build AI products you can crack internships with—and explain how they work.
Explore the courseQuick answers
Your questions,
answered.
Yes. You can start without knowing every language or framework. In Software Engineering + AI, you begin building with AI and learn the web concepts, debugging and testing needed to understand and improve your product.
NYC Accelerator
Not sure where to start?
Talk to us.
Build with peers, get engineering feedback and understand the product you build. Tell us what you want to make. We’ll walk you through the course and your options for internship preparation.