See also: SyllabusSyllabus
See also: Lectures · Grading · AI Policy
Course Title: Tech 1 — Web & Mobile Ecosystem Fundamentals
Course Objective: Gain a foundational understanding of the core technologies, p... · LecturesLectures
See also: Syllabus · Grading
One 2-hour session per week. Dates may shift — treat the week numbers as canonical.
Lecture 1 - Hello, Internet
Week 1 — 3 August 2026 — Ship a personal w...
Tech 1 Grading Plan
Assessment and Grading Breakdown
| # | Component | Weightage (%) |
|---|---|---|
| 1 | Attendance and Class Participation | 10% |
| 2 | Class Exercises | 50% |
| 3 | Final Project | 40% |
(If the university requires written midterm/endterm exams, weightages will be adjusted and announced in class.)
1. Attendance and Class Participation (10%)
Attendance is recorded each session. I have an open door policy — if you need to take a break or leave, feel free to do so. The penalty is that you don't get the attendance credit and don't learn from the material. No distinction is made between excused and unexcused absences.
2. Class Exercises (50%)
Weekly build exercises, done mostly in class and finished as homework:
- Exercise - Ship a PageExercise - Ship a Page
Build and deploy a personal webpage — today, in class
You will use an AI tool to generate a personal webpage and put it on the real internet with a URL you can send to anyone.
Steps
Pick a to... - Exercise - Web Page AnatomyExercise - Web Page Anatomy
Dissect a complex web page
Pick a rich, real-world web page (a news homepage, an e-commerce product page, a streaming service) and analyze it: identify its HTML structure, CSS styling, and JavaScr... - Exercise - Trace a RequestExercise - Trace a Request
Watch your website load, piece by piece
Use the browser's Network tab to observe everything that happens between typing your URL and seeing your page.
Steps
Open your own site (from Exercis... - Exercise - Figma to CodeExercise - Figma to Code
Get an AI to build your design — then judge it
Take a screen you have designed in Figma (from any past project) and use an AI tool to turn it into a working web page. Document faithfully where the... - Exercise - Add a DatabaseExercise - Add a Database
Make something that remembers
Everything you've built so far forgets on refresh. Build a small app where data persists — a guestbook, a class poll, an RSVP page, a tiny leaderboard.
Steps
Pic... - Exercise - API MashupExercise - API Mashup
Build a page powered by someone else's data
Use a public API to make a small, well-designed page that displays live data — and understand the request/response loop underneath it.
Ideas
Weathe... - Exercise - Add an AI FeatureExercise - Add an AI Feature
Put a model inside your product
Add one genuinely useful AI feature to something you've already built this semester. The bar: it must be better because it's AI, not AI for its own sake.
Ideas
... - Exercise - Mobile Ecosystem ComparisonExercise - Mobile Ecosystem Comparison
iOS vs Android, from a product team's chair
Compare and contrast the iOS and Android ecosystems for app development and distribution, highlighting the differences that would actually change a prod... - Exercise - SDLC Stage MappingExercise - SDLC Stage Mapping
Walk a feature through the lifecycle
Map a hypothetical product feature through the stages of a chosen software development lifecycle (e.g., Agile Scrum), and locate the designer's role at each st...
Each is graded out of 8 on outcome quality, understanding demonstrated, and process documentation (including prompt logs). I will consider your best 6 scores.
3. Final Project (40%)
Group project (up to 3 people), built over the last third of the course and presented at Demo Day. See Project - Final ProjectProject - Final Project
See also: Grading · Lecture 10
Brief
In teams of up to 3, take a product idea from concept to a working, deployed web application that a stranger could use — built primarily by directing ... for the brief and rubric.
A note on AI and academic integrity
See the full AI PolicyAI Policy
See also: Grading · Syllabus
In most courses, an AI policy is a list of things you can't do. This course is different: using AI to generate code is the expected way of working here. The sk.... In this course, using AI to generate code is not just allowed — it's the expected way of working. What is dishonest:
- Presenting work you can't explain. If you shipped it, you should be able to answer "how does this part work?" at the level this course teaches.
- Hiding your process. Prompt logs and iteration history are part of every submission.
- Copying another student's project or prompts wholesale.
Cite third-party assets, datasets, templates, and any substantial code you didn't generate yourself. For group projects, each member's contributions must be documented.