Fall 2025
Tuesdays, 12:20pm to 2:50pm in 370 Jay St, Room 413
Important Scheduling Note: Due to a conflict, Class 4, scheduled for September 23rd, will be rescheduled to MONDAY September 22nd. Exact time TBD.
Welcome
Welcome! I’m very excited to be teaching Physical Computing again this year. It’s material I love and use a lot. I created and teach two other courses in the Physical Computing Area: Energy and Time. I’m curious about every aspect of how things are made – how materials can be shaped into useful and beautiful forms, how sensors can perceive the world, how code can be crafted to run on processors and affect the world.
Recently there’s been an explosion in tools available to beginners for embedding computation into just about any project imaginable. It can be a bit overwhelming – electronics! programming! so many boards to choose from! – but this course lays a foundation for you to build amazing things now and continue developing creative and technical skills over a lifetime.
AI
Another very new and rapidly changing factor in our field is Artificial Intelligence. I began studying Computer Science during the “Second AI Winter”, when progress in the field seemed to stall. But we are all familiar with the unprecedented pace of AI today, and recent (as of 2025) model advances have made LLMs amazing tools tool for writing code, and even designing physical objects. (Pause a moment to consider that these are emergent abilities, often discovered after the fact, not “built-in” by design.) AI can be an powerful learning guide and creative tool, and at the same time it can create very difficult to solve problems when learning a new field and trying to build things that work in the real world. Everyone – you as students, me, industry pros, everyone – is learning right now how to harness these new capabilities and handle these problems.
My plan this year is to remain open to AI. It’s not going away. There will be good and bad ways to use it. In preparation for this year’s class, I’ve been trying to put myself in the mindset of a student to see how tools like Gemini, Claude, and ChatGPT can help understand the material of this course. I’ve asked models basic questions about electronics, requested explanations of code, and even given open-ended prompts to build passable midterms or finals for this class. The results are fascinating! Often helpful, sometimes astoundingly so; occasionally confusing and downright frustrating. Even sometimes – dangerous. But with enough potential that I’ll continue to explore and share the results as we work together this semester.
I also have so many things I still want to learn – new boards I haven’t had time to program yet, new IDEs and development tools I want to add to my skill set, let alone vague project ideas I want help moving forward – and I’m seeing how AI can assist, very much as you might during your time at ITP.
If you are coming to this class as a first-time programmer, I’ve always felt that PCOMP is a great way to learn coding. It’s so fun to see what you write come to life in a real object that is running just your code. I think PCOMP will be a great way to engage AI, too, because there is an immediate, testable ground truth – does the code run? Does the circuit work?
AI can too easily replace deep study with easy answers, short circuiting our learning. And I’m wary – and puzzled, excited, thrilled – about what it will mean to “be creative” in an age where machines can do things that mimic art. I’m so glad to be working with you this semester to find ways to accelerate our learning and deepen our understanding. Our task is to learn and build while preserving the core, human part of our creative practice, using tools that will no doubt be different and more powerful at the end of our 14 weeks together than at the start. This is an amazing time to be studying creative technology!
3 AI rules for this class:
- Anything generated with AI must be labeled as such.
- Link to logs of the full chat/context that generated the result so the full process is visible.
- I won’t debug your broken AI code. AI can generate things much faster than you or I can read them. If you use the tools to make a mess, its on you to clean it up before you start asking me to help.
And one AI recommendation: Don’t outsource your creative essence to a machine. I want to see *you* in your work.
Contact
This is my NYU office hours calendar. You’ll need to sign in with your NYU login to see it. I will schedule regular office hour appointment slots which you can book automatically once the semester starts.
My email: jeff@fddrsn.net, jeff.feddersen@nyu.edu (or jf543@nyu.edu)
Outside of office hours, email me and we can discuss issues and find a time to connect if necessary. For Fall 2025, I teach at ITP Tuesdays and Thursdays and have the roll of Production Mentor. I will have a desk space in the North area, and I’m happy to talk when I’m at ITP. I’m also glad to find times to connect over video outside those days or scheduled office hours. That said, I don’t work full time at NYU, and try to keep “normal” business hours balanced with other work and life.
For more support, the residents and other professors keep office hours as well, here; and watch for the weekly resident pcomp support sessions soon.
Class Blogs
You will document your work in this class online, typically through a blog, Notion, or similar. Please add a link to your documentation site to our shared class spreadsheet before Class 2. Note – set up a category/menu for this class and submit the link for that category, not to your whole blog.
Useful links
- The week-by-week syllabus. When in doubt, check here.
- Arduino Language Reference – code with this open
- The Nano 33 IOT page – useful for the pin functions in the Technical Spec. Getting started guide walks through board and driver installation.
- This image since the board didn’t label its pins (on top)!
A note on how to use this site
There’s a lot, lot! of information at itp.nyu.edu/physcomp. Then there’s the whole rest of the internet, starting with Arduino HQ, going on to great sites like learn.adafruit.com and learn.sparkfun.com, not to mention infinite how-tos on YouTube (even Vimeo), data sheets for every component ever made, etc… It can get overwhelming.
With the ITP site, we’ve tried to do two things:
- Provide a week-by-week syllabus for the semester that takes you through the physical computing material in a logical progression. Each week has clear tasks, assignments for the following week, and links to labs, write-ups, and videos that support or explain the current material. Follow along here and you’ll be fine.
- Provide an organized set of materials covering the core physical computing topics, to serve as a first resource for any questions you may have as you study the subject. These live under the Topics, Videos, Resources, and Labs tabs. These materials are also linked to from the syllabus, but here they’re organized by subject matter, whereas the week-by-week syllabus is chronological.
Class Notes
I’ll post slides and topics from class discussion here when they’re helpful.
Class 1
Slides
