Silicon Valley Hands On Programming Events

Goal: aid people in building project portfolios for job search in AI/ML.
This is a 2y journey - to go from nothing to either a open source contributor or to have a portfolio of projects to attract hiring interest.

Demos are much easier to build in the era of LLMs. Agent apps using web arena. https://webarena.dev/

You get access to :
Free GPU time from AMD,
Professor OH
online content
other members to compare answers with the caveat LLMs may work better than any human collaboration.

Structure:
Review the lectures 1-2 per week and do a short 10m presentation in front of a group. This material composes more than 50% of the interview questions for AI/ML entry or mid level jobs if you dont have substantial open source contributions. Best to practice NN implementation and basics before interviews. Some of the TAs for this class originally took this class over 10+y ago and are constantly practicing the basics to improve.

Agenda:
cs131 Computer Vision
Deep learning + RL

One example is redoing the cs131 homework questions from F2022 using a vision LLM and comparing with a traditional CV implementation.

Project Ideas. There are tremendous opportunities in CV and Vision LLMs. As an example the benchmarks on image segmentation pipelines need updating. They require additional work to establish performance/cost if the traditional algorithms are replaced with agent/LLMs.

GPU TIme: Free from AMD given a project proposal.
Implssible to make progress on OS code or projects wo substantial GPU time.

Deep learning is evolving quickly. Important new developments are appearing daily. This group attempts to keep up by reading and discussing current deep learning literature. This meetup uses discussion among the participants to speed understanding of current research results. That requires that some participants read the paper before attending. Anyone is welcome to attend and listen without reading the paper. If nobody reads the paper the meeting will be short.

RL Working Group:
We are going to review a few things. These are essential skills before starting a LLM or agentic project beyond 1 GPU.

1. dashboards and backend code for displaying and creating PPO policy parameter sweeps in single GPU mode. Distributed PPO is a separate topic and will require a different env than Ant/Walker/Hopper. This assumes basic knowledge of React. If you dont have it you can build it quickly with Claude Code iterations to get things working. This is different in 2026 vs pre LLM days.
2. Large scale data cleaning and acquisition and synthesis. Not sure this is necessary. We have been here since 2007 starting with Hadoop and Roman's series on Bigtop. New is synthesis using LLMs. This is different for each project.
3. Deploying vLLM to AMD single CPU droplet. Multiple CPUs/GPUs are a different topic. Create a dashboard for billing users. Oddly, billing isn't the default demo but it should be because it touches different skills required for jobs. The helm file production stack for vLLM isn't a good portfolio demo. It doesn't test or set a floor on the basic visualization, backend processing, and cloud API skills required in a job.