Reading Room

This is an early read on how the next generation is picking up technical skill, worth watching if you ever hire, mentor, or think about where AI talent comes from next.

Undergraduate enrollment in computer and information sciences at four year US colleges fell 8.4 percent in spring 2026 compared with a year earlier, according to the National Student Clearinghouse Research Center. That followed a 3.6 percent drop the year before, in fall 2025, when graduate enrollment in the same field fell 14 percent over that same stretch. Two enrollment cycles in a row, fewer students are choosing a computer science degree by name.

The twist: one expert quoted in the piece does not think students are losing interest in technology itself. The read is that they are folding tech skill into other majors instead of majoring in it alone, on the bet that pairing coding with a second field, like biology, business, or design, is worth more once AI tools can write a lot of code on their own. Colby College, a small liberal arts school in Waterville, Maine, is offered as one example: it has started teaching AI across departments rather than keeping it inside the computer science department. David Watts, who directs Colby's Davis Institute for Artificial Intelligence, put it this way: "The vision is really around an interdisciplinary approach to AI." His reasoning, in his own words: "A lot of the challenges around AI go a lot further than the AI itself."

If that pattern holds, it changes what a strong junior hire looks like in a few years: less "has a computer science degree" as the signal, more "can code well enough plus knows a real domain." Worth watching the next couple of enrollment cycles to see if this is a lasting shift or a two year wobble.

Outside your usual reading: Nature published a review taking stock of where AI actually stands in drug discovery today, separating real progress from hype. No specific numbers came through in the feed itself, but it is a useful source to check the next AI-and-medicine claim against.

OpenAI is rolling out new education plugins for ChatGPT Work and Codex, its coding tool, built for K-12 teachers, college educators, and students who want to learn, teach, research, and build with it. It's a quick read on where OpenAI is aiming its product next: after workplaces, now classrooms.

Spotted on LinkedIn: a post by Alexandre Zajac citing GitHub's claim that 20 percent of its 450 million repositories are now AI related. It's a rough but useful gauge of how much of all software work has already gone AI flavored, roughly one in five repos on the world's biggest code host.

Worth a look if you want to prototype an agent idea instead of building one from zero. A GitHub developer named Shubhamsaboo maintains a free collection called awesome-llm-apps: more than 100 working AI agents (programs that take several steps on their own to finish a task) and RAG apps (tools that look things up in your own documents before answering). Everything is Apache-2.0 licensed, so you can clone it, change it, and even sell what you build with it. Each app is described as hand-built and tested end to end, not a demo that only works once on stage. The collection runs on Claude, Gemini, GPT, DeepSeek, Llama, Qwen, and other open weight models (models whose files are public, so anyone can run them), and comes with more of them delivered via a companion newsletter called Unwind AI.

The simplest tier is 'starter' agents: single files you can run with just an API key, no other setup. There is a travel agent that builds a day by day itinerary, a data analysis agent that answers plain English questions about any spreadsheet, and a medical imaging agent that reads X-rays and scans with Google's Gemini. A blog to podcast agent turns any blog link into a narrated episode, a meme generator agent makes memes by driving a real web browser rather than calling an image tool, and a music generator turns a text prompt straight into an MP3 track. A finance agent built on Grok pulls real time stock data.

A step up are 'production style' agents, which chain several tools and steps together with memory and multi-step reasoning instead of answering once.

Above those sit agents that run on their own schedule, without being asked each time. An always-on Hacker News briefing agent scouts the site and delivers a ranked daily summary to Slack or email. A Release Radar agent watches your software dependencies and briefs you when a release breaks something, gets dropped, or carries a major security fix. At the top, full agent teams work a job together to handle complex, cross-domain tasks.

The newest section is not agents at all but 'agent skills': small add-ons that give a coding assistant like Claude Code, Codex, or Cursor a new ability with one command. Project Graveyard scans your old repositories, tells you why each abandoned side project died, and helps you pick the one worth finishing. Commit Archaeologist reconstructs why a piece of code exists by reading the commit that introduced it and everything that has touched it since. Dependency Doctor checks your project's dependency list for outdated pins and packages pulled after release. The most unusual one, Advisor Orchestrator Worker, runs three different AI models in one loop: Claude Fable 5 advises, GPT-5.6 decides which step runs next (deciding which step runs when), and Gemini 3.5 Flash does the actual work.

None of this is polished software, it is a shelf of working starting points. The value is in seeing more than 100 real examples of how other builders structured an agent, in code you can run in minutes and take apart line by line.

An 18 year old about to start a computer science degree posted a simple, honest question on Reddit: how do you actually learn the math for machine learning (ML, teaching computers to learn patterns from data) research, instead of just taking the standard path into a big tech company? The post has no answers yet, only the question, but the question itself is worth sitting with because it captures the exact confusion facing anyone trying to get into AI research right now.

The specifics are concrete. The poster wants to skip the usual path into a big tech job and go straight for research instead. Their blocker is math: they know it is central to ML research, but say there are, in their words, tons of free courses and videos and one shots out there, with no clear way to pick between them. Their second question is about programming: is Python enough on its own, or do they also need to learn C and C++?

There is no answer in the thread, and this piece will not invent one. What the post does show is that the entry point into AI research feels far less mapped out than the entry point into a coding job. The coding path has a well worn route: practice problems, internships, standard interviews. The research path does not have an equivalent yet, at least not one this poster could find. That gap is worth remembering the next time a junior person, wherever he encounters one, asks the same kind of question.