u/NASA_Orion

It sucks we dont have a physical GatorOne card anymore.

Hi I’m joining UF this fall. Does anyone know a church that offers service at Sunday night instead of morning?

Like many fathers before me, I’ve offered to conditionally pay for my daughter’s college. I don’t care what she majors in. I don’t care if she pledges a sorority. I do care if her school is located near a Delta hub. It is one of the most socially acceptable ways to bake Delta flying into my everyday life, and I’ve been looking forward to the day that I could use college trips to supplement my work trips and maybe make Diamond.

Apparently this makes me a terrible father. Of all the schools in the US of A, why did she have to fall in love with Rice and Northwestern?? She got into Emory, which I would happily pay for. I’d even settle for UT Austin, since it looks like Delta is ramping up presence there. But no, apparently my daughter is going to force me to fly United. And if that’s the case, she can put up with some work study to pay for her school.

AITAH?

For context, I’m a nuclear engineering major for my undergrad. I was trying to do a CS double major but just didn’t find it was that interesting so I downgraded it to a minor.

Now I’m doing a nuclear engineering phd in computational fields. However, I’m very interested in the recent developments of LLM and def hoping to be part of the team that creates AGI.

I’ve already started taking basic ML/DL online courses and can generally understand them with ease. I’ve always had a strong coding background + my current work is literally writing C++&HPC.

Now I really want to work for frontier AI companies starting from grad student internships. I don’t think I will have trouble learning all the stuff by myself or making side projects. However, since I don’t have an official CS degree, would doing Georgia techs OMSCS (online) help me to pass the automatic filter?

Hey guys, I've reverse engineered the one.uf.edu's course search API and packaged everything into an MCP (Model Context Protocol) on GitHub.

To use this, you must have access to either claude desktop or CLI. (or other LLMs)

The biggest advantage of MCP versus a traditional scheduler is that you can instruct your LLM agents to conduct non-linear searches or otherwise complicated tasks that sometimes require interpretations.

In my undergrad I mostly got by with a notebook + iCal + a folder of PDFs but I can already see this scaling differently.

I've seen discussions about:

• Linear for tasks/issues/backlog. Has a native MCP, so agents can read and update issues across chats.

• Obsidian for notes/papers/second-brain. Local markdown, wikilinks, graph view. Filesystem MCP lets agents read/write vault notes.

The MCP/agent piece is what's making me look at these instead of plain markdown

Does anyone actually use Linear for solo PhD work? Or any alternative setup you've been using for years that you'd recommend? Especially interested in combos (e.g. "Zotero for papers + Obsidian for notes + a markdown file for todos") and ones with decent agent integration.

Especially with AI nowadays, it's incredibly easy to ship a product with minimal team size.

Typical Y-Combinator startup: A small team launch a product and get into YC. They scale their company up and get invested by VCs. Eventually they sell the company and cash out. Throughout this process, the team is very small due to AI and everyone shares the same number equities.

Question: Is the couple million dollar profit justified? If not, did they exploit anyone?

Scope: mainstream weapon developments such as anduril, palantir, lockheed. it does not include sneaky top secret lab that's doing bioweapon etc

Argument:

1. Killing of enemy combatants is not inherently wrong. (does not accept CMV. do not reply if you don't believe this)
2. It's bad when:
   • mass collateral damage such as civilians
   • any amount of pain/agony beyond minimally required to neutralize enemy combatants to achieve a mission objective)
3. Most western governments do not INTENTIONALLY seek to impose collateral damages or unnecessary sufferings
4. We have reached the peak fire power (in terms of casualties imposed) since thermonuclear ICBM back in 1950-60s
5. Modern developments, such as the use of AI, are developed to better identify enemy combatants, to strike more precisely, to neutralize threats more effectively, and to protect friendly forces.

Conclusion, when evaluating modern weapon developments against point 2, we're actually reducing the negative effects of combats ever since the invention of thermonuclear ICBM.

Hi I’m an incoming PhD student in an unrelated field and I want to explore my options for additional AI skills. I noticed there are multiple tracks for AI. Both ECE and CISE has their own AI stuff. Even EGN has their own AI courses (which is part of the MSAIS core courses).

Can someone tell me what’s the difference or if there are any advising resources I can consult. I’d imagine my own departments advisors are useless for this

Both mean you see through the “just be funny and nice” facade and realize looks matter.
Black pill means you’re hopeless. Red pills mean you must acknowledge this fact and try to beat the system.

I feel like looksmaxxing obviously belongs to the latter one

I’m an incoming graduate student researching outdoor org landscape here. OAR seems really cool, but are there decent amount of grad students in there?

Incoming PhD student here. Curious if anyone is actually building real workflows beyond just email editing or summarizing. I want to use the summer to fully setup my workflow to boost my productivity once I get there.

For context on what I mean: I'm setting up Obsidian + MCP so the desktop client can read across my vault (research notes, course material, planning docs) without me manually re-uploading context every conversation. Projects layered on top for focused work with pinned files. For the actual coding side, I'm planning to use previous work that has deterministic correct answers as a benchmark.

Things I'd genuinely like input on:

• Which MCP servers are people actually using day-to-day vs. installed-once-and-forgot? I've seen a lot of demos and very few sustained workflows.
• How are you handling alignment and verification when the agent is doing things you can't easily check? I can catch obvious bugs in code I understand, but when the agent writes something subtly wrong in a codebase, I'm not sure I'd catch it before deploying them on real research.
• If you've had pushback from your advisor about heavy tooling use, what did that conversation look like?

Loading up two full coffee cups of pastries. F you