"In 2023–24, Bachelor’s degree production fell 5.5% compared to the previous year across CS, CE, and I departments. Among departments reporting both years, the decrease was 4.3%. Despite this drop, production remains well above pre-pandemic levels and reflects continued strength following the post-2020 rebound. CS saw a 7.4% decrease and CE a 13.3% decrease."

But it also looks like enrollment in CS programs increased in 2024/2025:

"U.S. CS departments reported an increase in new majors per department of 12.8%"

  https://datavisualization.cra.org/TaulbeeSurvey/CRA_Taulbee_Survey_Report_2024.html#Bachelor%E2%80%99s_Program_Production_and_Enrollments

This is where I think current hackers should be headed. I grew up with lots of family who were backyard mechanics, wrenching on cars and motorcycles. Their investment in tools made my occasional PC purchase look extremely affordable. Based on what I read, senior mechanics often have five-figure US dollar investments in tools. Of course, I guess high quality torque wrenches probably outlast current GPU chips? I'd hate to be stuck making a $10K investment every 24 months on a new GPU . . .

I have been renting GPU resources and running open weight models, but recently my preferred provider simply doesn't have hardware available. I'm now kicking myself a little for not simply making a big purchase last fall when prices were better.

I find this sadly hilarious. What are the current tells you see? I'm similar in that I read a lot of HN and don't have other social media accounts. But I couldn't even guess at what a person's preferred social media is.

That you are writing AI agents for a living is fascinating to hear. We aren't even really looking at how to use agents internally yet. I think local agents are incredibly off the radar at my org despite some really good additions as supplement resources for internal apps.

What's deployment look like for your agents? You're clearly exploring a lot of different approaches . . .

What kind of experiments are you doing? Did you try out exo with a dgx doing prefill and the mac doing decode?

I'm also totally interested in hearing what you have learned working with all this gear. Did you buy all this stuff out of pocket to work with?

One question I always think about is how much time and effort a "one-time" mirror maker should plan on making to exceed the quality of a generic 8" or 10" F/5-F/7 available from the Chinese mirror makers.

Zambuto seems to imply that whatever magic happens for his mirrors might be in very long, machine driven polishing to smooth out the final surface imperfections that cause scatter. With his retirement and with few mirror makers in the US, it seems like options for buying "high end" mirrors in the 6"- 10" size are very limited. I have been debating an 8" F/7 and would love to just purchase a relatively high quality mirror, but most of the mirror makers seem more taken with significantly larger mirrors.

Or check your local library. It may have a smaller Starblast table-top dobsonian you can check out - I did that when traveling once.

Whatever you do, do NOT buy a small cheap refractor on some flimsy mount. They are mostly awful.

Here are my lazy notes + a snippet of the history file from the remote instance for a recent setup where I used the web chat interface built into llama.cpp.

I created an instance gpu_1x_gh200 (96 GB on ARM) at lambda.ai.

connected from terminal on my box at home and setup the ssh tunnel.

ssh -L 22434:127.0.0.1:11434 ubuntu@

  Started building llama.cpp from source, history:    
     21  git clone   https://github.com/ggml-org/llama.cpp
     22  cd llama.cpp
     23  which cmake
     24  sudo apt list | grep libcurl
     25  sudo apt-get install libcurl4-openssl-dev
     26  cmake -B build -DGGML_CUDA=ON
     27  cmake --build build --config Release 

     28  cmake --build build --config Release -j 16
     29  ls
     30  ls build
     31  find . -name "llama.server"
     32  find . -name "llama"
     33  ls build/bin/
     34  cd build/bin/
     35  ls
     36  ./llama-server -hf ggml-org/gpt-oss-120b-GGUF -c 0 --jinja

     37  clear;history
     38  ./llama-server -hf Qwen/Qwen3-VL-30B-A3B-Thinking -c 0 --jinja --port 11434
     39  ./llama-server -hf Qwen/Qwen3-VL-30B-A3B-Thinking.gguf -c 0 --jinja --port 11434
     40  ./llama-server -hf Qwen/Qwen3-VL-30B-A3B-Thinking-GGUF -c 0 --jinja --port 11434
     41  clear;history

Then pointed my browser at http//:localhost:22434 on my local box and had the normal browser window where I could upload files and use the chat interface with the model. That also gives you an openai api-compatible endpoint. It was all I needed for what I was doing that day. I spent a grand total of $4 that day doing the setup and running some NLP-oriented prompts for a few hours.

I am usually just running gpt-oss-120b or one of the qwen models. Sometimes gemma? These are mostly "medium" sized in terms of memory requirements - I'm usually trying unquantized models that will easily run on an single 80-ish gb gpu because those are cheap.

I tend to spend $10-$20 a week. But I am almost always prototyping or testing an idea for a specific project that doesn't require me to run 8 hrs/day. I don't use the paid APIs for several reasons but cost-effectiveness is not one of those reasons.

How is it? I'd guess a bunch of the MoE models actually run well?

I've personally decided to just rent systems with GPUs from a cloud provider and setup SSH tunnels to my local system. I mean, if I was doing some more HPC/numerical programming (say, similarity search on GPUs :-) ), I could see just taking the hit and spending $15,000 on a workstation with an RTX Pro 6000.

For grins:

Max t/s for this and smaller models? RTX 5090 system. Barely squeezing in for $5,000 today and given ram prices, maybe not actually possible tomorrow.

Max CUDA compatibility, slower t/s? DGX Spark.

Ok with slower t/s, don't care so much about CUDA, and want to run larger models? Strix Halo system with 128gb unified memory, order a framework desktop.

Prefer Macs, might run larger models? M3 Ultra with memory maxed out. Better memory bandwidth speed, mac users seem to be quite happy running locally for just messing around.

You'll probably find better answers heading off to https://www.reddit.com/r/LocalLLaMA/ for actual benchmarks.

I suspect that is true for standardized tests like the SAT, ACT, or GRE.

I suspect in classroom environments that there isn't any intent at all on test timing other than most kids will be able to attempt most problems in the test time window. As far as I can tell, nobody cares much about spreading grades out at any level these days.

What if instead we are talking about a paper or project? Why isn't time-to-complete part of the grading rubric?

Do we penalize a student who takes 10 hours on a project vs the student who took 1 hour if the rubric gives a better grade to the student who took 10 hours?

Or assume teacher time isn't a factor - put two kids in a room with no devices to take an SAT test on paper. Both kids make perfect scores. You have no information on which student took longer. How are the two test takers different?

My kids use personal computing devices for school, but their primary platform (just like their friends) is locked-down phones. Combining that usage pattern with business incentives to lock users into walled gardens, I kind of worry we are backing into the destruction of personal computing.

How strong is the argument that a student completing a test in 1 hour with the same score as a student who took 10 hours that the first student performed "better" or had a greater understanding of the material?

It was amazing how often people wanted to optimize stuff that wasn't a bottleneck in overall performance. Real bottlenecks were often easy to see when you measured and usually simple to fix.

But it was also tough work in the org. It was tedious, time-consuming, and involved a lot of experimental comp sci work. Plus, it was a cost center (teams had to give up some of their budget for perf engineering support) and even though we had racks and racks of gear for building and testing end-to-end systems, what most dev teams wanted from us was to give them all our scripts and measurement tools to "do it themselves" so they didn't have to give up the budget.

I like the Qwen models and use them for other tasks successfully. It is so interesting how LLMs will do quite well in one situation and quite badly in another.

I think the crack house comparison is entirely appropriate. The brain is weird . . .