clusterhacks

I have experience as a coach in middle and high school in the US. I can say emphatically that school sports are relatively low impact in terms of time opportunity cost and individual family spending. The "club" or "travel" team private organizations are where the money and time really go.

I am also a parent of a kid who participated with a soccer club team for many years. She is a multi-sport athlete who loved several sports and we found the club experience in soccer actually quite good for her emotional and physical health. I will say that family experience with club sports varies based on the quality of the organization. She also played with her club in falls and her other sport in the spring at high school, so she got to do both. This was fun for her and us.

Assume a kid just wants to get better at some sport. I'm a player development nerd and believe most kids would be better served with 1-1 training with a much lower time investment that just joining a club or travel team. I do 1-1 sessions with players for 30 minutes 2x/week for 6-8 weeks in the fall. Players, of all natural athletic levels, will make about two years of progress compared to just participating on the spring team. That's with one whole hour per week of time invested. I love working with players this way because I am measurable-metric driven, so a kid can see their progress. I'm also a little bit qualitative about it too, because sometimes, the goal is just to build confidence and comfort.

The approach I'm describing looks like lots of hobbies. Imagine a kid wants to learn piano or guitar or drums. It's basically the same abstract approach. Work with an experienced teacher, commit to a regular+reasonable practice schedule, repeat for 6-8 weeks, and see if it fits and feels good.

The social aspect of being a good club or travel team is nuanced, but I will just say that my kid was incredibly well-served by having a good group of friends not connected to her school. And she had a great group of friends in school.

Running locally for me is mainly about learning, maintaining control+privacy, and helping shift my coding+design process to leverage LLMs. I guess if you made me boil it down to a single word to justify the cost I would just say: tuition.

Sounds like we have similar boxes - mine has a 10 core CPU, 64 GB of ram, and a 2070 Super. My motherboard had two unused PCIe3x8 slots and doesn't support Blackwell GPUs. I bought a couple of brand new Ada generation RTX 2000s with 16GB of memory for under $1400 to get to 40 GB of VRAM. That will easily run Qwen3.6-27b at a 6-bit quantization and 80,000 token context size. It isn't fast (19-21 t/s), but using pi-coding-agent is fine.

Now, my instinct is that I am giving up SOTA performance on agentic coding with this setup and LLM. But the gap between my setup and SOTA commercial models is small enough that it doesn't matter to me.

On the one hand, sure, why not have a default install throw a bunch of bells+whistles via skills and extensions.

But I like pi precisely because it is so minimal. I want understand and work around the simplest possible agentic coding setup, find the sharp edges, maybe even improve my prompting ability. And doing all three with a locally hosted LLM.

At some point, if I don't understand the foundations, am I just punting on actually thinking about what I'm doing?

Of course, making individual choices about how to do agentic coding are precisely just making individual choices. People should do what makes them happy and productive.

This may be too naive, but I created a user on my linux box who doesn't have very many permissions. Then I sudo to that user, use firejail to start pi in a dev project directory, and let it have at it.

My projects are usually very limited with respect to external dependencies and that is part of prompts or markdown files describing various project goals, plans, and current state.

My operating theory is that this probably won't get my systems borked. I wasn't patient enough to dig deeper.

FTA's conclusion:

"Is this decline a distinct change from the recent behavior of the labor share in the U.S.? Along the two key dimensions we investigate, our answer is no. <later> ... and they provide little evidence that it will evolve differently from past episodes."

This conclusion seems to be against "this time is different" arguments. Should we be generally encouraged by similarity to past declines pre-2000 or bearish and think that there is more drop to come like the 2000-2007 and 2007-2019 periods they graph out?

I guess there is no way to predict other than check back in after time passes.

Huh. Same problem, and I run with llama.cpp. In my case, Gemma4-31B (4-bit quant though) will just stop sometimes.

Gotcha. I'm past the point of having any confident thoughts about what happens to their share price at IPO.

What about the idea that there is a high likelihood that the potential share price for OpenAI and Anthropic are both going to be pretty divorced from a rational market price for either?

I used to agree with you but now do not. I now think the floor for this market is probably no worse than the annual revenue of cell phone plans in the US market. So say, $250 billion.

Now, that probably doesn't justify the valuations and hype being thrown around, but I think it gets at a real revenue number.

I also don't know how that number fits into the funding rounds already raised and VC dreams of IPOs for these two.

This isn't coming from deep analysis on a verifiable source, but I started asking people in my social circle (includes white-collar and blue-collar folks) about their LLM use. The biggest surprise in 2026 for me was that almost all of these people told me about regular (and sometimes sophisticated) use.

A more intriguing observation - I work on the side with high school students and have two college kids of my own. Their LLM usage (and their peers) is much, much lower than expected . . . that's a little counterintuitive given "popular" perceptions I read.

--what this means for the valuation of the AI companies

Probably nothing. Most users have no idea what an LLM is or how it runs. Anecdotally speaking, I see many LLM users default to whatever their day job provides to them. And even slightly more sophisticated users seem ok with paying for their openai or anthropic subscriptions.

Maybe we will see a small but dedicated group of open weight model users who prefer local llm, but everybody else will just consume from the big providers? The scenario might look something like OS choices today - a small, committed group of Linux users vs the vast majority of other users running Windows, MacOS, or Chrome?

Appreciate the anecdote and your other comments on HN. But I strongly suspect you are incredibly atypical based on your background and previous work experience in ways that would tremendously down weight the probability that any part of your experience with recruiters would apply to even above average engineers.

Check out the Taulbee survey results:

"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

"I personally dropped $20k on a high end desktop . . . "

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 can spot a person's social media app of choice is in 5 minutes.

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.

Very cool - thanks for the info.

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 . . .

Good grief. I'm here cautiously telling my workplace to buy a couple of dgx sparks for dev/prototyping and you have better hardware in hand than my entire org.

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?