Aurornis
This is the kind of bet where I would take the opposite position without hesitation.
The only thing I could see them removing would be models that are specifically trained to do bad things, like a model trained to produce illicit underage image content. And I would be totally understanding of and encouraging of that, even if it makes the free speech absolutists unhappy.
I think the uncensored and abliterated models are more hype than actual threat. The journalists and politicians are too busy going after the AI labs every time someone does something bad and the police can find ChatGPT logs before it. I doubt we’re going to find a situation where someone was running local models and also keeping logs of everything and also commits a crime and also the journalists find it and also they make a story that catches on and so on.
It’s such a niche thing it’s just not a priority. There are some open LLM people who are treating it like a crisis before it happens, but come on. It’s the internet. We can download gigabytes of pirated TV shows despite that industry and lawmakers working hard to stop it for decades. If open models got banned somehow, you’d be able to download them the same way.
Popular harnesses have mature enough remote access features that I can use the same interfaces and even mobile apps.
If you’re not familiar with Docker, you can even have an LLM set up the environment for you and help you start and stop the container. If you do this, be very sure that you switch to having a harness run inside the VM or container, not having the harness on the host machine run commands inside the container. If you do the latter style, it will be running commands on the host which is what you want to avoid.
That’s what DevOps meant where I worked.
I remember the separation of developers from deployment being one of the things DevOps pushers wanted to reduce.
In fact it was the beginning of my career when the older senior people were already trying to get away from the assembly line style of software where each person worked on their part and then tossed it over to someone else to become their problem.
For years the consensus on Hacker News was that the ride share companies were a bubble and it was all going to collapse. Nobody would pay profitable rates once the early discounted rides ended, everyone said.
Then the rideshare companies became sustainable and they’re accepted as a normal part of life.
Companies are liable for debts. I don’t know why you would conclude they’re not. If a company can’t or won’t pay their debts, they go into bankruptcy, which has significant consequences up to the company losing its assets to the creditors.
Lenders are not dumb. They collect interest in exchange for the risk of lending. This is the symmetry that you’re not seeing. The lenders can also structure their lending in ways that if the company can’t pay, the lenders get assets from the company in exchange.
LLCs apply at a lower level (not AI lab scale), and shielding individuals from downside risk is a feature, not a bug. It’s by design. There are a lot of countries where the only way to get startup funding involves signing your personal finances away in the event of failure. Financial ruin if the company doesn’t work out, aside from some minimal protections. Do you know what the small business and startup environments are like in those countries? Abysmal. Nobody can afford to take the risk, so the big establish companies are the place to work.
Financial failure of an AI lab wouldn’t make that AI disappear.
If people depend on them (aka demand) the IP of the labs would be valuable, someone would purchase it in bankruptcy, and they would continue serving the model.
There is basically no comparison to the credit market collapse, even though all of the simplified comparisons try to draw a straight line between the two.
The real concern is different: The theory is that if the frontier labs start collapsing, they will argue that their failure is equivalent to the US losing AI supremacy, which is equivalent to losing the future if you believe AI is winner take all. The argument is that they’ll be financially reckless and then ask the government to backstop losses if it falters, both to retain their lead and to protect the overall economy.
The other part that of this is the increasingly widespread belief that AI is a total bubble without demand and it’s all going to collapse because there’s no profits to be found at all because it’s all financial theater. That argument is harder to make if you’ve looked at the actual unit economics of these companies (hint: Anthropic isn’t subsidizing inference like so many believed). AI is not going away and the demand for compute will only increase. The question is how accurately these scale ups track it and if they’re going to dip below the insolvency threshold from getting it wrong.
But if the companies blow up, the technology doesn’t go away. The model weights aren’t deleted. The ML engineers don’t retire. Companies get restructured, IP assets can be sold to new operators, and engineers move to new companies with new business models.
You said “higher education” in your original comment, but it appears you really meant elite institutions?
“Higher education” doesn’t mean the best universities. It means post-secondary education, aka college. Any college.
https://en.wikipedia.org/wiki/Higher_education
When discussing falling admission rates you also need to consider the rising application rates. In the 1980s it was rare to apply a top university and students applied to relatively few universities at all.
For today’s students the strategy is to spray applications across a large number of schools and choose later. Even when I was in high school we were advised to apply to several reach schools (where our applications were probably not enough but it was worth trying) and a number of backup schools (where everyone was accepted) in addition to a selection of colleges in our range. That’s one reason why rejection rates are rising.
I don’t know where you're getting this. The complaint is that too many people are going into higher education where they shouldn’t be, getting expensive educations for careers that don’t need it.
An oversupply of education, not undersupply.
Generic college degree does not get into the upper crust. It’s a basic signal that someone is able to follow a program, stick with something for a few years, and complete some work. Sounds basic but when you’re hiring people it’s a valuable signal.
For most people trying to send that signal, getting scholarships plus in-state tuition is the right answer. A good enough college education doesn’t have to be expensive.
To get into “upper crust of society” range you can’t treat college like a checkbox where any college will do. The education does actually matter. The cynical hot takes about college being only about networking and a piece of paper are wrong at the upper end. The educational programs at the upper end are hard. They’re demanding. You’re going to have to learn something to graduate. People do actually fail out or give up, which is rare at the generic programs.
Even if you don’t use much of the education of one of those top programs (like if you study computer science and then take a finance job) you’ve learned how to learn, how to get hard things done, how to manage yourself and your time, and how to follow through on something hard that most people cannot do. That is the real signal of a top education program if you’re going to discount the education part.
There’s always sneering about how someone knew an Ivy League grad who was actually the dumbest person they know, or anecdotes about how the smartest programmer I ever met had no college degree, or other anecdotes they try to disprove this. They’re missing the point that it’s not a 100% perfect signal nor the only way to be good at something, but it is a strong heuristic that someone is at least equipped to set goals and accomplish hard things.
Many people believe college is not worth it for everyone, but believe a college degree is important for the job they’re hiring for.
This statistic also includes people who think college is too expensive for what it delivers, but that the value of a college degree is still nonzero.
There is yet another level of people who are frustrated with how academically weak and unchallenging common colleges have become, or how many students are going to 4-year degree programs to study topics with no job applicability. The answers would change completely if you asked people if a history degree from a $60K per year private college was worth it, compared to a Computer Science degree from Stanford. The question “is college worth it” is too generic to mean anything now that it could mean anything from a $200K piece of paper without real job skills, or a golden ticket into the recruiting funnel at FAANG.
This creates the discrepancy between polls like this and actual revealed hiring preferences. Many will tell you they don’t think college is worth it but will prefer applicants will college degrees.
I’ve been using Qwen3.8FN and DeepSeek. I would not trust them to evaluate 4 different libraries from scratch unless I gave them extremely specific benchmarks to run. They’re very good at consuming tokens for long stretches and then giving you what looks like thoroughly researched output, but their intelligence just isn’t at a level I trust.
But really, the difference you’re describing is easily shaped by prompting. If you want Opus to download and compare the libraries, just tell it to. It will. All of the models benefit from being clear in your intentions. If you try to find models that will read your mind where spending 20 extra seconds to type out your intentions would accomplish the goal, you’re ignoring the very important intelligence and efficiency differences between models.
> Look, all I actually need out of a model is things like "tell me where this distributed algorithm might break", "get this ancient C code to build on modern Debian with modern dependencies", or "write a plan to migrate this database from MySQL to PostgreSQL and to test that the app still works." The modern open models can do all that, no problem.
I actually read the code output for projects that matter. There is a huge difference between a model that can eventually accomplish a shallow goal like “fix this bug” and a model that does it cleanly. Even with Opus or Astra I will spot cleanup issues frequently.
When using models down to Qwen3.8-FN or DS-Flash size, the code can feel like the kind of spaghetti mess that you get from an overly caffeinated and confident junior developer who accomplished the goal via brute force and staying up all night. Yeah it compiles and does the thing you asked, but all of that spaghetti code compounds with each change until a couple months later your harness is spending half a day and countless tokens doing simple tasks because it has to navigate the mess of a codebase.
That problem is much less of an issue at the Opus level and I can trust Opus to clean up better when directed to do so. It’s those differences that compound.
But if I’m doing a throwaway task in a sandbox that just needs to work for a day? I’ll throw it to the open models and see what they can do. They are interesting to play with.
The article is correct that the major labs are not benchmaxxing, but I think it overstates their focus being on something else.
They don’t play the benchmark manipulation game because they’re comfortably in the lead and they have their own teams producing internal benchmarks and targets that are representative of what their users expect.
Your experience with the open weights models is not missing anything. Nearly all of them are disappointing if you set your expectations according to where they fall in the benchmark charts. They can be useful for some tasks once you’ve developed an intuition for their capabilities, but you have to start learning their relative cost per task and their limits, which takes a lot of observation and honestly might not be worth it.
One of my unpopular open weight model hot takes is that, right now, OpenAI’s Luna model is often cheaper per task than even DeepSeek and others (paying API prices, not based on the fluctuating subscription prices).
The real advantage of open models has always been that you can run them on your own hardware or a rented server and have more control over where your data goes. Some of the hype about them being 3 months behind frontier models or people making claims that 27B local models have replaced their Claude subscription become extremely questionable if you actually use both open weight and frontier models frequently.
Is that chat messages that involve tool calls (web search, file reads, etc) or actually just messages?
When I'm on the $20/month OpenAI plan, I can get about 3-5 turns out of Astra before the 5 hour limit is up.
When Fable was on the $20/month Anthropic plan, I remember getting about the same.
3-4 turns out of the $20/month plan would seem about par with other providers for their best model, but really Carbon should occupy that slot, not Argon.
If Argon's limits are that low, it wouldn't make sense to use that plan for anything when models of similar intelligence are available with much higher limits on other $20 plans.
I gift meals to people in other cities when they're in need (sick, childbirth, death in the family) and I'm not nearby to help.
I've had intense working situations where an extra hour of work was worth paying someone $30 to deliver my $100 of groceries.
We've frequently used DoorDash and other services for small business meetings where we wanted to provide lunch, or in some situations where we had to stay late (security incidents, etc) and wanted to provide the team with something better than pizza delivery.
However it's also scary to watch some people who will DoorDash whenever they feel like it without regard to the cost. I watched some eye-opening conversations among college students comparing how much they had spent on DoorDash in the past year, even though they were not in a financial demographic that should have been indulging in delivery.
It's a good system for aligning the costs with the people who use the service.
If you thought all tax money went into a big general bucket and the government decided what services to offer out of that, municipal garbage pickup does not work that way.
There couldn't be a free delivery service because the usage would be all over the place. Someone DoorDashing burritos for breakfast lunch and dinner 7 days a week would use it 20X more than someone doing their once-weekly grocery order. Subsidizing it would incentivize a lot of waste, traffic, vehicle usage, and other things that we actually don't want to incentivize people to use more of.
The only way this could work is with a substantial per-delivery fee, which gets us right back to what we already have with DoorDash.
The details are much less dramatic: The company doing the work messed up on a part of the approval process that was supposed to be completed first. It’s technically possible that the environmental impact review would come back with some obscure exception that required them to revise their plans, but it’s unlikely that anything was going to stop those trees from being cut down.
The 250K trees headline appears dramatic if you don’t know that Finland is covered in trees and cutting down a lot of them is part of building anything.
The moderation style in the latter years was very different than at the beginning.
It was always heavily moderated, but after some point it felt like there were more layers of moderation and rules and cultural expectations added every year.
Moderation went from being a background cleanup operation that quietly kept the site in line to being the primary thing I expected to see when following a StackOverflow link from Google. In the later years it felt like every other StackOverflow search result was going to be the question I had followed by a moderator shutting it down for some reason before anyone could answer.
The moderation wore me out and I wasn’t even posting questions, just trying to find answers!
Rates are rising in other countries, too. Global phenomenon.
The rate of cancer deaths is actually growing faster in several other regions like the UK
> The authors saw the sharpest increase in incidence rates among the 45-49-year-old age group, with an increase of 12.76% annually from 2019-2023. They said that this increase is likely driven by more people undergoing colorectal cancer screening after national recommendations lowered the age to begin screening from 50 to 45.
So the rates for 45-50 year olds can’t be directly compared in the US.
The strange part about the change in colorectal cancer rates around the world is that it’s showing a large increase in much younger people. I’ve read some studies where the sampled time period showed cancer rates declining for elderly people while they increased for very young people.
I wouldn't use it as a 100% infallible AI detector when the stakes are high (accusing someone of cheating) but it's very good at detecting AI generated text when you can drop large sections of text into it.
A 16" M3 Max with 128GB of RAM and no other upgrades would have been $5000 at launch.
They started shipping at the end of 2023, so you couldn't have bought a refurb.
Either you're remembering the price wrong or you got a crazy deal.
Most people don't pick one thing and put all of their time into it. They balance priorities.
You don't pick one and ignore the others.
Even the most family-minded people I know enjoy having something to work on outside of family time.
However the standard for performance is relative to reasonable expectations for someone in that role. Typically relative to peers within the company. If one person is unable to do their job to within a reasonable level relative their peers even with reasonable accommodations, accommodations do not require the company to continue employing them anyway.
I’m just old enough to remember some older adults in my life who refused to use email. They would only talk to coworkers in person or on the phone.
It did not work out very well, unless they could afford to retire at that point. When your career evolves, the only way to keep participating is to evolve with it.
For this person’s job at an academic institution, I doubt it. This specific case feels very symbolic from start to finish: His management probably mandated AI use because they felt the need to do something about AI, and he used this as a perfect opportunity to stage a protest against AI. Knowing what I know about academic institutions, I suspect AI would have very little impact on productivity.
Many jobs might be the same: AI’s productivity impact can be large on a small set of tasks, but if those tasks were only 10% of someone’s hours at work then a couple people refusing AI will not make a difference to the whole department.
Workloads also flow to people who are good at that work, so the work that benefits from AI would be distributed to the AI users and more of the non-AI work would go to the few people who don’t use AI. It can be self-balancing when the number of refusals is small.
There are jobs where it’s not going to work out, though. If most of someone’s job is accelerated a lot by AI and they refuse to use that accelerator, it’s going to be really hard to avoid failing behind.
Companies do not have to retain employees who need accommodations that are not reasonably compatible with the work. AI is a hot topic and everyone has strong feelings about it, so it can help to think back to other technology leaps and consider what it would be like if your coworkers refused to use them.
What if you had a coworker who refused to use email and would only communicate on the phone? It could technically be done, but they would fall so far behind and produce enough hardship for their coworkers that it wouldn’t be a reasonable request for most office jobs. The laws do not force companies to accommodate requests that are unreasonable for the work.
I can already think of jobs where refusing to use any AI tools would put someone at so much of a disadvantage that it couldn’t be justified. That set of jobs is small today, but it’s growing.
There are also limitations on what accommodations can be requested. If you took a job mowing lawns and then had a religious belief against harming living plants, you wouldn’t collect paychecks while you watched your coworkers mow lawns. They would have to find other work for you if they could, like raking leaves or lawnmower maintenance. If they didn’t have that work, then you wouldn’t have a job.
The raking leaves example was to make a point. For a programming job you could be exempted from using AI tools to do the work, but you would still have to do the work at a reasonable standard.
Money is not unlimited though so “just pay everyone a living wage” isn’t a real solution. If you can’t afford to pay ideal “living wage” (which is an undefined target that keeps moving depending on who you ask) then your options are to either not create the job at all, or outsource the work.
So therefore, creating a job at any wage is better for your economy than not creating the job at all.
Being mindful of wages is great, but eventually you have to accept that creating a job at a less than ideal wage is better than not creating any job at all. If the pay is not enough then you’ll get the signal through being unable to hire because workers are taking better paying jobs.
An unintuitive feature of the job market is that creating more low paying jobs actually puts upward pressure on wages. Ideally the low end of the job market should be saturated with job opportunities, which creates a situation where employers have to compete with each other to raise wages to find and retain employees, which means pay goes up for everyone.
So don’t let worries about not being able to pay as much as you’d like get in the way of paying anyone at all. The alternative (less jobs, more outsourcing) is worse in every way.
My M1 is still fast and enjoyable, but I regret to inform you that the M5 actually does feel faster in day to day use. I thought for sure I wouldn’t notice, but the difference is there.
When I do large builds the difference is also bigger than I was expecting.