Badabinski

Is this real?

OpenAI has a trillion dollars in financial obligations they need to meet by 2030. I doubt Clear Channel’s financial obligations were in the same order of magnitude.

I’ll say that given the way OpenAI and Anthropic have hideously overextended themselves (they have over a trillion dollars of financial commitments to companies like Oracle), it’s not impossible that the current crop of American LLM providers do just kinda… poof away. Traditional banks want nothing more to do with them, they’re getting majorly spooked. All that needs to happen is for private credit to lose confidence in them, which is already happening. When they’re out of cash, they’ll be on the hook for an absolute ridiculous amount of money and they’ll probably just get liquidated.

I’m sure there will be new companies that pop up, but they’re going to have to charge 10x what Anthropic is making enterprise customers pay, since inference likely still isn’t profitable at the price Anthropic is charging.

I don’t use LLMs as a knowledge base because if the problem is bad enough for me, I’m likely just grepping through kubernetes source code or something. That being said, I don’t necessarily have an issue with folks using an LLM that way as long as they fully understand exactly how bad it is at what it does. You’ll be fine if you lose access to LLMs, and that’s the number one thing in my book. Your friend? Not so much.

(As a fun bonus to all of this, Oracle is very likely to die if OpenAI can’t meet its commitments. Either way, Larry Ellison will probably stop being a billionaire, since almost all of his wealth is in Oracle stock).

The speed and ease at which LLMs allow you to generate code is a bug, not a feature in my opinion. In my org, a group of 3 very junior engineers wrote a 5k line shell script for building k8s clusters according to our business specs and it’s fucking awful. The actual time to get it out the door was short, but now it’s basically impossible to change it without fucking up like 20 different things. The fucking thing will randomly quit because the shit ass LLM thinks set -e is a good thing to use, and it’s full of unused variables everywhere. I had to add a feature to it (which is how I learned of its existence), and I spent a miserable week just reading the entire fucking thing so I could ensure that my change wouldn’t cause an oil refinery in the North Sea to explode due to a butterfly-effect series of bullshit.

The frustration and toil you feel as a software dev is a feature. If something is making you mad and is taking forever to write, that’s a sign you probably need to change your approach. If you’re using an LLM to write a bunch of boilerplate, why not just eliminate the boilerplate or like, make a factory to spit out a bunch of it or something? Your discomfort is a powerful tool and you are not best served by ignoring it. Those junior devs would have written something much better if they had been forced to experience the true toil and suffering of writing a 5k line shell script.

Senior software dev here at a company you know of. I was forced to use Claude for a week at my job and it was absolutely miserable. I hate LLMs and don’t use them in any way, shape, or form. I do spend a lot of time cleaning up the fucking slop written by some of my colleagues who have no qualms about unleashing them on our codebase which is already bursting with tech debt.

Like, it’s gotten to the point where I check potential new dependencies for AGENTS.md/CLAUDE.md/Claude as a commit co-author/.cursor in .gitignore before I use them. It’s obviously not possible to avoid using code written with LLMs, but I’ve had too many fucking problems at this point, so I’m going to try.

There are very probably architectural limitations which will prevent large language models from ever getting much better than they are right now. They are most likely a dead end.

That’s the route I took. I still pray for a nice Home Assistant helm chart because I’d really like to be able to quickly bounce a Home Assistant container around between nodes and also have easier monitoring and alerting, but all the k8s options just seemed too jank.

If the benchmarks are to be believed, it’s much, much faster than juicefs.

One kinda scary thing is that for use with NFS, all writes are basically unsafe. The server says it has committed everything to disk immediately.

I love my XL. It’s been the best printer I’ve ever owned.

I saw a video where a guy just shoved a $1 carabineer into the door latch to defeat this.

EDIT: Here’s the video: https://www.youtube.com/watch?v=EF6VBnUla58

That device kinda terrifies me. Like, I appreciate that SCUBA gear is so… simple. No electronics, no batteries, just relatively straightforward pneumatic equipment. At least you’re not very deep if the compressor shits the bed.

I like the way you think. I think more of life’s second-order problems should be solved with blinding lasers.

How do you ensure that the room is empty, 100% of the time? Those disinfecting light bulbs don’t have the same level of risk as this laser system.

Nah, it’s really not necessary. I’m senior dev at a large software company you’ve absolutely heard of and I’m just as productive as my colleagues who use LLMs. My tasks usually take fewer PRs as well, since there are fewer bugs that need to be fixed.

I still don’t understand why people are foaming at the mouth about LLMs. They’re fucking awful at writing software.

Is your container using BusyBox? if so, then it’s not even real wget, it’s just the disgusting awful busybox version.

God I hate BusyBox.

Free as in speech, friend. I donate to multiple FOSS projects every month.

Regex absolutely counts imo. I love it, especially when you combine it with a parser like, say, parsimonious.

Every time I use Mcmaster-Carr’s website, I weep at the lost potential for places like Costco. I wish every store’s website was like Mcmaster-Carr.

Security Council.

Building a jet doesn’t require over a trillion dollars of capex, and selling jets is profitable. There’s solid evidence that inference isn’t profitable, and the AI labs need inference to be extremely profitable if they’re going to meet their absolutely ludicrous contractual performance targets. Oracle is expecting hundreds of billions of dollars from OpenAI by like 2030. That shit is not happening.