Badabinski

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.

The segmented caching request thing is… weird. I worked for a company that developed a caching proxy and it very much did not work that way. Like, random access in a caching system is usually kinda bad and you should try to avoid it. Like, our proxy manually controlled the disk (it wasn’t a mounted filesystem) so it could constantly sweep the head across the disk and cue up reads and writes optimally. This gets much harder when things are fragmented as fuck.

If the concern was about what would happen with multiple connections for the same cache miss, then the caching proxy should just combine the client-side connections into a single upstream one. You can still cache the first part of the response if your upstream connection gets terminated and then restart it from that point.

ASA can still warp, but an enclosed and warm build chamber should do a lot to squelch that. Are you printing on an open bed slinger? Or do you already have a build chamber?

I don’t know why, but 🫩 has been bugging the shit out of me recently. There’s something about the way it’s used that really irritates me.

God, the fucking comment spam drives me absolutely fucking nuts. I used to enjoy reading 2 consecutive changes with 12 lines of comments because it meant I was in for a hell of a story of woe and misery. Now, it’s just the fucking slop machine doing its thing.

With regards to LLMs being good enough to do our jobs, I don’t think that’s ever going to happen. Token prediction is a neat trick, but you actually need something that can reason and understand to replace human intellect, and nothing I’ve seen on the horizon appears to be capable of that.

Yes, there’s far more code to review and the reviews are extremely fucking frustrating and I can tell who is using an LLM based on the volume and texture of the shit they’re pushing out. You have to check everything excreted by an LLM far more thoroughly than if it had just been written by the senior dev who produced the slop. An LLM is incapable of reasoning, it’s just choosing likely tokens based on past context, so nothing it produces can actually be trusted.

Source—I’m a senior dev at a large software company you’ve absolutely heard of. I am drowning in slop.

Yeah, it’s so fucking frustrating. I felt conflicted writing this because I don’t want to reduce anyone’s resistance to the garbage being pushed by the big corpos. We should be saying “no” as strongly as possible at every encroachment. I just also don’t want essential research to also take the blow. A lot of environmental research benefits from satellite imagery, and anything we can do to glean more information from that is a good thing.

Damned if you do, damned if you don’t. You can’t really expect the average person to learn the distinction between the good and the bad here. You can try to educate folks, but people already have enough shit on their plates as it is.

This is why “AI” is such a shit term. This is not a general purpose generative model, which is what you (and me) should (and very clearly do) dislike.

This is a model that is designed to operate on a very specific set of data and extract information from it. It was created by people at the University of Cambridge, not one of the big shitty companies. It’s not something that you run all the time, it’s something you only run when gathering data for research purposes. The model was trained on truly freely available data. No nonconsensual large-scale scraping was used to train this model, so it’s free of the ethical concerns typically associated with “AI”. Since it’s something a research group would run by themselves on a single (albeit very powerful) machine, it has very modest power requirements.

Models like this have been around for at least 15 years in the research space, and they don’t deserve your ire. It’s one of the truly good uses of ML.

If you want more details on the system, it’s all open source and can be found here: https://github.com/ucam-eo/tessera

EDIT: Please don’t take this as me trying to defend LLMs and image generators. I fucking hate LLMs and image generators. People at my workplace have described me as “the anti-AI guy” because I really am. I think almost all of the ML products made by OpenAI, Anthropic, and others are unethical and also just shit.

I gave it no advice, and all I wanted it to do was generate a script to tell me the file type of the newest file in the current directory. It was a very trivial piece of code. Each time it generated something I disliked, I told it “don’t do this, reference this guide for the correct thing to do,” or “don’t do that, do it in such a way that X happens.” It was like 20 lines of bash in the end.

I was expecting it to write me a bash script because that’s the example that everyone, without fail, says will work well. “I just used Claude to write a little throwaway script to move some files around” were the exact words a colleague used.

Bash is a shitty, unsafe language. I don’t write large programs in it. I expect “throwaway scripts” to still be written in a way that defends against all of the innumerable shitass foot guns present in the language. Claude was incapable of doing this in a reasonable time frame.

I also dislike the Python and Go it generated, while we’re at it. It produces overly verbose, overly documented, poorly performing code. It was also fucking dog shit at referencing runbooks and documentation in a local folder when I was on call and responding to alerts.

It sounds like you’re quite partial to Claude, and I hope it’s been a very good and helpful tool for you. I did not find it to be particularly helpful for me. It was very good at putting me in a sour mood, however.

Shellcheck, while good, doesn’t capture all best practices in my opinion. There are many items in that doc which shellcheck would happily allow, worst of all being set -euo pipefail.

I’ll say that during a recent week where I was forced to use an LLM, I found Claude Opus to be extremely poor at referencing this guide: https://mywiki.wooledge.org/BashPitfalls

it took almost an hour to get Claude to write me a shell script which I considered to be of acceptable quality. It completely hallucinated about several of the points in that guide, requiring me to just go read the guide myself to verify that the language model was falsifying information. That same task would have taken me about 5 minutes.

I believe that GIGO applies here. 99% of shell scripts on the internet are unsafe and terrible (looking at you, set -euo pipefail), and Claude is much more likely to generate god awful garbage because of the inherent bias present in the training data.

And as for unit tests? Imo, anything other than property-based testing is irrelevant. If you’re using something like Pydantic, you can auto-generate a LOT of your tests using the rich type annotations available in that library along with hypothesis. I tend to write a testing framework once, and then special case property tests for things that fall outside of my models. None of this is super helpful for big ugly codebases with a lot of inertia around practices, but that’s not been my environment, thankfully.

Where’s the CVE? Was there an attempt at responsible disclosure? Was confidentiality breached? Did they coordinate this release with the devs like the dirtyfrag people did? This “announcement” doesn’t answer any of these questions and I am frustrated by it.

EDIT: Ok, there IS a CVE: https://security-tracker.debian.org/tracker/CVE-2026-46300

There are far more bountiful resources to be found, including GitHub issues with concrete examples. I picked that one because I know that all four of the companies listed are problematic. I do not currently have the time to find more detailed links, but they’re out there.

Here’s just one sample that came up when I searched for “3d printer GPL violation”

https://www.th3dstudio.com/2018/04/05/gpl-violations-in-the-3d-printer-community-creality-anet-tronxy-zonestar-and-others/

Current sodium chemistries have a kinda shitty voltage curve. I expect it will get better, but right now a LOT of the power delivery happens with voltages below 3 volts. LFP batteries deliver most of their power at higher voltages which lets you use thinner conductors and cheaper/more efficient electronics.

Again, not saying that it’s necessarily an inherent flaw in sodium chemistries, just that the current generation that people can access and test right now is unsuitable for some tasks.

If you get some PETG V0 or ABS V0, that will be perfectly safe and probably up to code. That shit can self extinguish when it catches on fire.

set -e: Exit on error. Very useful, but notoriously weird with edge cases (especially inside conditionals like if statements, while loops, and pipelines). Don’t rely on it blindly as it can create false confidence. (Pro-tip: consider set -euo pipefail for a more robust safety net, but learn its caveats first.)

while I appreciate that the author mentions how weird this is, nobody is going to learn all the caveats correctly. Don’t use set -e. Don’t use set -e. Don’t use set -e. It’s a shit ass broken ass fucked feature that half of nobody understands well. Here’s a great wiki page explaining why it’s trash: https://mywiki.wooledge.org/BashFAQ/105

People like Go, and Go requires you to manually and stupidly handle every possible error case. Why not do the same for shell? It’s really quite easy:

#!/usr/bin/env bash
echoerr() { echo "$@" 1>&2; }

die() {
  message="$1"; shift
  exit_code="${1:-1}"
  echoerr "$message"
  exit "$exit_code"
}

temp_dir="$HOME/tmp"
mkdir -p "$temp_dir" || die "Failed to make persistent temporary dir $temp_dir"
lc_dir="$(mktemp -d -p "$temp_dir")" || die "Failed to make target dir in $temp_dir"

Look at that, descriptive error messages! And it doesn’t depend on a shell feature that is inconsistent between versions with no good documentation about all of the fucked up caveats.

deleted by creator

So uh, OPNSense?

Ed Zitron seemed to estimate that it was probably 4-5x the current going rate, so a $20 ChatGPT subscriber likely costs OpenAI $80-100.