I wouldn’t do any of that in a CI-only system. If I did, I’d use tools that exist for those jobs that already allow scripting languages.

1. Why wouldn’t you use IaC tools for this? All the majors have Python already.
2. What build or deploy task needs to both create and reference a bug ticket?
3. What build or deploy task needs to do database lookups? Or possibly what task needs those across multiple, independent stages?

I dislike scam money as much as the next person. I do not, however, dislike cryptography. There’s a really good common in the thread about elliptic curve cryptography not having a place on Codeberg anymore and I think that’s valid. I don’t even know if OpenSSL could have a place on Codeberg with this decision. Their nonprofit, their rules. Just not very libre-minded and definitely sets a bad precedent. And before someone tells me Microsoft actively censors GitHub, this isn’t a whataboutism thread. It just reduces my trust in a supposedly open collective.

This is a boilerplate example. I asked for something more than boilerplate. Give me some reasons why I need an incredibly stateful CI engine.

AFAIK there have been no cases setting precedence for this copyright attack globally. I am also not aware of any hosting platform under government attack for hosting AI works. Similarly, the WTFPL has no precedence and its legal status is unclear (contrast against GPL and Apache precedence). However, Codeberg recommends WTFPL. I also find it incredibly hard to believe that all 650k+ projects on Codeberg have better security than AI slop.

Either be consistent or stay off the fucking bandwagon. If you’re going to make copyright a big deal, you can only advocate for licenses that have precedence. If you’re going to say shitty security slop matters, you need a real security review process and active testing+moderation. Moderation like this opens up different classifications of scrutiny and isn’t a good look for a libre platform.

Note that I think the idea is solid and agree with the reasoning. I just don’t think this is the execution that makes people think differently about AI.

Looking at the examples, you’ve just made a brand new GitHub Actions framework. There’s YAML to wrap everything together and a bunch of Python that’s so declarative it might as well be HCL. Do you have an example that’s a bit more than “do what YAML does only in bash?”

You took this from Reddit’s r/interestingasfuck and posted it to c/mildlyinteresting? Clearly neither is a good fit because this isn’t interesting at all.

If you are saying the probability of sampling something with half ones and zeroes is greater than the probability of getting 0000 0000 or 1111 1111, that is a correct statement because you’re sampling the output, not the generation. The probability of generating 1010 1010 is the same as the probability of generating 0101 0101 which is the same probability as generating 0000 0000 or 1111 1111 or 1111 0000 or 0000 1111. You’re looking at the difference between (8 choose 4) vs (8 choose 8).

However, in a truly random system, 0000 0000 will appear as often as 1010 1010. The distinction of “solid patterns” is meaningless at scale. You’re the one differentiating between the two. Now as we analyze the randomness, we would expect more samples that have four ones than have eight ones, but we would also expect an equalish number of each pattern to appear (eg 0011 1100 appears the same amount as 1100 0011 which appears the same amount as 0000 0000).

You’ve conflated the number of patterns with the number of ones or zeroes that appear in the patterns.

I’m gonna need you to explain your math here. More specifically, I want to know which bytes are going to appear more often when you are generating them in sequence with an equal probability of each digit being zero and one.

Let’s take the other track and instead assume that byte generation is uniformly distributed. In fact, let’s go stronger and assume that any binary number generated by coin flips is uniformly distributed. The base case is a single flip. We have two possibilities, head or tails, each with 50% probability. This means our resulting numbers, 0 and 1, occur with equal probability. This is the uniform distribution. Now assume a binary number of length k - 1 generated by coin flips is uniformly distributed. A binary number of length k is composed of a number of length k - 1 and a single flip. The first k - 1 parts create a range of [0, 2 ^ (k - 1) - 1] and are uniformly distributed. Let’s put our new bit at the front. A 0 gives gives the current range, [0, 2 ^ (k - 1) - 1], and a 1 gives us [2 ^ (k - 1), 2 ^ k - 1]. Note we have an equal probability of falling into either space and they are the same size. In other words, a binary number of length k generated by coin flips is uniformly distributed.

The Litter Robots I’m aware of rotate with the door at the pole, meaning the cat can always jump out. If the cat jumps in when the globe is dumping into the waste drawer, the globe does not rotate with enough force to seriously injure the cat. If you’re genuinely worried, stick your arm in and try to force it so you can get a sense of the absolute worst case scenario.

I think there are plenty of things to not be thrilled with Litter Robot about. I don’t feel this is one of them.

Your second check is exactly why someone would check if it’s AI-generated.

I check whether it is safe to install. Are there security or privacy concerns?

Let’s review some basic security: the CIA triad stands for Confidentiality, Integrity, and Availability. Confidentiality is never a guarantee with AI-generated systems because the developers are usually spending even less time thinking about the code than normal since AI does the thinking. Plus AI systems are getting owned left and right (litellm anyone?). Integrity is never a guarantee because the developers don’t understand the system the AI slopped together and AI is only good at unit tests in some cases, not integration or end-to-end. That requires a system perspective. Finally, availability is usually worse with AI slop because AI is trained on really bad software that is rarely optimized. That requires vertical scaling out of the box.

Looking at this codebase, the integration cover hits three services and not totally at that. There are no security tests. There are no published security findings. There are no security standards in the contribution guidelines. While there is a disclosure process, there are no automated baseline tests available.

So why exactly did you move beyond your second check? This project has no security. Remember, that’s your guideline even before constructive criticism.

Edit: I just realized you’re the maintainer and you’re yelling at someone for asking about AI stuff when you can’t be bothered to do basic security. Worse yet, you’ve attempted to hide your slop instead of making users aware of the extra security issues. You have to understand I wouldn’t have commented on this if you hadn’t included a basic check you went out of your way to screw up. Glass houses and all that.

I don’t think you’re using straw man correctly.

You’re naively referring to how consensus should work while completely ignoring both the well-defined attacks I referenced and the reality of large actors in a consensus network. You don’t know what you’re talking about or you don’t understand how the theory works or you’re possibly just being obtuse. No matter what, this is pointless. Good luck.

If login tokens are stored on a public ledger replay attacks write themselves. Public or private, keeping every login token ever is a horrible audit mechanism and doesn’t scale well. At scale, speed to generate becomes a concern. Not at scale, something lighter is faster.

A normal database scales better than a license blockchain and doesn’t require extra computation to write. Audit logs and hashes prevent extra edits. License files signed by a central authority don’t require a database and the central authority is functionally equivalent albeit less expensive than a blockchain.

I am still interested in a good use for the tech. I have yet to see one that is genuine.

If any of it is rewritable, none of it is immutable. You can’t have it both ways.

“You just don’t know” doesn’t answer my question. A private blockchain, by design, is already owned by its largest actors.

I don’t know that it is, though. Can you show me form of blockchain in the real world where this doesn’t apply? Saying large actors can’t affect a specific piece of internet technology, so far, is rather like teaching physics without friction. It’s nice and fun and easy to understand but completely ignores the reality of any implementation.

Go ahead and prove me wrong. Show me blockchain implementations that are immutable post append. On my end, we can talk about Bitcoin forks. We can also talk about the current state of consensus mechanisms, each of which has the explicit ability for large actors to rewrite history in their favor. Even Monero is susceptible because this is fundamental to the blockchain in any form. It’s been a huge reason why I make sure I get paid up front for any consulting I do in this space.

What exactly are some of the use cases for an infinitely growing, append-only database built primarily so its largest users can rewrite history at will?

The pyproject.toml spec is ten years old. Python 3 is almost twenty years old. The community standard moved within the last five years. Tox is only necessary if you want to validate across specific versions so it can be replaced by tools like Poetry. If you’ve got GitHub workflows that’s the standard anyway so you should be running something like act locally. Static typing redundancy is a waste of compute.

If you’re going to be an asshole, make sure you can back everything up. You can’t so I’d recommend taking a breath and going outside.

Edit: I looked through your GitHub and even though your profile name is “msftcangoblowm” you don’t seem to use .yaml but instead the YAML extension Windows devs use.

What the fuck? The only task that didn’t degrade across most models was Python. Very basic things like JSON, Makefiles, and schemas got screwed. Fiction, emails, and food menus got screwed. Did you even bother to read the legend? If you consider a single pass to be “producing and manipulating language” you didn’t bother to read the idiotic article you started this thread in support of. Good luck.

Edit: why do you lie?

Catastrophic corruption (80 and below) occurs in more than 80% of model, domain combinations.

Sorry, I assumed you would have actually read the DELEGATE-52 study linked instead of just the abstract. For “a model optimized for replacing StackOverflow” that is “better at writing papers than most students” LLMs sure did pretty bad at those tasks over multiple rounds.