iocase

KMS

Thats some Kojima level shit. The Bismark would have been unstoppable if it was called the KYS Bismark

Yeah that’s very true especially since rocket scientists use metric for all of their calculations so the target audience speaking in nm is basically just pilots

It’s because if you travel at one knot for an hour you’ll travel a nautical mile. Still dumb but that’s where it comes from… And of course knot comes from knots in a rope tied to a board or bag dropped off the side of the ship to measure it’s forward speed.

I think you get wealth disparities as a result of specialization. Should a surgeon make the same as a ditch digger? They’re both important to society but almost anyone can dig ditches. Not everyone can learn to be a surgeon, and the requirements are strict otherwise people won’t trust surgeons not to kill them, which is a net negative for society if people don’t trust them. As a result it’s a small group of people who can do it, and it takes decades of dedication just to get to “I am a surgeon” levels.

Voila. Wealth disparity.

A healthy amount is fine and normal. We’re at cartoonish levels where even bare mention of the word “wealth disparity” causes anaphylaxis in some people.

There’s wealth disparity in normal well functioning societies it’s just a healthy amount. It’s like it’s normal for there to be a range of body weights in a population, but extreme levels of obesity or starvation aren’t healthy.

When the US was functioning at its best wealth disparity was present but small. There was even wealth disparity in the USSR and high wealth disparity was also correlated with the decay and collapse of the Soviets.

A bit like the word queer, I think wojacks and Pepe the frog have been taken back from them which is a good thing.

This is kind of like taking two M&Ms and squishing them together until one wins. You keep repeating it until you have the strongest M&M in the bag, then you repeat it with a new bag.

There’s a guy who does that and he mails them to M&M headquarters “for their breeding program”

I don’t know about that. The constraint seems to be context since AI struggles to hold all of the pieces for a project mentally while making changes like an experienced maintainer can. I find AI is a lot like using a credit card. It seems like free money at first until you start paying back the tech debt of a junior programmer who had no idea how to architect a project. You end up making your own legacy code base, except you were the one who made it… And because your context has long since been wiped clean from using your AI it can’t remember why it did what it did.

“Only Claude and God knew what this did when Claude made this code. Now that it’s context has expired, only God knows.”

Well it’s what maintainers are already doing. Closing community contributions or burning out. A reputation system can be games unless the requirements for contributing are so restrictive it kills contributions anyways since most people don’t have the time and energy to get 100 PRs reviewed and accepted in a gold-standard group of projects to prove they won’t suggest slop.

Honestly I think the solution is the true price of tokens being charged to end users. If it costs you $15 instead of $0.10 to generate a slop PR now you actually give a shit about how it works. I think we’re heading there soon… Anthropic already hiked their prices 9-27X, and it looks like they need at least 2 more of those hikes in a row to begin to break even.

I think the eventual mature path for AI is as a coding assistant instead of autopilot. An expensive tool you use like an intern for making unit tests or finding a persistent bug in your code.

We’ve had low code tools before that caused similar issues. Nothing like AI though… I do agree with the other commenter here that a reputation system is probably the path forward. Something like a minimum number of PRs accepted on gold standard projects or something? People will game that too though and post slop regardless…

I appreciate the feedback regarding verbosity, and I think that concern is both understandable and illustrative of the broader discussion we’re having. However, I would suggest that your response actually reinforces the systemic dynamics that I’m attempting to highlight. Specifically, while you are entirely correct that an individual response can simply be closed or ignored if it fails to meet project standards, that observation does not necessarily scale in environments where the overall volume of incoming communication increases by one or two orders of magnitude.

To elaborate, the issue isn’t that any individual AI-generated pull request, issue report, or discussion comment is impossible to dismiss. On the contrary, as you’ve demonstrated, a maintainer can absolutely review a single submission, determine that it lacks sufficient value, and reject it accordingly. The challenge emerges not at the level of individual interactions but at the aggregate systems level, where review capacity becomes the scarce resource rather than code generation capacity.

For example, if one contributor submits an unnecessarily verbose pull request, the cost of evaluating that submission may be relatively modest. If ten contributors do the same, the cost increases proportionally. If one hundred contributors begin submitting plausible-looking but low-value AI-assisted changes every week, each of which requires even a few minutes of human evaluation before being confidently rejected, the cumulative impact becomes substantially more significant. This isn’t because any individual submission is uniquely problematic but because the total review burden scales with submission volume while maintainer attention does not.

From a queueing theory perspective, this creates an interesting imbalance. Human review throughput remains relatively fixed, whereas AI-assisted content generation dramatically increases the arrival rate of new work. Once the arrival rate consistently exceeds the processing rate, backlog accumulates regardless of how efficient reviewers become at rejecting individual items. Consequently, “just reject it” is a locally optimal strategy that may nevertheless fail to address the global characteristics of the system.

Furthermore, I think it’s useful to distinguish between identifying low-quality contributions and doing so at scale. The cognitive effort required to conclude “this is not worth merging” is still non-zero. Every submission requires context switching, repository loading, architectural reasoning, verification that nothing subtle has been overlooked, and ultimately a decision. While each of these activities may appear trivial in isolation, their cumulative effect across hundreds or thousands of submissions represents a meaningful opportunity cost for maintainers who might otherwise be spending that same time reviewing genuinely valuable work or implementing new features.

For that reason, I don’t think the existence of a rejection mechanism meaningfully addresses the broader concern being discussed. Rather, it demonstrates that the cost of filtering remains attached to human reviewers even as the marginal cost of generating candidate contributions approaches zero.

In other words, I agree that you can close this thread.

Now imagine doing that another 699 times today…

I think it’s important to distinguish between descriptive and normative claims here. Nobody is disputing that AI exists, or that people will continue to use AI. That observation, while factually correct, doesn’t meaningfully address the underlying concerns regarding review burden, maintainer workload, long-term software sustainability, or governance challenges. The existence of a technology does not, in and of itself, imply that existing institutions have already adapted to it, nor does it imply that adaptation is trivial, inevitable, or costless.

Similarly, I think there’s a tendency to frame this as a binary choice between “adapt” and “die,” when in reality the situation is substantially more nuanced than that. Adaptation itself is not a single event but rather an ongoing process involving changes to community norms, tooling, contributor expectations, trust mechanisms, and review workflows. The fact that this process is occurring says relatively little about whether it is succeeding, who bears the associated costs, or whether those costs are being distributed equitably across maintainers and contributors.

Regarding disclosure, I broadly agree that attempting to prohibit AI tooling outright is unlikely to be effective over the long term. However, that observation does not eliminate the practical challenges introduced by dramatically increasing the marginal cost asymmetry between producing code and reviewing it. Even if disclosure disappeared entirely tomorrow, maintainers would still need to invest substantial cognitive effort into understanding, validating, testing, and integrating incoming changes. In other words, removing disclosure changes the visibility of AI usage but does not meaningfully reduce the review burden itself.

Likewise, while I agree that individuals ultimately attach their identities to pull requests, identity alone should not necessarily be interpreted as a proxy for trustworthiness or software quality. Trust is not merely an intrinsic property of an individual contributor but rather an emergent property built over repeated interactions, demonstrated competence, consistency, responsiveness to review, architectural understanding, and alignment with project goals. Consequently, shifting emphasis toward contributor reputation may indeed form part of a broader governance strategy, but it should not be viewed as a comprehensive solution to the broader ecosystem-wide challenges associated with increasing contribution volume.

I also think it’s worth recognizing that contributor reputation systems themselves introduce additional complexities. Reputation requires accumulation, maintenance, interpretation, and governance. New contributors necessarily begin without reputation. Existing contributors can experience changes in quality over time. Organizations can rotate personnel while preserving identities. Accounts can change ownership. Even sophisticated trust models therefore require ongoing human oversight rather than eliminating the need for reviewer judgment altogether.

Ultimately, I think this discussion benefits from avoiding false dichotomies. The issue has never been whether AI exists, whether contributors will continue using AI, or whether maintainers should attempt to inspect people’s prompt histories. Rather, the central question is how finite pools of human attention are allocated in an environment where the cost of generating plausible-looking contributions has fallen dramatically while the cost of validating correctness, preserving architectural integrity, and preventing long-term maintenance debt remains comparatively unchanged. That seems to me to be the more interesting systems question, and one that likely cannot be answered solely through increased reliance on identity-based trust mechanisms.

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To demonstrate the core issue, from now on I’ll argue with you using chatgpt. You’ll need to read this wall of AI slop, and all I need to do is copy paste your comment into my current chat and hit “generate”

I think there are actually several different dimensions to this discussion, and I don’t think it’s quite as straightforward as you’re presenting it. It’s important to recognize that technological transitions have historically been disruptive before new equilibria emerge, and while the current situation certainly creates challenges for maintainers, I don’t think that necessarily implies a long-term negative trajectory for the open-source ecosystem as a whole. From a systems perspective, what we’re really observing is a temporary mismatch between contribution velocity and review capacity. Historically, software engineering has repeatedly experienced periods where productivity increased faster than existing workflows could absorb those gains. While AI-generated pull requests undoubtedly increase the volume of contributions, that doesn’t automatically mean the ecosystem is fundamentally unsustainable. Instead, it suggests that governance models, review methodologies, contributor onboarding, and trust mechanisms will likely evolve over time. Another point worth considering is that AI-assisted development should not necessarily be evaluated solely in terms of code quality. There are also accessibility benefits, educational benefits, and opportunities for new contributors who otherwise might never have engaged with open source. While some of these contributions may indeed be lower quality, the broader increase in participation could, over a sufficiently long time horizon, create a larger pool of experienced contributors than currently exists. This is, admittedly, speculative, but it is also consistent with historical patterns observed during previous shifts in software tooling. Additionally, I think it’s useful to separate concerns regarding code generation from concerns regarding software architecture. Current language models certainly have limitations with maintaining long-lived systems, preserving architectural consistency, and minimizing technical debt. However, these limitations should not necessarily be interpreted as permanent characteristics rather than temporary engineering constraints. Future iterations may demonstrate substantially improved long-context reasoning, architectural awareness, and repository-scale understanding. Finally, I would caution against assuming that current social dynamics necessarily represent the eventual steady state. Communities have historically developed moderation strategies, reputation systems, automated quality gates, and contribution standards in response to changing incentives. While the present situation may be frustrating, it seems plausible that open-source governance will adapt in ways that reduce reviewer burden while maintaining quality. Ultimately, I think the long-term outcome remains uncertain. There are certainly valid concerns about maintainer burnout, review overload, and declining signal-to-noise ratios. At the same time, there are also reasons to believe that new institutional norms, improved tooling, and changing contributor behavior could partially or substantially mitigate those issues over time. As such, I don’t think it is possible to confidently conclude either that open source is doomed or that everything will automatically work itself out. The reality is likely to be considerably more nuanced than either extreme.

Based on what? Hopium? Open source maintainers are burning out and even extremely popular projects struggle to recruit new devs to help. What’s supporting your argument here besides “lol idk they’ll figure it out I guess”

There’s a ton of load bearing stuff that’s going to break once maybe 100 people have enough and stop thanklessly maintaining things. In fact it’s even worse than being thanklessly expected to fix shit since people are outright hostile towards you for maintaining your own passion project that nobody else wants to help with

Paris

Does any of what you just said sound reasonable or likely?

I’m arguing that it’s not a small difference since the cognitive burden is put on maintainers who are already overworked. Now they need to edit through AI PR manure to find decent requests. A lot of OSS projects have stopped taking public PRs for this reason. It shifts the cognitive burden from the programmer to make good code, onto the reviewer to read and understand AI slop contribution.

Wait, why am I being smited!?

There’s a huge variety. Grinding trains usually travel at around 30km/h and it looks like what Hollywood thinks an emergency brake application looks like

Track POV of a grinding train

They usually have a water tender at the end with a water cannon to spray down fires started by the grinding train itself.

Railway ballast cleaning to remove fines and tamp

Sleeper replacement

Yeah by a long shot. Railroads spend 1-3% of the capital cost on maintenance. So if it cost $3 million to build a km of track they spend roughly $30-90k/year. It might not look like it since tracks are just kind of always “there” but there is a decent amount of work you need to do pretty much constantly.

The stones underneath the tracks and sleepers is called ballast and it needs to be rough, angular rock of a certain size. It’s an engineered fill material that’s designed to lock together and hold the rails in place. Every train that passes by does a couple of things at once. It cyclically loads the ballast and breaks down those sharp edges that lock it together. At the same time the force of the wheels on the rails contributes to fatigue causing microcracks to form on the rail surface.

If you leave rails unmaintained long enough (by gross tonnage BTW. Rail can last a long time if it’s hardly used at all) the track will start pumping, where it compresses and expands as each axle passes over it. You’ll start lifting the subsoil up into the ballast ruining its ability to hold the track in alignment even more. Also, microcracks can combine enough that the railhead begins spalling and eventually the cracks can combine enough that the rail itself can fracture well before it’s service life tonnage.

To fix this you have to tamp ballast every X number of tons, grind the railhead every Y tons, and replace the ballast every Z tons .etc

Basically every part of the permanent way is some kind of wear item that needs maintenance. Mind you, only mainline track needs to be perfect. As long as speed limits are low enough along with an inspection, you can run on degraded secondary rails or spurs safely. It just wears it out even faster.

It’s like carcinization, the tendency for everything to evolve into a crab body plan.

Railinization? The tendency for every hare brained tech idea that touches transportation to evolve into railroads?

The issue is you need to grind the tracks multiple times a year on busy routes, along with ballast tamping or replacement. That’s set by total gross tonnage not by a set time span, and these maintenance items are not optional. Tamping prevents pumping and rail fatigue which can be catastrophic if you allow it to degrade and suddenly fail. Rail grinding removes microcracks created by fatigue due to every wheel passing over it. Grinding deletes the cracks, but if you leave it for too long the cracks grow and can total the rail. 10 minutes per panel is a long time when you need to maintain tens of thousands of kms of track.

Even a short distance between two towns is a maintenance headache. It could take weeks to remove the panels entirely and that’s before you get started doing maintenance at all… All you’ve accomplished is removing a maintenance obstacle you put there in the first place. Then you have to put it all back when you’re done…

A railroad typically spends 1-3% of the entire cost to build a km of track just to maintain it every year. That’s a big operating cost that eats into rail budgets already (part of why I believe they should all be nationalized to better align public incentives with a natural monopoly but that’s beyond this conversation.)

For reference, most rail costs around $1-3 million per km to build, $5-10 million or more within urban areas due to land acquisition. Typical railroad maintenance is somewhere around $10 000-$30 000 per km per year with unrestricted access. 20 mins round trip per panel (probably half an hour with deadtime between panels moving tools and gear) that’s a massive amount of increasedl about costs and service interruption.

I’m interested to see how it impacts maintenance for the permanent way. I don’t think it helps at all… Reballasting, tamping, changing sleepers, railhead grinding .etc all need to go places where these panels are, so now you have to remove them, do your maintenance, and put them back?

Instead they just put it in a place where they have to be disconnected and removed to reballast or tamp the permanent way, or for railhead grinding, or for sleeper replacement, or for any maintenance task that needs access between the tracks…

Would that word have basically meant all of the British isles since Irish independence happened far after WWII?

Ancient Chinese recording that half the world died in a minor scuffle so pwease Mr. Emperor I pwomise my taxes will be low for the next 10 years because they all died 🥺

The guy who suspected washing would reduce mortality (and did a study proving it) was made a laughingstock and was driven mad by his colleagues refusal to wash their god damn hands.

He was proven right eventually but I think I would go insane too if I knew it was directly causing my patients to die, and suggesting it as a way to reduce patient mortality made me into a laughingstock.

If you pick right there’s a small but nonzero chance that you and your brothers can work to overthrow the emperor in a temple rebellion

Same with konjac noodles. Basically no nutrition in them so they keep you from feeling hungry.

I heard the quote in passing but can’t find it. It was translated from a cuneiform tablet something to the effect of “a skilled cuneiform scribe can write as fast as the mouth moved”

Dr. Irving Finkel has said the same. The Babylonians and others managed massive bureaucracies at the time handling thousands of letters, court transcripts, and official dictations. There was more than enough practice for a scribe to get fast at it.