If I had to guess, niche products for niche interests have small ad budgets, but the random detergent ad buyer is happy to bid on anyone's eyeballs. You can't target ad buys that don't exist!

On the other hand, before I bought YT premium I was regularly getting ads for Chevron gas in Spanish (which I don't speak), and would be unsurprised if YT ad enshittification drove premium sales.

There's like three reasons for this disconnect.

1. The agents aren't expert at your proprietary code. They can read logs and traces and make educated guesses, but there's no world model of your code in there.

2. The people building these apps are unqualified to review the output. I used to mock narcissists evaluating ChatGPT quality by asking it for their own biography, but they're at least using a domain they are an expert in. Your average MLE has no profound truths about kubernetes or the app. At best, they're using some toy "known broken" app to demonstrate under what are basically ideal conditions, but part of the holdout set should be new outages in your app.

3. SREs themselves are not so great at causal analysis. Many junior SRE take the "it worked last time" approach, but this embeds a presumption that whatever went wrong "last time" hasn't been fixed in code. Your typical senior SRE takes a "what changed?" approach, which is depressingly effective (as it indicates most outages are caused by coworkers). At the highest echelons, I've seen research papers examining meta-stablity and granger causality networks, but I'm pretty sure nobody in SRE or these RCA agents can explain what they mean.

> The key insight: individual session failures look random. But when you cluster the hypotheses, failure patterns emerge.

My own insight is mostly bayesian. Typical applications have redundancy of some kind, and you can extract useful signals by separating "good" from "bad". A simple bayesian score of (100+bad)/(100+good) does a relatively good job of removing the "oh that error log always happens" signals. There's also likely a path using clickhouse level data and bayesian causal networks, but the problem is traditional bayesian networks are hand crafted by humans.

So yea, you can ask an LLM for 100 guesses and do some kind of k-means clustering on them, but you can probably do a better job doing dimensional analysis first and passing that on to the agent.

This is almost certainly not banned for pornographic reasons.

This is about what we paid for Netflix in 2006. Especially after accounting for inflation.

Something like https://desknest.com seems much more likely to work.

I think most teams use verify as a "closed" state to hide all that messiness. But sure, zero bugs is a project management fiction and produces perverse outcomes.

    [1]: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=traa+anti+aliasing&btnG=

Reasonable. If I were more conspiratorial, I might suggest that it's precisely because people are watching college exam numbers that 9th grade -> high school is where the break is. Or could just be the result of compounding growth from two competing officials making different exaggerated claims decades ago.

But really, the high school enrollment gap is not super germane to my main point: we may have seen peak China population, stemming largely from a smaller incoming cohort. The sidebar about offsetting that decline with increased enrollment percentages is interesting, I'm just default skeptical.

> cited Chinese population statistics upthread, but when somebody else does it, they're suddenly unreliable???

My cite appears to use UN data, not the PRC's official stats (at least not directly). But I'm pretty sure the official stats are also showing the same trend, just at a slower rate of decline. I mean, it's the entire reason for loosening the one-child policy to two, then to three.

It's bad enough they passed some legislation a few years ago[1], but the damage has in many senses already been done. And it's unclear how effective the changes will be. So it's entirely possible those 3 million missing high schoolers never existed.

[1]: https://www.reuters.com/world/china/chinas-top-legislative-b...

I'm not so sure about that: https://www.populationpyramid.net/china/2026/ suggests peak high school in china was years ago.

Depending on where the baseline is, 1.8 degrees could be huge! But more importantly, heat dissapation is a non-linear function. The warmer you are relative to your environment, the more energy is lost. While Shackleton's kit forms a lower baseline, it probably makes sense to imagine how some imaginary perfect vacuum insulated sleeping bag would perform.

It was a literal bug in the computer. Metaphor as humor!