jldugger

And 2017 was _late_ in their pivot. They'd been active for much, much longer. Last winter break I sat down to watch every GTC keynote, going back to 2009[1]. Even then, he's talking about expanding to non-graphics workloads. Google's GPU paper[2] just slotted naturally into their existing narrative and were happy to support it. "fortune favors the prepared" as they say.

[1]: https://www.youtube.com/watch?v=fYuH2Kl_b98 [2]: https://scholar.google.com/citations?view_op=view_citation&h...

It's certainly someone's dream: https://devhumor.com/media/dilbert-s-team-writes-a-minivan

I mean, there are many unserious engineers in corporate America.

You know, when I see the phrase "CIA funding" I expect something like "the CIA put a backdoor in NeXT computers destined for Hungary" or something, not "NeXT made computers and the CIA bought and used them."

I think I'm missing a _lot_ of context here. Obviously management consultants are sycophantic but why post this now?

> I don't understand the distinction you are making.

20 years ago: https://en.wikipedia.org/wiki/DRAM_industry_price_fixing

> According to the one-count charge filed in San Francisco's federal court on Thursday, Park conspired with unnamed employees from other memory makers to fix the price of DRAM sold to computer makers from April 1, 2001, to June 15, 2002. The government says the move directly affected sales to U.S. computer makers Dell, Hewlett-Packard, Compaq, IBM, Apple Computer, and Gateway.

etc

OP's link: > Now the researchers have addressed this issue by having both the self-improving agent and the evaluator evolve together.

and your quote:

> This particular paper is about co-evolving predator and prey, where the behavior of each is the ‘evaluation’ of the other.

Both sound like the GAN approach that was popularized a decade ago and kinda the start of the "genAI" boom.

Ah, TIL. I knew prevailing wages were higher locally but was surprised to see it hit $20. Now I guess I know why.

> When I was young it was common for a McDonalds to have 10+ employees working the lunch rush, ... > Now I go and it's 3, sometimes 2 employees.

The one I went to last week had a banner advertising a $20/hr starting wage. Clearly they'd like to hire more.

> Hard working people tend to know other hard working people. ... Hard working people get more done.

Getting promoted at a FAANG is like a micro-effect. One percent of one percent of one percent. Generalizing our experience to 72 million Americans ain't it boss.

And our experience is subject to dozens of selection bias. For example, most of the best people I know at my current job are that way in part because I trained them at my last job at a university. And then they referred me into their employer networks. Beyond the hurdles of getting into a flagship state school, having those connections matter. Nobody I know from undergrad had any employment at FAANG when I was laid off a decade ago, I just got lucky enough to have a second cohort contacts in the form of student employees at a different flagship state university doing internships, landing jobs and looking for referral bonuses. Last I checked LinkedIn I'm the only engineer from my alma mater at my employer.

I think the difference is that Goodhart's law describes how the causal chain _changes_ as a result of management behavior, and in particular the incentives they design for the labor they manage. Incentives are a causal variable for outcomes, and what happens is that people find much easier ways to produce the outcomes you thought you wanted.

Like if you manage a call center and set up KPIs around average call time, reps will start hanging up on customers. Employees could always have done that, and the causal link was always there, there was just no reason to.

IMO the problem is executives want (and perhaps need) their directs to report and track one big number month over month. If you give them five metrics they'll never know if you're making progress or just oscillating between a few local minima. And if each of their ten directs has five metrics, you now have 50 numbers and no idea what time it is[1].

[1]: https://en.wikipedia.org/wiki/Segal%27s_law "A man with two watches never knows what time it is"

True, I guess I just presume someone blogging about it has more than average insights on the subject, and the bar further raised by posting to HN. They do get to somewhere interesting by the end, I just had to scroll past a lot of basics.

It's little surprising that the author was doing perf work and not already comparing distributions. As the rest of the article outlines, you learn a lot more with more data!

It's complicated, but really worth learning how prometheus and grafana heatmaps combine if you want dashboards for real time service data. Multimodal distributions are basically the expected outcome given all the caching done in distributed systems.

The gpus not the models silly

> And I'm also standing by my own idea that AI will run out of money and just be turned off due to the huge operational cost.

Thats a pretty strong outcome. It implies that not only are GPUs that power AI too expensive long term, but they cost too much to operate even if they were free.

Seems to me the more likely outcome is a wave of dotCom style bankruptcies wiping out equity holders for companies who contracted to buy chips and datacenters at MSRP, and a second wave for the groups that step in after to operate whats left without the absurd financing charges and lower capex.

> Plus, electricity costs money while the sun is free.

Paradoxically, wholesale electric costs are negative during the day here in California[1], especially in the Salinas region. In theory you could get paid to tumble dry clothes.

[1]: https://www.caiso.com/todays-outlook/prices

It's true that markets are more _adversarial_. But there's still a lot of trouble with distribution shifts even in server metrics. As an example, our SRE team got paged a few times in the past month for traffic drops due to the World Cup. This stresses the nowcasting alert in several dimensions:

- there's no seasonal pattern to the matches, they happen sorta randomly.

- they drive increased query traffic in the hour or so before the game

- then during the game usage drops, sometimes to below "normal" depending on time of day and who's playing

So... now the accuracy of your forecasting tool depends on correctly predicting when world cup matches happen, and also who wins them!

edit: and this is just one recent example. others involve severe weather, national gameshows, earthquakes, and when you celebrate christmas.

Apparently not given how many comments in opposition I got.

The difference is that vacuuming seven times as often can compensate for a less effective process. Folding 7 times doesn't eliminate wrinkles.

> You're telling me we've finally got robots that can do our laundry?

Did you watch it actually fold that shirt? Super slow and bad at it.

The linked paper suggests brain sizes were only measured once at visit 5.

After the third !, the probability of a fourth probably skyrockets =)

There's a difference between how you describe using "honestly" and how claude seems to prefer tokens like "honest" and "load-bearing." An example from some coworkers attempting to replace product managers with Claude.

> Deliberately avoid a heavyweight "alert governance" process; the lightest recurring check that keeps FP-rate honest is the right dose.

And one for load bearing:

> Five open questions still stand; the load-bearing two are the runbook-AC contradiction (ratify "high-priority set only") and pinning the "high-priority set" definition + SLO source-of-truth before Milestone 3 (small-sample noise on a low-traffic fleet).

One hopes that "you cant ship any more features until your test suite stops failing due to timeouts" would motivate them.

I think the request here is not about sounding like Majel Barrett but in keeping the output extremely terse and unobtrusive.

There's been a few studys showing that novices love LLM output that's long, but experts hate it. As an example, I've been tasked with using some agentic PM tool to write specs, and it keeps generating these huge page long outputs with "HBR voice" bolded summaries of paragraph long bulletpoints. I.e.:

> Right-size hard, and watch the one open-ended edge. Endorse the DRI's simplifications wholesale: drop the runbook-per-alert mandate (keep 1–2 diagnostic-only runbooks for the high-priority set), and ride durability on the existing weekly incident + monthly operational reviews — no new governance. The single scope-creep risk is the coverage strand (gaps are defined by absence); bound it to gaps evidenced by real, already-missed customer-facing outages, not a proactive gap hunt. Curing ownership gaps (e.g. foo-bar, no clear owner) is finite in-scope work.

There's dozens of these every iteration. I can't imagine trying to deal with that via voice, I would just zone out after the second sentence.

I was just wondering if there was a postgresql specific version of https://use-the-index-luke.com i could send a coworker who seems oblivious to the perf penalties of missing indicies. Certainly a good start!

> Sure but why pussyfoot around the issue? We should be actively encouraging each other to punish misbehaving companies. It's the right thing to do.

Probably because doing what you suggest would not look great in court:

"Dear Jeff Bezos,

Here is my signed confession letter of intellectual property theft.

Yours Truly, Mike Taylor"

but what if i learned to type Colemak?

Paging Vernor Vinge to the white courtesy phone.

Would 1.0 have fixed the wide variance in scoring?