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Average is all you need

rawquery.dev

61–70 of 111 posts

Re: Average is all you need

#61

> You did not write a single line of SQL. You did not set up an attribution model. You asked a question, in English, and got a table. But nobody bothered to check if it was correct. It might seem correct, but I've been burned by queries exactly like these many, many times. What can often happen is that you end up with multiplied rows, and the answer isn't "let's just add a DISTINCT somewhere". The answer is to look a…

Ok but… nobody said you didn’t had to check either(?).

How do you check if you don't have any other view into the data but SQL and you don't know SQL?

Re: Average is all you need

#62
post #30

If average is all we need, then anyone can do it. What value do I add? How does an employee differentiate themselves? Why didn’t the boss ask the AI for the charts to begin with? Everyone’s income is going to be below average, because they got fired.

That isn't a sane starting point; if a corporation's strategy is to only hire above average employees they're going to fail. Enron springs to mind. Corporations generally take average people and give them a reasonably well defined scopes of simple work to complete that adds value. The bigger the corporation the more difficulty they have handling even the standard deviation above average differently to the one below;…

> The people who need to be above average and exceptionally are senior management and maybe a few bright sparks in middle management. Most of the value-add happens there that builds social machines that then do the work.

That is the dream. Upper management can get software made without talent.

But is seems to be the greatest ideas in the last 30 years didn’t start in board rooms. They started with a couple coders creating a new idea.

No boardroom could have invented Google. It was so fundamentally different than what other search engines were doing.

We have this myth that upper management is so important. It is as the business grows in size, they are excellent for coordination. But ideas come from people closer to the problems.

Re: Average is all you need

#63

Average is all you need, if your needs are average.

I liken it to the Ikeaficiation of furniture. To a great majority, such as my college self, it was preferable and desirable. As I've made more money, I've wanted something better.

There's a market for both, but the furniture slop of Ikea is dominant.

Re: Average is all you need

#64
post #7

Earlier quoted context omitted.

> Half of people will be below average. s/average/median

The article assumes a normal distribution, making the distinction moot But it is useful to question whether that is true in all cases. The cases that aren't normal-distributed might be exactly the cases where it pays off to be neither average or median

there is a major shortcoming in this assumption; everything we've seen related to the internet and technology in general suggests there is rarely a normal distribution. I think it's way more valuable ato frame the questions as a long tail (pareto) distribution and a "good enough" cut-off point.

Re: Average is all you need

#65

Earlier quoted context omitted.

Ok but… nobody said you didn’t had to check either(?).

How do you check if you don't have any other view into the data but SQL and you don't know SQL?

Same way you do today; you trust whoever wrote the query.

I do not sell a wrapper on top of some LLM; you can absolutely write your SQL directly. There is an engine, there are iceberg tables. You can just live your best life doing your own SQL by hand.

Now if you couldnt do it before and you have a sensible understanding, you can likely do a bit more with the CLI tooling. And if you know a lot more, you can still do that. The queries are not hidden, or abstracted, If you need them they will be saved - transparently in SQL.

So I dont know what is the answer to the question "how do people do things they don't know how to do" ?

Re: Average is all you need

#66
post #51

Earlier quoted context omitted.

Good gosh no. That's like saying 'cars were better made in the 1950's because they used tons of steel'. Like they were 'heavier and more robust' - but that doesn't mean better. Foundations are way better, more robust, especially weatherized. Windows today are like magic compared to windows 100 years ago. What we do more poorly now is we don't use wood everywhere, aka doors, and certain kinds of workmanship are not th…

Yes, houses got better because materials got better. Windows are better. But the construction of the houses is worse. The precision of how the wood or material meets is worse (when cut at the site). There is a huge amount of sloppy work in modern construction.

I'm interested in how one would prove that one way or another.

It seems to me that in the past there probably was lots of shoddy workmanship and just no-one paid attention to it.

But I have no proof of that.

Re: Average is all you need

#67
post #7

Earlier quoted context omitted.

Not everyone can be average. Half of people will be below average. I might not agree with the point, but I can see that idea that many things just need to be "good enough" (which we might define as "average") and we save our real expertise for the things that really matter.

> Half of people will be below average. s/average/median

Median is a type of average.

Though usually "average" implies arithmetic mean.

Re: Average is all you need

#68

Why average? I've always taken pride in my work and developed things that went beyond the expectations of the management and of the final users. Now I'm using LLMs a lot and I've been able to do much more than I used to- I find them great coworkers, technically very knowledgeable, patient and fast. I provide the big picture, keep an eye on the architectural soundness and code quality, and design the features. The LLM…

Nobody cares

[dead]

Re: Average is all you need

#69
Tbh I dont really agree with your statements. Especially with working with data, intention is key. By using an llm, by definition, you are loosing intention. And Thai puts you in a position where you have to 1) think of exactly what you look for. 2) able to understand what the llm generated.

You might say it "still less work" and that's true, perhaps, only for the first few times. After a while you _learn_ how to do it, and understand how to _think_ with the language of your data. With LLMs, you never get this benefit, and also loose your ability to judge the LLM's output properly.

But again, that might be enough on your case, or, you simply don't _know_.

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