> 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 at the base table and the joins. You're joining customers to two (implied) one-to-many tables, charges and email_events. If there are multiple charges rows per customer, or an email can match multiple email_events rows, it can lead to a Cartesian multiplication of the rows since any combination of matches from the base table to the joined tables will be included.
If that's the case, the transactions and revenue values are likely to be inflated, and therefore the pretty pictures you passed along to your boss are wrong.
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 does the rest. The results are way above average.
I always find it a bit weird to see posts on the front page where all the comments disagree with the central premise of the article. In this case the post is an ad advocating for executing code you didn't write and handing the results to your manager.
It makes me wonder if Hacker News has a silent majority of people who would actually use AI in this way without wanting to admit it, and a vocal minority of people who wouldn't.
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.
I don’t believe this is a meaningful distinction when we’re not going to agree on how to judge performance of software engineers. If this were solely about income, it might be an important distinction.
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
For that matter, how does a business differentiate themselves, if people can write their own software? While we're busy trying to replace our employees with AI, our customers are trying to replace our products with AI.
At any tech company with leveling guidelines that I have seen, promotions above mid level have never been based on “I codez real gud”. It’s always been based on scope, impact and dealing with ambiguity. It’s stated differently in different companies.
No one has ever differentiated themselves based on how good of a ticket taker they are. Coding especially on the enterprise dev side where most developers work has been being commoditized since 2016 at least and compensation has stagnated since then and hasn’t come near keeping up with inflation.
In 2016, a good solid full stack, mobile or web developer working in the enterprise could make $135K working in a second tier city. That’s $185K inflation adjusted today. Those same companies aren’t paying $185K for the same position.
My one anecdote is that the same company I worked for back then making $125K and some of my coworkers were making $135K just posted a position on LinkedIn with the same requirements (SQL Server + C#) offering $145K fully remote.
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; almost everyone just becomes a human resource to be swapped around based on social factors.
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.
> If average is all we need, then anyone can do it.
Pretty much, yes. That is why the range of salaries on offer is pretty compressed compared to the range of returns capitalists get.
yes. Most people are upset and fear losing their job because they feel their job is sub-par. In reality, that's for most of them impostor syndrome, for some could be a wake up call.
Average is only a tombstone of someone having failed to do better. And settling for average means pulling down.
When it comes to bs dashboard where "average is all you need", maybe the "better than average" result would be asking yourself if it's even worth doing in the first place?
how do you know those queries are actually correct without domain knowledge?
Do you know enough about JOINs and how they work to be able to break those big queries down and figure out whether they are doing exactly what you're asking for in English?
It's a post claiming average AI is useful... by a for-profit "data platform with a CLI that LLM agents can use directly". What are they going to do? Criticize the whole industry they are selling to?
I did not post it. I did not intend for it to be posted here - really. It just happened someone did see it and posted it. So I did not advertise anything :-)
This tracks. Tasks that used to be a day or two of grunt work are now an hour with Claude.
And there is a lot of that type of work to do if you're trying to grow a business. But, something in there should be trying to be exceptional or else you have no moat. Claude will probably not be able to breeze through that part with the same amount of ease...
But, you see, our needs are above average because we target above average exists so we only hire from the top 1% of software engineers, blah, blah, yadda, yadda, etc.
The Business simply cannot admit that it’s really doing nothing above average. If they did, investment dries up.
> But this is a pain, first because, if you do anything that is not selling a product online that people can buy right when they click a button, it is a drag to create those attribution models effectively: is it last click, first click, weighted attribution... who knows. Nobody knows. Everybody gives up and just adds it to a dashboard and pretends it makes sense.
Yes, thinking about your data and how to check it is so annoying. Much better to do something average, see if the result puts you in a good light, and share that insight into your company's working with ~~everyone on the internet~~ your boss.
Rarely have I seen "we help you create meaningless slop more easily" advertised so explicitly. Or is this also average?
Probably the same way other models learned to surpass human ability while being bootstrapped from human-level data - using reinforcement learning.
The question is, do we have good enough feedback loops for that, and if not, are we going to find them? I would bet they will be found for a lot of use cases.
Humans can decide to write above-average code by putting in more effort, writing comprehensive tests, iteratively refactoring, profile-informed optimization, etc.
I think you can have LLMs do that too, and then generate synthetic training data for "high-effort code".
Well state of the art LLMs sure can't consistently produce high quality code outside of small greenfield projects or tiny demos, which is a domain that was always easy even for humans as there are very few constraints to consider, and the context is very small.
Part of the problem is that better code is almost always less code. Where a skilled programmer will introduce a surgical 1-3 LOC diff, an incompetent programmer will introduce 100 LOC. So you'll almost always have a case where the bad code outnumbers the good.
Current LLMs do tend to explode complexity if left to their own devices but I don't think that's an inherent limitation. Mediocre programmers can write good code if they try hard enough and spend enough time on it.
Because you ask it to improve things and so it produces slightly better than average results - the average person can find things wrong with something, and fix it as well. Then you feed that improved result back in and generate a model where the average is better.
This is not only average. This is actual magic.
So let's be real: the SQL is average. The joins are average. The chart is average. And that took us less than 5 minutes and that was amazing, that is the entire point.
You did not need a data engineer to model your HubSpot data, or a meeting to agree on whether it should be last-click or first-click or linear or time-decay or whatever.
You needed a query, written fast, on data you already own. Your LLM wrote it. You confirmed it made sense. Your manager got a link.
Honestly, average is clearly magic; prove me wrong.
I'll give it a go. This is generated slop, and the poor, factory-made quality of the writing undercuts every aspect of the argument.
Author here; I suppose the... side eye awkward monkey meme was a bit lost on you; it was written on purpose. Funnily enough. Everything is slop if you want it to be slop. This however, was written by hand my little hands. Now I might be a bad writter - that is indeed another subject.
A car that starts 50% of the time isn't "average". The average new car starts more or less every time. (And if you said 'modal average', I'd say the modal average new car starts every time).
> 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 at the base table and the joins. You're joining customers to two (implied) one-to-many tables, charges and email_events. If there are multiple charges rows per customer, or an email can match multiple email_events rows, it can lead to a Cartesian multiplication of the rows since any combination of matches from the base table to the joined tables will be included.
If that's the case, the transactions and revenue values are likely to be inflated, and therefore the pretty pictures you passed along to your boss are wrong.
Further reading, and a terrific resource:
https://kb.databasedesignbook.com/posts/sql-joins/#understan...
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 does the rest. The results are way above average.
I always find it a bit weird to see posts on the front page where all the comments disagree with the central premise of the article. In this case the post is an ad advocating for executing code you didn't write and handing the results to your manager.
It makes me wonder if Hacker News has a silent majority of people who would actually use AI in this way without wanting to admit it, and a vocal minority of people who wouldn't.
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.
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
I don’t believe this is a meaningful distinction when we’re not going to agree on how to judge performance of software engineers. If this were solely about income, it might be an important distinction.
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
It is almost never true. If you filter people you're going to get a Pareto distribution.
For that matter, how does a business differentiate themselves, if people can write their own software? While we're busy trying to replace our employees with AI, our customers are trying to replace our products with AI.
At any tech company with leveling guidelines that I have seen, promotions above mid level have never been based on “I codez real gud”. It’s always been based on scope, impact and dealing with ambiguity. It’s stated differently in different companies.
No one has ever differentiated themselves based on how good of a ticket taker they are. Coding especially on the enterprise dev side where most developers work has been being commoditized since 2016 at least and compensation has stagnated since then and hasn’t come near keeping up with inflation.
In 2016, a good solid full stack, mobile or web developer working in the enterprise could make $135K working in a second tier city. That’s $185K inflation adjusted today. Those same companies aren’t paying $185K for the same position.
My one anecdote is that the same company I worked for back then making $125K and some of my coworkers were making $135K just posted a position on LinkedIn with the same requirements (SQL Server + C#) offering $145K fully remote.
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; almost everyone just becomes a human resource to be swapped around based on social factors.
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.
> If average is all we need, then anyone can do it.
Pretty much, yes. That is why the range of salaries on offer is pretty compressed compared to the range of returns capitalists get.
The power saw makes average cuts, it didn't disemploy carpenters, we just made better homes.
We make more homes, but I would say the construction of the average home is worse after the invention of the power saw than before it.
The average of quality isn't always available in all people.
Reducing the amount of time I spend on the average code has meant I'm spending more time adding my above-average contributions to the code base.
How stable that is on the long term, I don't know any more than the next guy, but it is where I'm contributing now.
yes. Most people are upset and fear losing their job because they feel their job is sub-par. In reality, that's for most of them impostor syndrome, for some could be a wake up call.
Average is only a tombstone of someone having failed to do better. And settling for average means pulling down.
When it comes to bs dashboard where "average is all you need", maybe the "better than average" result would be asking yourself if it's even worth doing in the first place?
This seems like a nice context to mention Sturgeon's law:
> ninety percent of everything is crud
https://en.wikipedia.org/wiki/Sturgeon%27s_law
how do you know those queries are actually correct without domain knowledge?
Do you know enough about JOINs and how they work to be able to break those big queries down and figure out whether they are doing exactly what you're asking for in English?
You don't, and if businesses start using vibed reports for regulated reporting then I guess we'll see soon what the courts say about that
This is yet another ad, it's tiring.
It's a post claiming average AI is useful... by a for-profit "data platform with a CLI that LLM agents can use directly". What are they going to do? Criticize the whole industry they are selling to?
I did not post it. I did not intend for it to be posted here - really. It just happened someone did see it and posted it. So I did not advertise anything :-)
This tracks. Tasks that used to be a day or two of grunt work are now an hour with Claude.
And there is a lot of that type of work to do if you're trying to grow a business. But, something in there should be trying to be exceptional or else you have no moat. Claude will probably not be able to breeze through that part with the same amount of ease...
Average is all you need, if your needs are average.
But, you see, our needs are above average because we target above average exists so we only hire from the top 1% of software engineers, blah, blah, yadda, yadda, etc.
The Business simply cannot admit that it’s really doing nothing above average. If they did, investment dries up.
> But this is a pain, first because, if you do anything that is not selling a product online that people can buy right when they click a button, it is a drag to create those attribution models effectively: is it last click, first click, weighted attribution... who knows. Nobody knows. Everybody gives up and just adds it to a dashboard and pretends it makes sense.
Yes, thinking about your data and how to check it is so annoying. Much better to do something average, see if the result puts you in a good light, and share that insight into your company's working with ~~everyone on the internet~~ your boss.
Rarely have I seen "we help you create meaningless slop more easily" advertised so explicitly. Or is this also average?
Being average is a just stage LLMs pass through as AI makes its way towards 'expert' and 'super human' levels.
LLMs are trained to predict tokens on highly mediocre code though. How will it exceed its training data?
Probably the same way other models learned to surpass human ability while being bootstrapped from human-level data - using reinforcement learning.
The question is, do we have good enough feedback loops for that, and if not, are we going to find them? I would bet they will be found for a lot of use cases.
Humans can decide to write above-average code by putting in more effort, writing comprehensive tests, iteratively refactoring, profile-informed optimization, etc.
I think you can have LLMs do that too, and then generate synthetic training data for "high-effort code".
Well state of the art LLMs sure can't consistently produce high quality code outside of small greenfield projects or tiny demos, which is a domain that was always easy even for humans as there are very few constraints to consider, and the context is very small.
Part of the problem is that better code is almost always less code. Where a skilled programmer will introduce a surgical 1-3 LOC diff, an incompetent programmer will introduce 100 LOC. So you'll almost always have a case where the bad code outnumbers the good.
Current LLMs do tend to explode complexity if left to their own devices but I don't think that's an inherent limitation. Mediocre programmers can write good code if they try hard enough and spend enough time on it.
Because you ask it to improve things and so it produces slightly better than average results - the average person can find things wrong with something, and fix it as well. Then you feed that improved result back in and generate a model where the average is better.
/end extreme over optimism.
Who are you to question our faith? /s
The majority of devs are average. What a shocker.
The majority of any filtered group are below average. Imposter syndrome isn't a thing, 80% of people really did just barely make the cutoff.
adding LLMs to the incompetent doesn’t transform them
if anything it makes the world more dangerous
a reckoning is coming
the top decile will be janitors for the rest
It is like nails on a chalkboard.
Author here; I suppose the... side eye awkward monkey meme was a bit lost on you; it was written on purpose. Funnily enough. Everything is slop if you want it to be slop. This however, was written by hand my little hands. Now I might be a bad writter - that is indeed another subject.
Another writer trying to redefine a common english word to mean whatever they want it to mean at the time.
Pass.
Average is all we need ! I mean, working 50% is enough, right ?
A car that starts 50% of the time ?
A plane that stops on 50% of the flights ?
A pacemaker that beats only 50% of the time ?
David Goodenought said that average is enough ..
A car that starts 50% of the time isn't "average". The average new car starts more or less every time. (And if you said 'modal average', I'd say the modal average new car starts every time).
It is not average today because people in the past tried to do better, not average things