alt Hacker NewsAverage is all you need
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I think the author should be introduced to (or reminded of) the tale of the average from the US Air Force [1]. Social reality is high-dimensional and the "normal" thing is actually to be average in some dimensions, but strongly non-average in many others. So a "perfectly average" family would paradoxically be an outlier themselves.
I think this is important, because if his hypothesis is right, then LLMs behave differently here: They really are average in all dimensions. They are the pilots the Air Force thought they had before Daniels made the study.
So if he is right, we'd be changing from a mostly-non-average to a mostly-average society, which would really be a massive change - and probably not a good one IMO.
[1] https://noblestatman.com/uploads/6/6/7/3/66731677/cockpit.fl...
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.
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_.
The real risk is not that "LLM SQL" is wrong, it is in fact that a man who asked for it can't recognize when it is wrong. Writing a query and knowing when JSON silently doubled your rows are different skills and LLMs are able to give you only one of them (I'm not sure if they really can)
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?
Average is all you need, if your needs are average.
i fail to understand how text2sql on quite simple data sources is anything to write home about 3 years after it came onto the market? can someone elaborate?
where it gets interesting is when you have a custom system that your LLM surely never saw (custom ERP) that has 50 sometime cryptic tables, unclear look up tables and unexplained flags.
something no text2sql solution solved for us.
we built a second mcp that lets the agent look up business logic (generated from source code) and then does better queries. that i think is something i never read in a blog post about a text2sql solution.
This seems like a nice context to mention Sturgeon's law:
> ninety percent of everything is crud
> 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?
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.
Being average is a just stage LLMs pass through as AI makes its way towards 'expert' and 'super human' levels.
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...
This says "Editorial" at the top but has no authorship information. Who wrote this?
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?
> Claude doesn't ask you to define an attribution model. It doesn't open a whiteboard. It runs [query]"
Why is that a good thing? Claude didn't ask any obvious follow up questions, like what determined whether a user got an email or not? It is using the ab test terminology in Step 3 without any kind of confirmation that this is, you know, a valid test.
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.
I flat out disagree. If we want things that are better than average, we will need to be making things that are not average. The machine doesn't learn yet.
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
This is all fun and games when you work with toy data samples. But most organizations are more complex, they have to match invoices from SAP with opportunities in Hubspot; or they have to consider that little sales territory exception for the sales guy in Munich to calculate the proper commission projection; or they have custom tables in Salesforce with 0 documentation; or... you get my point.
Not all context is documented, and some context has to even be changed because it doesn't make sense.
I find AI very useful, but I think a lot of this AI SQL products are misleading.
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 ..
Another writer trying to redefine a common english word to mean whatever they want it to mean at the time.
Pass.
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.
It is like nails on a chalkboard.
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> 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...