When my eldest daughter was in high school (~2010, Argentina) there was a provincial policy where if every single student had a result below a certain score in a test, the scores had to be re assessed against the maximum result.
The resulting situation here was that she was constantly bullied into underperforming. Both cases are actually similar in that each individual has a personal incentive to underperform - the difference is that in your friend's case the policy is granted at the company level so no single employee can defect and break it for the rest, while in my daughter's case one high scorer could invalidate the reassessment for everyone, which is exactly what made defection punishable and the bullying emerge naturally.
This is the natural result of "equity" which is the academic jargon term for "forced equality of outcome". High achievers are attacked. People who push us forward are demonized. The low achievers are never pushed to be better. And the average drops.
It’s not that absurd and happens all over the world in university systems. I had a Comp. Sci. Professor that taught assembly and graded on a curve. As you might imagine the one guy that was a wizard at assembly caught flak from the unwashed masses.
I had another professor that not only did a curve but dropped statistical outliers to prevent this problem, he literally explained his system on Day 1 of the course. This was 15+ years ago and by no means a new idea.
I tried to search for it, but even the 2 documents that superseded the one from around the time my daughter was at school at not available.
I mean, the site doesn't even have a valid secure certificate so...
In the site below (In Spanish) you can search for 10/2019 and a cursory translation of the document title will show that this is the proper document (For 2019 onwards, the replaced doc 04/2014 isn't available either)
For me, what AI brings is augmented humans. Just as we don't calculate on paper anymore, what is the reason of doing things by hand when a machine in X times better.
Want to code by hand, as artisans of old? Suit yourself.
I regularly (Say, once a month) do a comparison of results across all Claude, Gemini and ChatGPT. Just for reasons, not that I want to see if there's any benefit in changing.
It's not "fair" in that I pay for Claude [1] and not for the others, so models availability is not complete except for Claude.
So I did like things at time in the form of how they were presented, I came to really like Sonnet's "voice" a lot over the others.
Take into account Opus doesn't have the same voice, and I don't like it as much.
[1] I pay for the lower tier of their Max offering.
> The long-term effect is less clear. If we generate more code, faster, does that reduce cost or just increase the surface area we need to maintain, test, secure, and reason about later?
My take is that the focus is mostly oriented towards code, but in my experience everything around code got cheaper too. In my particular case, I do coding, I do DevOps, I do second level support, I do data analysis. Every single task I have to do is now seriously augmented by AI.
In my last performance review, my manager was actually surprised when I told him that I am now more a manager of my own work than actually doing the work.
This also means my productivity is now probably around 2.5x what it was a couple of years ago.
> In my last performance review, my manager was actually surprised when I told him that I am now more a manager of my own work than actually doing the work.
I think this is very telling. Unless you have a good manager who is paying attention, a lot of them are clueless and just see the hype of 10x ing your developers and don't care about the nuance of (as they say) all the surrounding bits to writing code. And unfortunately, they just repeat this to the people above them, who also read the hype and just see $$ of reducing headcount. (sorry, venting a little)
This has been my experience, too. In dealing with hardware, I'm particularly pleased with how vision models are shaping up; it's able to identify what I've photographed, put it in a simple text list, and link me to appropriate datasheets. yday, it even figured out how I wanted to reverse engineer a remote display board for a just-released inverter and correctly identified which pin of which unfamiliar Chinese chip was spitting out the serial data I was interested in; all I actually asked for was chip IDs with a quick vague note on what I was doing. It doesn't help me solder faster, but it gets me to soldering faster.
A bit OT, but I would love to see some different methods of calculating economic productivity. After looking into how BLS calculates software productivity, I quit giving weight to the number altogether and it left me feeling a bit blue; they apply a deflator in part by considering the value of features (which they claim to be able to estimate by comparing feature sets and prices in a select basket of items of a category, applying coefficients based on differences); it'll likely never actually capture what's going on in AI unless Adobe decides to add a hundred new buttons "because it's so quick and easy to do." Their methodology requires ignoring FOSS (except for certain corporate own-account cases), too; if everyone switched from Microsoft365 to LibreOffice, US productivity as measured by BLS would crash.
BLS lays methodology out in a FAQ page on "Hedonic Quality Adjustment"[1], which covers hardware instead of software, but software becomes more reliant on these "what does the consumer pay" guesses at value (what is the value of S-Video input on your TV? significantly more than supporting picture-in-picture, at least in 2020).
> Having the LLM write down a skill representing the lessons from the struggle you just had to get something done is more typical (I hope) and quite different from what they're referring to
Just as of last week I had Claude build me a skill when I ask it to help me troubleshoot issues, and it came out quite good.
It did had some issues (Claude tends to o er specify over anecdotal data) but it's a strong step in the right direction.
Also, "skills" are too broad in my opinion. I have one (that Claude wrote) with my personal data that I have available when I analyze my workouts.
I think there's ample room for self-generated skills when you use a rather long exchange on a domain you plan to revisit, _specially_ when it comes to telling Claude what not to do.
I recently had to create a MySQL shim for upgrading a large PHP codebase that currently is running in version 5.6 (Don't ask)
The way I aimed at it (Yes, I know there are already existing shims, but I felt more comfortable vibe coding it than using something that might not cover all my use cases) was to:
1. Extract already existing test suit [1] from the original PHP extensions repo (All .phpt files)
2. Get Claude to iterate over the results of the tests while building the code
3. Extract my complete list of functions called and fill the gaps
3. Profit?
When I finally got to test the shim, the fact that it ran in the first run was rather emotional.
[1] My shim fails quite a lot of tests, but all of them are cosmetics (E.g., no warning for deprecation) rather than functional.
This applies to many different things, depending on the pair of languages you are using.
In Spanish the closes approximation would be "ni mal ni bien" (Not bad not wrong) but I understand the Chinese expression has a strong lean on "not being wrong".
Not so long ago (I'm 50+, Spanish native speaker, and I've spoken English for the past 30 years almost daily) I learnt about "accountability".
Now before I get a barrage of WTFs, the situation is that in Spanish we only have "Responsabilidad" and that accounts for both responsibility and accountability, with a strong lean on responsibility.
So basically we recognise what is it to be responsible of something, but being accountable is seriously diluted.
The implications of this are enormous, and this particular though exercise I'd leave for people that spend more time thinking about these things than I do.
> Ok, so does anyone remember 'Watson'? It was the chatgpt before chatgpt. they built it in house
I do. I remember going to a chat once where they wanted to get people on-board in using it.
It was 90 minutes of hot air.
They "showed" how Watson worked and how to implement things, and I think every single person in the room knew they were full of it. Imagine we were all engineers and there were no questions at the end.
Comparing Watson to LLMs is like comparing a rock to an AIM-9 Sidewinder.
Watson was nothing like ChatGPT. The first iteration was a system specifically built to play Jeopardy. It did some neat stuff with NLP and information retrieval, but it was all still last generation AI/ML technology. It then evolved into a brand that IBM used to sell its consulting services. The product itself was a massive failure because it had no real applications and was too weak as a general purpose chat bot.
The reason LLMs are viable for use cases that Watson wasn't is their natural language and universal parsing strengths.
In the Watson era, all the front- and back-ends had to be custom engineered per use case. Read, huge IBM services implementation projects that the company bungled more often than not.
Which is where the Palantir comparison is apt (and differs). Palantir understood their product was the product, and implementation was a necessary evil, to be engineered away ASAP.
To IBM, implementation revenue was the only reason to have a product.
> Read, huge IBM services implementation projects that the company bungled more often than not
Well this is _not_ what they wanted to sell in that talk.
But the implementation shown was über vanilla, and once I got home the documentation was close to un existent (Or, at least, not even trying to be what the docs for such a technology should be).
The resulting situation here was that she was constantly bullied into underperforming. Both cases are actually similar in that each individual has a personal incentive to underperform - the difference is that in your friend's case the policy is granted at the company level so no single employee can defect and break it for the rest, while in my daughter's case one high scorer could invalidate the reassessment for everyone, which is exactly what made defection punishable and the bullying emerge naturally.