281–290 of 686 posts
Earlier quoted context omitted.
Lazy or efficient? A dev could spend an hour on something or 10 mins, if the outcome is the same what's does it matter?
Because the reviewer ends up doing the real work actually checking it works. The laziness is offloading work down the line.
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Does anyone know where that style came from? Did it become popular in listicles or on github or something? Or is there one person deep inside OpenAI or Anthropic who built the synthetic data pipeline and one day made the decision on a whim to doom us to an eternity of emoji bullet points?
I think it likely performed well in A/B preference tests with chat users. I've noticed Claude does far fewer listicles than ChatGPT. I suspect that they don't blindly follow supervised learning feedback from chats as much as ChatGPT. I get Apple vs Google design approach from those two companies, in that Apple tends not to obsess over interaction data, instead using design principles, while Google just tests everythi…
Turns out you can get away with a lot when you have a quasi-monopoly on an addictive product, and you buy out your realistic competitors...
Earlier quoted context omitted.
Because the reviewer ends up doing the real work actually checking it works. The laziness is offloading work down the line.
That has nothing to do with using AI, if the dev didn't check their work then that is being a bad dev.
One day, 100 orders come in for you to update. The next day, you get 50 orders to update. Did your productivity just get cut in half? If you get 200 orders on the third day, did you just quadruple your productivity from the previous day?
Solution: managers need to ask 'how does $THING_YOU_MADE actually work?'.
Pre-AI, it could be taken for granted that if someone was skilled enough to write complex code/documentation then they have a sound understanding of how it works. But that's no longer true. It only takes 5 minutes of questioning to figure out if they know their stuff or not. It's just that managers aren't asking (or perhaps aren't skilled enough to judge the answers).
On the issue of over-enthusiasm from upper management, this may be only temporary since it makes sense to try lots of new ideas (even the crazy ones) at the start of a technological revolution. After a while it will become clearer where the gains are and the wasteful ideas will be nixed.
The cope-ism in this blog post is palpable. The author is genuinely offended that someone who doesn't know how to code is daring to invade his turf. It's pretty sad that this is how he is reacting. I, for one, welcome the new paradigm shift of vibe coders entering the field. I still think I have a competitive advantage with my 30+ years of coding experience, but I don't think it's wrong for vibe coders to enter my tu…
> "Requirements documents that were once a page are now twelve. Status updates that were once three sentences are now bulleted summaries of bulleted summaries. Retrospective notes, post-incident reports, design memos, kickoff decks: every artifact that can be elongated is, by people who do not read what they produce, for readers who do not read what they receive." Great article. The "elongation" of workplace artifact…
The OP has an amusing side point - LLMs have automated sucking up to management. There is a large market for that. His main point, though, is this: I have a colleague ... who spent two months earlier this year building a system that should have been designed by someone with formal training in data architecture. He used the tools well, by the standards by which use of the tools is currently measured. He produced a gre…
My friend built a construction management SaaS entirely via Claude. It looked damned impressive, and it kind of worked to demo, but he is in no way a programmer, though he understood the problem domain very well. I asked a few basic questions: - where is the data stored? - How would you recover from a database failure? - does it consume tokens at runtime? - what is the runtime used at the back end? - why are the web…
Verifying LLM output needs to occur every time LLM output is generated, so no it doesn’t just take 10 minutes.
It takes 10 minutes + time to change the LLM input + 10 minutes to verify it worked * ~the number of times the code is generated.
Which is why vibe coding is so common, if you actually care about quality LLM’s are a near endless time sink.
What is described here closely resembles my experience too. My company is full of managers who haven't written code in years. They hired an architect 18 months ago who used AI to architect everything. To the senior devs it was obvious - everything was massively over engineered, yet because he used all the proper terminology he sounded more competent to upper management than the other senior managers who didn't. When…
I think for a lot of companies, AI is a destabilizing force that their managerial structure is unable to compensate for. When you change the economics to such a degree, you're basically removing a dam - resulting in far more stress on the rest of the system. If the leaders of the org don't see the potential downsides and risks of that, they're in for a world of hurt. I think we're going to see a real surge of compani…
From the article:
> because the competence the work reflects is not the novice’s competence at all
The core of the problem is that AI allows engineers who were previously inexperienced or downright mediocre, pretend that they are talented, and a lot of management isn’t equipped to evaluate that. It’s like tourists looking at a grocery store in North Korea from their tour bus. It looks like a fully functioning grocery store from the outside, but it is mostly cutouts and plastic fruit.