We always find that small teams of locals can do much much more than a team with an unlimited number of low cost "developers". Not just because the competence of low cost devs is poor, but also the structure of how you work changes for the worse with a bigger team, for the worse with a distributed team, and for the worse with a skill-diverse team.
Thats before you get into the cultural flaws of favored destinations like India.
So we have been able to argue things like add one local + ai is better than about 20-100 Indians, depending on role and business structure needed to manage low-competence low-trust "developers". So we are planning to completely on-shore in the near future.
The bean counters are happy, and the quality of the work is improving.
I often wonder how much more productive I'd be if just a fraction the effort and money poured into LLMs was spent on better API documentation and conventional coding tools. A lot of the time, I'm resorting to using an AI because I can't get information on how the current API of some-thing works into my brain fast enough, because the docs are non existent, outdated, or scattered and hard to collate.
I don't bring huge codebases to it.
Pragmatic sure, but we're building a tower of chairs here rather than building ladders like a real engineering field.
Someone at work recently termed this “Claude Creep”. It’s so easy to generate things push you towards going further but the reality is that’s you’re setting yourself up for more and more work to get them over the line.
I agree that the efficiency and quality are very hard to measure. I’m extremely confident that when used well, agents are a huge gain for both though. When used poorly, it is just slop you can make really fast.
So lately I’ve just decided that I’ll time box things instead of set defined endpoints. And by “endpoint” I really mean “I’m done for the day” and honestly maybe thinking about it… “I’m done with this project”.
I don’t know. But the term “Claude Creep” is absolutely something I can identify with. That thing will take you down a rathole that started with just pulling in some document and ends with you completely repartitioning your file system. lol.
> I’ve had the idea that from a social perspective it’d be regarded like plastic surgery, in that it only looks weird when its over-done, or done badly.
Your friends, family, partners, coworkers, aren't going to say anything, neither are people you meet casually, certainly not service workers, strangers aren't going to pull you aside to tell you the truth about your nose job, etc.
I hope the same social taboo doesn't transfer over to AI content. We should honestly critique AI generated content, used either in-whole or in-part with human creations. If the inclusion of AI content botched your article, saying so should be socially acceptable.
We saw some of this here on HN. It used to be that when AI content would be submitted here, it was a social faux pas to even mention it was LLM generated, same thing with LLM generated comments, no matter how obvious it was. Mentioning a comment was AI was socially verboten and you'd be finger-wagged at.
Eventually, AI fatigue caused the community to discount Show HN entries, submissions and comments, and the signal to noise ratio could no longer be ignored.
Now, turn on showdead. Those same comments, that users were expected to interact with as if they were made in good faith by real people, litter every submission's comment section. These comments objectively hurt discussion and it's a good thing they're shadowbanned.
Culturally, I hope we can reach a point where critique of AI content, including code, doesn't brand critics as haters, Luddites, or worse, and stifle conversation about what our communities really value and want.
So it creates this selection effect where people only associate AI with fake and bad. The good stuff, they don't associate with AI at all.
Much of what they are doing is incomprehensible to me. I often find that being a programmer is actually holding me back in this regard, because I feel the need to understand everything the code is doing, as well as the specialized knowledge (e.g. the math involved in audio processing and sound effects). Whereas my friends can just say... yeah add a phaser effect to the synth and it just does it.
The style isn’t a limit of the technology, it’s a limit of the lobotomized models from OpenAI and Anthropic. The open source community has lots of models that are great at creative writing.
The section about being "glazed" into action resonates. Hidden within this concept I think is something profound about human motivation, innuendo and all.
> AI generated prose is at best boring, and at worst genuinely unappealing. I’m continually tempted, because in theory it should work well. The AI has perfect spelling and grammar, has more than enough context to produce article-length content, and can do in seconds what takes me hours.
I have a thesis in mind...that there is something fundamental to the human spirit that relishes a sort of friction that LLMs cannot observe or reproduce on their own.
[1] https://en.wikipedia.org/wiki/Wikipedia%3AAI_or_not_quiz [2] https://en.wikipedia.org/wiki/Wikipedia%3ASigns_of_AI_writin...
The gap between LLM-generated writing and the composite style of the average Wikipedia page is more narrow than most people may believe.
AI generated. Some of the clues include:
- Most obviously, a failed ISBN checksum
- Other source-to-text integrity issues; for example, the WWF source says very little about Malaysia specifically, only mentions Sunda tigers (Panthera tigris sondaica), and does not mention tapirs at all
- Very short yet consistent paragraph length
- Generic "see also" links, one of which is redlinked
This is not the sort of thing that I pay attention to unless I'm doing detailed research. And even then I'd probably have a bot check these for me, ironically, since it's such a mechanical job. At the very least detecting AI like this requires conscious effort.
I can easily tell AI writing. I'm sure plenty goes under the radar, but I can still catch a lot.
The more you see those patterns the more you start recognizing them. By now I can recognize quickly if a blog post or README.md was generated by Claude or ChatGPT because the signs are so obvious.
Even Hacker News comments that are AI written are easy to spot if they weren't edited. I know I'm not alone because when I recognize an AI comment I check their comment history and find other people calling out their AI-generated submissions, too.
Learning how to recognize the output of the popular AI models is becoming a critical business skill, too. You need to be able to separate out the content from someone who was doing real work that you should take seriously as opposed to the output of someone who is having ChatGPT produce volumes of text that they don't review. The people who do that will waste your time.
Ask it to write in the style of patio11 or someone else with a distinctive tone, and it will do a remarkable job.
It will pass pretty consistently. Not sure I love it.
This article doesn't have the tells, it looks human written.
My friend is trying to do the same, the Docker stack he made for his SaaS is really amazing, it is following the standards from the ancient age.
Local models are about 25 months behind the current SOTA. If that holds, businesses won't need the paid models for many things.
Not counting from 1971s DARPA? Sorry I'm allegric when LLMs being called AI like nothing existed before it.