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know what this will be about without reading Python 3.12-style type annnotations are a good example imo, no one uses the type statement because dataset inertia
As long as the AI is pulling in the most recent changes it wouldn't seem to be stiflling.
If AI stifles the relentless churn in frontend frameworks then perhaps it's a good thing.
Earlier quoted context omitted.
Does that really matter to most companies/developers? I’d much rather have a good enough solution with a large ecosystem built around it. It also takes a lot of investment for companies to change their tech stack
> Does that really matter to most companies/developers? If you're asking about performance and memory, then yes, it does. This is especially true in e-commerce where many studies have shown that overall page performance has a correlation to conversion. Add to that the fact that a lot of e-commerce has moved to mobile web, there's a strong case for picking the best performing technologies versus developer preference -…
And you still pay a few cents extra in power used because of all those inefficient and memory hungry "applications". You just don't notice.
Earlier quoted context omitted.
Does that really matter to most companies/developers? I’d much rather have a good enough solution with a large ecosystem built around it. It also takes a lot of investment for companies to change their tech stack
> Does that really matter to most companies/developers? If you're asking about performance and memory, then yes, it does. This is especially true in e-commerce where many studies have shown that overall page performance has a correlation to conversion. Add to that the fact that a lot of e-commerce has moved to mobile web, there's a strong case for picking the best performing technologies versus developer preference -…
I think if you specify a technology in your prompt, any LLM should use that technology in its response. If you don't specify a technology, and that is an important consideration in the answer, it should clarify and ask about technology choices, and if you don't know, it can make a recommendation. LLMs should not have hard-wired preferences through providers' prompt structure. And while LLMs are stochastic parrots, an…
If AI stifles the relentless churn in frontend frameworks then perhaps it's a good thing.
It isn't only frontend frameworks. I currently AI-coma / tab-complete C++17 with decent results for stuff ridiculously far away from frontend, but I do wonder who is providing the training data for C++23 and onwards as there isn't wide adaptation yet.
I specifically found it very useful when dealing with a bunch of boilerplate C++ shim code that used some advanced template magic to declare families of signatures for typed thunks that wrapped and augmented some underlying function call with record/replay log.
It was arcane, bespoke stuff. Highly unlikely to be imitated in training data. The underlying code was written by a colleague (the CTO) but it was hard to grok just because of all the noise the template magic added to the syntax, and carefully keeping track of what was getting substituted where.
The changes I was making were mostly to add new shims to this collection of shims, and co-pilot happily used the pattern of shims in previous lines of code to extrapolate what my new shims should look like and offer me tab-completions that were sensible.
That included some bad guesses (like inferred names for other templated forms that referred to different argument types), but overall it was structurally close enough to use as a reference point and fix up as needed.
It was really great. Took what would have been about day's worth of work carefully figuring out from first principles how the template system was structured.. and made it into about half an hour of "get it to generate the next shim, verify that the shim does the right thing".
I think if you specify a technology in your prompt, any LLM should use that technology in its response. If you don't specify a technology, and that is an important consideration in the answer, it should clarify and ask about technology choices, and if you don't know, it can make a recommendation. LLMs should not have hard-wired preferences through providers' prompt structure. And while LLMs are stochastic parrots, an…
As others have pointed out, it's a flywheel: Popular library gains traction → LLMs are trained to produce the “most likely response”, which naturally aligns with what’s already popular → people stop seeking alternative solutions and instead double down on the existing mainstream choices. (Hypothetically) It's not that OpenAI decides to push AWS, it's just that at the time it was trained AWS was the only real option so it just regurgitates a common view from a point in time.
To extend your analogy, the more realistic scenario isn’t that kids slip in extreme view points and take them as ground truth in their history assignments, it’s that they don’t take a stance on anything at all: Their essays become like CSPAN transcripts, perfectly regurgitating what happened without taking any position or applying any critical thinking one way or the other.
Imagine kids writing about civil rights, but all their reference material was stuck in time at 1953: That's what's more likely to happen.
Pathologists as a specialty has been grousing about this for several years, at least since 2021 when the College of American Pathologists established the AI Committee. As a trivial example: any trained model deployed will necessarily be behind any new classification of tumors. This makes it harder to push the science and clinical diagnosis of cancer forward. The entire music community has been complaining about how o…
https://www.advisory.com/daily-briefing/2024/12/03/ai-diagno...
Edit: yeah, people don't like this.