341–350 of 442 posts
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> Maybe some product will become a hell with millions of lines of code that no one knows how to evolve and manage. That is exactly what will happen, so why would you do that?
On the other hand you should ask yourself why do you care? If you assume no human will ever read the code except in very extraordinary circumstances, why wouldn’t you do that?
Also it doesn’t hurt that React has quite a stable/backwards compatible API, so outdated snippets probably still work… and in Tailwind’s case, I suspect the direct colocation of styles with the markup makes it a bit easier for AI to reason about.
Like several other commenters in this thread, I also wrote[0] something recently on a related topic: Google's AI Overviews and ChatGPT harm the discovery of long tail information - from a product builder's perspective. Basically, users are having a tougher time finding accurate info about your product ( even if the correct answer to their query is in Google's own search results ). And I also found the basic tier of C…
> Basically, users are having a tougher time finding accurate info about your product (even if the correct answer to their query is in Google's own search results). That's a gigantic "even if". In my experience, I'm able to find stuff much easier with LLM's that Google search couldn't surface. If I'm looking for a product that does exactly X, Y but doesn't Z, keyword search can be pretty terrible. LLM's actually unde…
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> The entire music community has been complaining about how old music gets more recommendations on streaming platforms, necessarily making it harder for new music to break out. Compared to what though? Compared to Limeware/Kaazaa back in the day, or compared to buying records in a store? Personally, I find it easier than ever to find brand new music, mostly because Spotify still surfaces new things with ease for me (…
> Compared to what though? Compared to Limeware/Kaazaa back in the day, or compared to buying records in a store? Compared to curation by other humans. Be it music labels, magazines, radio DJs, or a person sharing their playlist or giving you a mixtape. In this model, tastes never overlap perfectly, so you're exposed to unfamiliar music fairly regularly, often in some emotional context that makes you more likely to a…
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> Once you add in other concerns: security, performance, maintainability, it can fall apart. Does anyone care about that? E.g. CRWD is all time high, after all. There is zero need to change anything according to market.
> Does anyone care about that? Yes
The answer to this seems obvious: continuous training of live models. No more "cutoff dates": have a process to continually ingest new information and update weights in existing models, to push out a new version every week. Note that I said "obvious", not "easy", because it certainly isn't. In fact it's basically an unsolved problem, and probably a fiendishly difficult one. It may involve more consensus-based approac…
Also, if the proportion of training data available is larger for more established frameworks, then the ability of the model to answer usefully are necessarily dictated by the volume of content which is biased towards older frameworks.
It might be possible with live updating to get something about NewLibX but it probably would be a less useful answer compared to asking about 10YearOldLibY
> if people are reluctant to adopt a new technology because of a lack of AI support, there will be fewer people [emphasis added] likely to produce material regarding said technology, which leads to an overall inverse feedback effect. Lack of AI support prevents a technology from gaining the required critical adoption mass, which in turn prevents a technology from entering use and having material made for it,
At present. But what if this is a transient? It depends on the new technology's dev team being unable to generate synthetic material. What happens when they can create for themselves a fine tune that translates between versions of their tech, and between "the old thing everyone else is using" and their new tech? One that encapsulates their "idiomatic best practice" of the moment? "Please generate our rev n+1 doc set Hal"? "Take the new Joe's ten thousand FAQ questions about topic X list and generate answers"? "Update our entries in [1]"? "Translate the Introduction to Data Analysis using Python open-source textbook to our tech"?
The quote illustrates a long-standing problem AI can help with - just reread it swapping "AI support" to "documentation". Once upon a time, releasing a new language was an ftp-able tar file with a non-portable compiler and a crappy text-or-PS file and a LISTSERV mailinglist. Now people want web sites, and spiffy docs, and Stack Overflow FAQs, and a community repo with lots and lots of batteries, and discuss, and a language server, and yes, now LLM support. But the effort delta between spiffy docs and big repo vs LLM support? Between SO and LLM latency? That depends on how much the dev team's own LLM can help with writing it all. If you want dystopian, think lots of weekend "I made my own X!" efforts easily training transliteration from an established X, and running a create-all-the-community-infrastructure-for-your-new-X hook. Which auto posts a Show HN.
AI could at long last get us out of the glacial pace of stagnant progress which has characterized our field for decades. Love the ongoing learning of JS churn? Just wait for HaskellNext! ;P
[1] https://learnxinyminutes.com/ https://rigaux.org/language-study/syntax-across-languages.ht... https://rosettacode.org/wiki/Category:Programming_Languages ...
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Did you read the article? It makes valid points that your comment doesn't address. It isn't a brainless "AI is making us stupider" post.
Yes, but do search engines not have a bias towards existing technologies?
The answer to this seems obvious: continuous training of live models. No more "cutoff dates": have a process to continually ingest new information and update weights in existing models, to push out a new version every week. Note that I said "obvious", not "easy", because it certainly isn't. In fact it's basically an unsolved problem, and probably a fiendishly difficult one. It may involve more consensus-based approac…
While you might be able to continuously update the model, are you able to continuously update the moderation of it? As the article says, it takes time to tune it and filter it; if you allow any content in without some filtering of outputs you might end up with another Tay. You'd have to think the liability would slow down the ability to simply update on the fly. Also, if the proportion of training data available is l…
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The model and features available to it matters. o1 says "I’m not aware of a widely recognized or universally known product called “CurrentKey Stats,” so it may not be a mainstream or highly publicized app. There are a few possibilities, though:" and then some guesses. o3-mini is similar: "I couldn’t find any widely recognized information about an app specifically called CurrentKey Stats. It’s possible that:" When I t…
Fascinating. I would have thought the fancier models would have definitely known about it. The app is six years old, has ~120 ratings globally with ~4.5 stars, with several articles written about it etc.. It should be pretty widely available in the training data