tripledry

Not disagreeing, but I feel there is something to be said about how the tools we use nudge us to do things a certain way.

Similar to how using lambda functions and some document DB is easy and fast, but it often times leads to shit architecture as you don't have to think about it anymore, bolt on whatever cloud service X and Y for features and you have a mess.

AI is similar to cloud in that way, it's so forgiving that it pushes you towards a shit architecture as it "doesn't matter anymore".

Funnily enough AI makes the cloud mess easier to manage. These opinions are based on my own experience and most importantly, vibes.

I would say in addition to domain knowledge, it's also about intent. Code does not capture intent, it usually doesn't include the "why" just the "how".

I argue you still need communication for the "why" to get to the "how", otherwise you will just get the wrong thing.

Question, how viable are these tools and are they maintained?

I genuinely want to know if this is mostly hype or are they actually good. I don't think trying myself would really give a better picture as I'm not a user of said software.

Anyone who tried to replace photoshop with these AI clones successfully?

I think it's both. I want to agree with you but it's also just human nature, I notice myself slacking even when I care, because it's easy. The human brain is really good at finding shortcuts.

It's like saying it's the kids fault for being addicted to social medias and assigning no responsibility to the providers.

To be clear, I'm not advocating anti-AI laws or arguing with you, just blurting out my thoughts. Ultimately I simply feel this will not be a good thing for tech and human understanding in the short / medium term, but this is just based on vibes, like most arguments on AI are.

Yes. I've always suspected inference is profitable.

But I'm also interested is retraining needed? how relevant will a model be in N years if it's just "static", and if you do need to retrain and fine-tune over time, is it still profitable? how often is that needed?

For most cases it's probably ok, often times answers are general or basically a web search anyways, but if my Agent started blasting outdated syntax it would suddenly be way worse for coding for example.

> Random copy-pasta from stackoverflow anyone?

Was this a common thing? I don't know which bubble is more common, but personally I can't recall ever working with someone who couldn't explain what they did (just copy paste?), except for maybe configurations.

In my experience, people most definitely understand less of the technical implementation now vs before. Heck, not only technical, I've seen presentations from business where the presenter clearly vibed their deck and don't understand the content.

I think it's fairly likely medium to large corporations don't pay anywhere close to the listed API pricings as they get deals through existing partnerships with the big cloud providers.

Also think it depends on language, literally asked 2min ago from chatGPT (no login so maybe it's a shittier model?)

> Hur många 'r' I abborre, använd inte web search? Det finns 3 r i abborre.

And I explicitly had to say not to search the web, because that's what it did by default, to count letters in a word...

Huh, was not aware of that. Interesting

> Synthesizing complex information from a range of technical sources enough to build bioweapons is cognitively impenetrable for the vast majority of people. The ability of AI to synthesize this information into a step-by-step recipe to follow is a step change in accessibility.

I agree with this part.

> But as the barriers to entry to bioterrorism fall away and become widely accessible we may see the cost/benefit shift.

But I argue that the barrier to entry is not about information, it's about needing to build, execute and maintain a lab and production facility? It's not the theoretical part that's the bottleneck.

Or maybe I'm overestimating the difficulty of implementing such a thing, this is not in my area of expertise.

Is this something that happens by accident in the sense that the decision makers didn't know how it would sound or is there actually someone in a room at some point going "fuck it".

But hasn't bio attacks already been possible before AI, what has changed? Maybe the barrier to entry lowered, but one could argue the availability of info has not been the bottleneck for as long as the internet has existed.

The threat is ofc real, but AI won't magically "do the thing" still, it's not code that's the bottleneck AFAIK? Correct me where I'm wrong.

I'm not disagreeing with your comment, should probably have made that clear.

The SaaS to inhouse argument is just something I've heard that came to mind when reading this thread.

In stuff like wikis and ticket systems what I see is people now just making their own little tools around the ones we already use, this arguably just sements their position even deeper.

Also I dont get the inhouse argument, you dont really save anything. So instead of paying X to SaaS, you now propose to pay X to anthropic instead AND maintain said SaaS... what? how is that better, that just shifted the cost to another company and you got the burden of maintenance and responsibility.

I agree with what you said, and _sometimes_ it's true, but I still wouldn't call it a "preference". One could argue there isn't a "need" to deliver faster and come up with solutions to problems, but is it a "preference"?

Maybe I'm completely off base here, arguing about semantics.

I'm a bit slow, I don't get the C argument?

I remember fighting with business about this in a startup way before LLMs, more code, velocity or motion doesn't mean more value.

If I put on my optimist hat for a moment, I hope LLM's would finally make this obvious for people, more stuff !== more value. 250k lines of code a week isn't a flex IMO, it doesn't really mean anything out of context.

> Benchmark doesn't really show the whole story.

For me it seems like benchmarks are mostly noise, and the rest is based on vibes. Some find newer models annoying, some are amazed.

Good use case.

For some reason this reminds me A LOT of past discussions about microservices, most wonderful on paper and forever debated, but I've never seen it work out perfectly in practice, for me it's mostly been a cluster F in most companies that adopted them.

Currently I see AI similarly, in theory perfect, in practice I don't see the claimed effects. So yes, skill issue, but skills are relevant and your company probably can't hire a rockstar team (if that matters in the future).

But your comment on personal experiences was very good! Spot on, we are all biased, easy to forget. Thank you for that.

This I can believe, also seen it on a personal level.

But I'm not sure about the long-term effects of this, we just shifted the "subscriptions for apps" to "subscription for an app" AND you must now maintain said apps.

I would.

Here I am starting to think a central part of knowledge work is the knowledge gained.

Maybe you are right, maybe not, let's see. Unfortunately I kinda agree, because humans are really good at being lazy and going in the path of least resistance (including me). It's genuinely difficult to not use AI even if it makes my work worse, as long as it's easier and faster.

> ... with AI coding IS faster and quality is better than ever before.

Is this claim based on something?

I'm not against or "for" AI (whatever that means), I try to use it as effectively I can, but for me it's not at all obvious that quality is better than ever before.

Speed I can buy, especially in new projects and utilities, but quality? At least I haven't seen this in practice, if anything I'm just seeing more code, issues, PR's and pressure ==> more slop, more bugs, less quality.

You can always say "skill issue" and "process issue", but that's partly my point here, AI doesn't magically solve this.

I compare it to math, you can't learn it by reading it you need exercises. Same thing, reading is not sufficient. Just my experience.

I dont't really know stats about such things, but I assume the adoption of CC has been insane compared to almost anything, and is also like two years old? Yeah, not surprised.

But also, if they all use 500 agents daily and latest models can "one shot" everything, one would think 5k issues are fixed in no time...

Havent used CC in some time, worked fine last time I tried.

Do you have a link? I would also be very interested in seeing the prompts, from what I've seen you still need to understand the field, maybe I'm wrong.

Why is this downvoted? Genuine question, I haven't followed up on the drama.

Not only hard to understand, but if one uses it without first understanding the topic, it's a disaster.

Couple weeks ago I had someone linking to a doc page in my ticket "Made this summary, hope it helps". It was 100% AI generated, the person clearly had no understanding of the problem, it only mentioned UI changes although the thing required changes in many systems. Complete waste of time, I don't think I will humor anyone who makes AI summaries anymore.

True, but that's unfortunately (usually) not because the thing misfired randomly.

This is a good point.

However, if I put my sci-fi hat on for a second, it's not so far fetched that we figure out a way to compress models to a size where it wouldn't need all that compute.