Not everyone is using AI for everything

Have you considered just answering truthfully?

Would you even want to work somewhere where you need to play a role and where they flip out when you say the wrong word you should've correctly guessed through mind reading? That sounds not like a job but a toxic relationship.

I'm an old hat on both sides of this type of discussion from a post-grad view.

Recommendation: use it to own the conversation and to signal mutual fit. Yes, your idea of AI lover versus hesitant matters. I recommend reframing the question to pivot to your fit to the org (and org fit to you) question. Show/concisely explain how you consider whether LLMs are fit to a task and how to tell it improves outcomes.

An outcome focus and willingness to show thought process around a common use case will be a substantially strong response.

You should just be honest. If you're not a good fit for the company then you should honestly be eager to discover this.

> I've been responding with a sort of long winded answer

"I don't. I personally don't find value in them for the type of work I do. I am also uncomfortable with using their outputs under the current copyright regime. I also question how competitive any organization can possibly be if LLMs become the main driver of their work products."

> I've had more bad results than good the few times I've tried them

"I prefer to write correct code rather than debug bad code generated from a limited context window."

I am really interested how someone can make a 100x of themselves when working. This often means "how are you using claude code", "what is your coding setup", "how do you decide if your agent is going in the right direction or not w/o looking directly at the massive amount of code generated". This is not technical, but it measures how much we would have to teach someone that comes from a non-industry background. Remember that companies have money, universities dont pay for a 200 USD Codex+Claude+Grok+Cursor per employee.

That said, I also had to ask the same stuff to folks from big techs that dont actually are software engineers. How are data scientist leveraging LLMs to automate experiments? Are they using it for something other than data viz? Are they using it for /loop exploration? how trustworthy are the agents right now for this?

Having someone with this grasp nowadays might be very very valuable IMO

- Any long-winded answer to a question is immediate out and has been for years.

- Not having used agents and not being able to comment on what to do and what not to do with them is immediate out since early this year.

I think you will find that both AI-pilled as well as AI-sceptics will actually agree on that approach, because it rejects the question of "how much" and rather reflects it towards the "when to use AI". Depending on who it is, you can then talk more about the beginning, end or center piece of it.

That's probably not going to be enough for AI maxxers, but it probably won't be too much of a turn off for anyone but the most extreme AI minners, and everyone in between will probably be fine with it.

Frankly I plan to steer well clear of any "the majority of our code is AI generated" shops for the foreseeable future. Seems like disasters waiting to happen and I'd rather let other people step on those rakes

Having been in academia in the past and now in software I can say with a lot of certainty that this will take a lot more upfront work than otherwise.

Academic code does not have a lot of structure. And usually lacks a lot in terms of tests. While AI is best when it can mimic patterns as well as there are tests to target.

So you will probably need to budget a few weeks to establish good patters, docs as well as testing patterns before you can seriously make it really do what you want it to do.

The UI/UX is built around one-shotting the code as a single fully baked unit. There should be intense competition around having the best code review tools to adjust the fine details and yet everything is devolving into a text prompt, even CAD tools where being off by a millimeter or less can make a part completely useless even if it is 99% of the way there.

All of these tools are assuming that the LLM already achieved superhuman AGI capable of reading your mind while making zero mistakes.

That this doesn't have a clear and obvious answer one can expect shows how the issue is politics, not strategy.

When you apply as a mechanic, there is no such weird political debates about certain power tools where people have passionate opinions on which tool to use.

The analogy I've had for myself is that it feels like using a bulldozer to dig rather than a shovel. If you use it to dig archaeological artifacts, it can make things worse than you started. A lot of the work however, is just moving dirt around, so you are wasting time by using a shovel.

personally i find this offensive and would disqualify the candidate.

The reason a technical interviewer will be asking this question is because they want to see how you adapt to using new technologies, LLMs being one of the most disruptive technology that has hit the tech industry since at least the internet. You will likely be expected to use LLMs and they will want to know that you are someone who truly understands the capabilities of them - upsides and downsides, where to use them, what guardrails you need to put in place.

I'd encourage you to revisit the re-factoring task you worked on. Work out why it didn't work, work out what didn't work about it and if you have the chance try again, but use different techniques, there's a lot of conversations going on about what people find working and not working - try to join that conversation. Try to document what you learn. Then in the interview discuss these rather than just saying you gave up. The interviewer isn't going to check up on how successful your project was, they just want to know how you think and how you approach problems.

It saddens me to see that high quality content is drowned in this sea of garbage to the point of being almost impossible to find.

Swift, not so much. It's relatively new. Looking at AI's abilities like an engineer's career span scaled about 10-20x of time makes it make a bit more sense.

It's going to be worse at newer/niche things, intuitively - which is only going to get worse as it "learns" from garbage outputted by other LLMs moving forward.

But I've seen Claude write crazy code in Python and JavaScript, too

The classic AI Gell-Mann effect.

EG: That the entire conversation is juggled by the token selling shills, per usual.

Gemini still isn't sure what details are in the version of OBBA that actually passed, because there was more discussion about various proposals (that didn't make it into the bill) than there was about the final version of the bill itself.

Unfortunately, it's an intractable problem based on the ways that LLMs work. In order to overcome those limitations, you have to provide so much detail to the prompt that you would find the answer faster searching manually.

Instead of using the LLM to create deterministic tools, we are using LLMs to replace them. It's completely backwards and I don't know why people (especially high ranking people in my company at least) seem to think that this is the way forward. No, I don't want a whole CI pipeline that is just LLM prompts. Yes it's very easy, but it's expensive, slow and prone to failure in ways you can't even predict.

Same things like using LLMs for the code review process. What would have been a simple linting rule is now a pass with an LLM rather than using the LLM to create the linting rule, which it is absolutely excellent at creating.

[1] https://thedailywtf.com/articles/Classic-WTF-No-Quack

The AI psychosis is a real thing.

The issue is, they don't want to provide "better" support but "cheaper" support. Imagine a trained agent that understands the big picture. Now imagine a company investing in humans to use AI to retrieve knowledge that the human can easily identify as being relevant or not, and using that knowledge to better aid the customer.

Right now AI is being sold as a "we don't need support personells" instead of "how can we provide better service." For a lot of products, better service will probably not matter as "cheaper" products will win most of the time.

Most people don't want to pay for better. They want to pay the same for something better, which is what companies are not investing their time in figuring out how to use AI properly for I think.

Now that’s real value.

Edit: The deciding factor being whether you want to figure out if people are interested in AI / find it useful, or if the question you seek answered is more akin to "X% of people consume lead in their food"

I am guessing the threshold is when changes outgrow mere consumer convenience and start being something imposed without choice.

https://nces.ed.gov/surveys/piaac/measure.asp?section=1&sub_...

I'm curious as to how I would score, I would definitely count myself as "literate" but I wonder how well I'd do on the level 4/5 tasks and if they cross over into more general memory, intelligence, and study habit metrics that even a normally "literate" person would not do well at.

Though given those descriptions I can't help thinking those would be great tests for AI. I'd love to see the proficiency scores for various models.

EDIT: Ok I just needed to scroll further, they have sample items in the last section up to level 4 and even at level 4 the question seemed trivial.

The most wordy one is the Q Drum article (which by the way Q drum is a real thing, kinda neat idea) and there's literally only two basic criticisms (flat land and expense) and if you had any idea what the life straw is you can probably construe what the similar criticism in the email is going to be without even looking.

Based on the scores and the proficiency description I assumed they were actually targeting some sort of normal distribution and levels 4/5 would be genuinely difficult explaining the scores. I'm now much more sad that the scores are so low.

At least I got a laugh at how they refer to each test item as "the stimulus" which has such a sterile/clinical flavor to it.

These stats don't pass the smell test. About a third of people in the US have a bachelor's degree, but only 13% can pass level 4/5 literacy challenge? If you dig into the sample questions, they are not hard. A level 4 task has the person read a short article and pull out the criticisms of some products.

I know not everyone with a bachelor's degree is 'smart' but it's hard to believe 2/3rds couldn't pass level 4/5.

Also 13% have a master's degree, does that mean those 13% are the only people passing level 4/5?

https://en.wikipedia.org/wiki/Educational_attainment_in_the_...

Google has search results still? I don't use Google much anymore (thanks Kagi), but this is what ends up showing for me, I don't even see any search results anymore: https://i.imgur.com/eHIA2Df.png It seems like it's 50/50 on page reload if the LLM-reply UI expands automatically or not, which covers my entire screen. I guess Google is doing some A/B testing perhaps.

If I wanted to read an AI response to a query, I would use that tool specifically - I consider them different tools entirely.

It's definitely taken some time to get into the habit - perhaps defaulting to Google as the search engine was my first mistake.

However, most people I've seen run a quick search DO in fact just read the AI response (often with a caveat of "this is just from AI but..." which I appreciate), because for a lot of things it IS good enough or exactly what they needed.

Anyone who does a search and accepts the first answer just doesn't care much or is incompetent. Anyone with any critical thinking whatsoever does way more than that if they want a correct answer.

"followed up by someone with 5k karma"

Incorrect. Maybe the reply was made in haste

Anytime a comment of mine receives lots of upvoites thare will always be some snarky replies. It's almost guaranteed

These will sometimes include personal attacks

Note all I did here was relate a personal anecdote: I'm not using "AI". HN commenters do this every day, many times off-topic

I expressed no opinion. I did not make any argument, prediction or "recommendation". All I did was state a fact, 100% on-topic

Also, Gemini is free or at least has much higher usage limits than ChatGPT or Claude, and it's well integrated into Android and soon Apple with their new Siri, so things like circle to search just work well.

This is a pattern I encourage - the AI might not be reliable, but with coaching, it can produce reliable tools. `colordiff` was causing issues with `less` when I was looking at diffs (character encoding issues I think), and when I asked Kimi K2.6 what to do, it built me a rust command-line diff tool in one shot that I've been using ever since (it even downloaded rust, wrote the tool, and compiled it).

Local models are highly likely to dominate in the long run as "good enough" inevitably becomes trivially cheap. This is a very different pattern of incentives and adoption compared to the internet.

I think it's more similar to the advent of personal computers. They had a brief surge and then turned into something else (smartphones, cloud, etc.) for all but a few niche cases. AI is not changing the consumer landscape. It's getting absorbed into existing platforms where there's a clear use case and benefit. It's just another expected software feature. This is far from the first time people have rejected a "personal assistant" concept and they'll just keep rejecting it.

[1] https://sparktoro.com/blog/new-research-20-of-americans-use-...

and for the ones that are using it (especially the paid subs). the lure is undeniable.

The moment you have to interact with the physical world or humans (psychological, imaginative, aesthetic, etc), there are often undiscovered or changing rules—or no rules at all. Or systems are subject to perturbations beyond a defined scope.

The other thing I believe is software developers are experts at doing the things that allow them to make doing those very things easier and more automated. And they do this in public, perfectly documented online.

Both because of the things I described above and because software developers have created the largest machine-accessible training set for plying their trade of any trade, ML—that is ultimately interpolating massive datasets to do things—is unsurprisingly uniquely successful for software tasks.

The less popular a language, the more models struggle.

Writing, UI, and presentations have similar knowledge bases.

Outside of those, quality becomes much more hit and miss. If you ask for a recipe you may get something good, or you may get something completely inedible and random.

"Domain specific knowledge" really means "strong foundations and relevant abstractions" and LLMs just don't do that reliably.

> Computers should adapt to people. Asking people to make themselves more legible to software — to turn themselves into a database — is a doomed idea.

I've been in software a long time, and I do sort of see this trend, but I think it's because these are tools that build other tools. The interface has always been a 'best I can do for now' thing, with the focus on doing things that are useful. Computers were just calculators in the beginning, which led to more complex calculators, instruction sets, programming languages, operating systems, GUIs, interconnectivity, etc.

What people are doing today is experimenting, like they always have. They're putting their experiments out there so that others can use them and build on them. Some will use those tools to build other tools, and some won't. But over time, the experiments that work will get distilled and turn into real products that people who 'do not yearn for automation' will still want to use, so it seems like the value is there.

I guess the real question is whether they will create value that offsets the near-term costs, because I don't think the billions in investments are sustainable, and I'm not convinced the centralized data center paradigm is the right way.

the tech is pretty good at helping identify simple bugs when they happen and to write short sections of code given very explicit instructions but yeah I have yet to see good examples of short one sentence ideas turned into a working product that looks better than anything that could be a UDemy tutorial app.

But more likely it’s just that those who still listen to Hard Fork agree with the conclusions before listening to the podcast.

Additionally when the finally bubble bursts and the executives wake up from psychosis and look to distance themselves from this because it's become a dirty word, you'll be one of the first to go. The nail that sticks out gets hammered down and all that.

I do think there are real benefits and productivity gains with this technology, but it does not benefit everyone equally. It's great for the programming parts of my job, but useless in the other 40% of the work. I have coworkers for whom generative AI has no obvious practical application, and yet management is trying to find a way to shoehorn it in anyway. No doubt because they've also drank the kool-aid and are eager to reduce headcount.

This attitude of it making everything more productive and anyone who doesn't follow will be left behind is not just false, it's cruel and myopic. You're talking about people's livelihood being taken away because a handful of executives decided this is how things should work despite the MASSIVE number of shortcomings and poor product market fit.

Edit: I also almost missed where you're seemingly celebrating the devaluation of human labor as a result of this. Please stop and reflect on how your position may read to someone who is just trying to put food on the table.