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▲ shreddude 7 hours ago

I could go on and on, but Claude recently decompiled the firmware of my camper van, documented all the CAN interfaces, then programmed an ESP32 module to talk to the van’s integrated systems (power, HVAC, lighting, tanks). That sort of embedded systems integration is completely out of my wheelhouse.

I honestly don’t understand AI naysayers. I use Claude every day both professionally as a Solution Architect and personally in a variety of projects I simply could not have ever approached alone.

▲williamdclt 4 hours ago | parent | next [-]

> projects I simply could not have ever approached alone.

I think that's part of the divide between enthusiasts and naysayers. If you use GenAI on things that you couldn't approach alone, it's an incredible tool. If you use it on stuff that you're pretty good at, it's not a gamechanger (and if you're an expert, it's a minor boost at best). Many people's job are about doing what they're an expert at.

▲bawolff an hour ago | parent | next [-]

I think part of it is we often notice bad AI usage. The llm generated "art" by someone with bad taste, or the patches to open source projects by people who cant program at all and are teerrible.

If the use is half decent people just dont notice it.

▲LouisSayers 2 hours ago | parent | prev | next [-]

I find it's a huge boost for my day-to-day work.

If you work on architecture and Claude docs, then you can essentially just have it fill in the gaps. Work then mostly becomes a matter of defining what the next piece of functionality is (which you can also use Claude to help with).

The stuff that used to take days now takes hours. It's not perfect, but if you get your codebase into a good shape then the payoff is huge.

▲dawnerd 2 hours ago | parent | prev | next [-]

And in a team setting it can really accelerate tech debt especially if used by people that know just enough to be dangerous.

▲jorl17 an hour ago | parent | prev [-]

While I think this is true

> If you use GenAI on things that you couldn't approach alone, it's an incredible tool.

I think this isn't true in all cases

> If you use it on stuff that you're pretty good at, it's not a gamechanger (and if you're an expert, it's a minor boost at best).

I think even then there's a divide.

I mostly work greenfield projects (and love it!). For these, AI has been a literal game changer. Our projects are built faster, with one or two orders of magnitude more automated tests, and all quality metrics are up.

Meanwhile, nearly all of my friends complain that AI doesn't help them. But they mostly work in very large existing codebases.

Still, even in large projects I think AI (the expensive variant) has been a complete gamechanger for me. Sure, I spend a lot on tokens, but I just feel happier and enjoy what I do more. The singalong people say about "thinking at a higher abstraction level" is what I feel. I really am thinking about architecture and larger patterns, instead of the boring nitty-gritty (which wasn't boring at all when I was a kid learning to code!...)

I think a key factor in all of this, to me, has been dictation. Most of the time, I don't write -- I use voice-to-text. I don't even read what comes out of it -- the LLMs get it (it is mostly unintelligible to anyone else) .

This means when I'm planning a big feature, I give a gigantic brain dump to the LLM in perfect stream of consciousness way, going through ideas, pros and cons, edge cases, what exists, what doesn't exist, where I'm sure of something, where I'm not sure and want the LLM to browse the state-of-the-art. Sometimes I spend 20 minutes just talking to the microphone before I send the first prompt. When I pair that with Opus, I find that I am able to build much faster and to go through alternative designs much more frequently as well.

I keep trying to tell all my friends: use voice to text and braindump to the computer. But they refuse... I couldn't imagine having to type everything nowadays. Even though I'm a fast typer, it's still much slower than the speed of my thought, which, granted, is still faster than the speed of my voice.

In effect, I filter much less, but I've come to think that's positive for the good LLMs: I throw all the edge cases and what ifs I'm thinking about -- all those years of experience dealing with similar systems.

If I wanted to go back to work in-office, that would be my major problem: I need to be able to talk with my computer all the time, loudly, and pacing through my room.

▲jesse_dot_id 2 hours ago | parent | prev | next [-]

Same. I'm a DevOps engineer, so a jack of all trades master of none type of guy, and Claude Code backfills my knowledge gaps and turns me into kind of a superhero. I think it's key to already have a pretty good idea of what you're looking at, though.

▲archagon 5 hours ago | parent | prev | next [-]

[flagged]

▲donkey_brains 5 hours ago | parent [-]

Just as bad as the technical debt is the cognitive debt in your codebase. When something breaks, your only recourse is to ask the AI how to fix it, since it wrote it and you did not have time to review all of its code. Except now the code base is so large it won’t fit into the context window, and the AI can’t help you, and…you’re screwed.

▲shmoogy 4 hours ago | parent [-]

If you're vibing such complex things you should probably be in the habit of also generating detailed documentation and commits so the ai can follow breadcrumbs, add some playbooks for how to debug and it's actually pretty good. Too complex for local models context though - so you're probably still correct albeit there are ways to mitigate or delay this.

▲rvnx 7 hours ago | parent | prev [-]

I get it understand either. "This is just a stochastic parrot".

I suppose these people are lying so that they can justify their well-paid job, or they just don't know how to use LLMs or to prompt GenAI tools.

▲camel_gopher 7 hours ago | parent | next [-]

It’s a probabilistic parrot

▲foobarbecue 4 hours ago | parent [-]

What's the difference (stochastic vs probabilistic)?

Or... were you illustrating?

▲jazzyjackson 7 hours ago | parent | prev | next [-]

I’ll explain it: these tools are non-deterministic and people have different experiences with them. For a few people every interaction is totally fumbled and they think the cheerleaders of gen AI must be lying, for others the chatbot hits one home run after another and lets them add microcontrollers to their CAN bus. When these people’s good luck runs out and they start getting mixed results like the average user, they assert the service must have been down graded

▲triMichael 6 hours ago | parent | next [-]

I'll add to that: you are more likely to have a good experience if it has a lot of relevant data that it was trained on. You are also more likely to have a good experience if errors don't cause major issues.

So one-shotting a game of Snake should be great (tons of training data, errors are easily caught because it's a small program). Similar with building a lot of web UI front end, or one-shotting a personal project. On the other hand, I haven't been convinced that it's good enough to maintain large codebases or assist with niche topics that are not very well documented.

▲thewebguyd 6 hours ago | parent | next [-]

> if it has a lot of relevant data that it was trained on

This became evident to me the moment I tried to have these models work on some PowerShell tasks for me. Even Opus today struggles with PowerShell.

Since anything in PS is probably some internal sysadmin tool, there's not much public code out there outside of Microsoft's documentation. Plus the Verb-Noun naming scheme makes it really easy to just hallucinate cmdlets (which it does, often). Its easier to have the LLM just do things in python using M365 Graph API than any of the provided PowerShell cmdlets.

OTOH, I've been using Claude for a lot of Swift & Swift UI work lately and it has no problems there, and I'd imagine there's even less publicly available training data for that so to be honest I'm not entirely sure why it fails so badly at powershell.

▲lowbloodsugar 6 hours ago | parent | prev [-]

> On the other hand, I haven't been convinced that it's good enough to maintain large codebases or assist with niche topics that are not very well documented.

Same is true of humans. So far my experience is that addressing the issue with the help of AI is faster than not (ie comprehending the system and creating the documentation).

▲cauch 4 hours ago | parent [-]

I don't understand the comments of the kind of "same is true with human".

This feels a bit like whataboutism.

It also feels like people don't listen to each others.

For example, reading the previous comment, it feels like the thing that reduce the enthusiasm was that at first GenAI looks like it was "reading, understanding and using its own knowledge to answer the problem", but as soon as it is a ore niche or a more complex situation, GenAI looks like it "does not understand the code, just does the equivalent of a StackOverflow search and try to apply the solutions that it found there, and this is why it felt like it understood the code before".

It does not at all means that GenAI is not terribly useful. And even better than humans in some situations.

But it feels that answering "same with humans" is missing this point: that's the opposite, humans usually try to understand the code and are bad at covering a very large range of very well documented subjects. That's the "uncanny valley" they talk about: they assumed GenAI performance on a subject X is due to a "human-like" approach, and it feels very strange when this impression falls apart.

▲dyauspitr 7 hours ago | parent | prev [-]

I still don’t get it I can dictate a prompt and sometimes I do it so quickly the text looks like a drunken parrot dictated it and it still always gets exactly what I’m asking for. I’m just going to attribute malice to the naysayers.

▲bonoboTP 6 hours ago | parent [-]

Some people are really bad at specifying what they want to ask for. Or they already start prompting with the attitude that it can't possibly work so they don't even really try, or stop at the first failure to point and say how bad it is.

▲thewebguyd 6 hours ago | parent | next [-]

People are really, really bad at specifying what they actually want. I've worked in IT for my whole career, starting in help desk (now an IT manager). My days in the service desk was enough proof that people have no idea what they actually want, or at least, they really struggle to articulate it into words.

It's the famous "email broken, fix pls" but in the form of an LLM prompt.

▲bonoboTP 4 hours ago | parent | next [-]

Well, today's multimodal llm agents with tools would at least have a good chance to do something with even such an underspecified query. Because fixing things is simpler to specify, the agent could look at config, network settings, send a test email, take a screenshot etc and get a good idea of what's broken. But when you want some new feature or new app, you can't do without actually asking for specifics, or at least you shouldn't complain if it didn't read your mind correctly. Or at least accept that you have to iterate. I think many average people can get this if they are motivated, and they can incrementally say what they don't like even in vague terms and it can get better. But some just stop without trying to ask for changes.

It can be frustrating to observe people interacting with these things. But it was just as frustrating 20 years ago, so maybe it's just a constant.

▲rvnx 4 hours ago | parent | prev [-]

Similarly, doing service desk, the thing that makes me flip the table is how people start by explaining what does not work, instead of explaining what they are trying to do.

▲bonoboTP 4 hours ago | parent [-]

It's hard even at the highest levels, such as in writing scientific papers or doing scientific conference talks. People just generally have a hard time to step outside of their context and think with the head of someone who has a different set of facts and assumptions in their context. It's hard to know how much context you both share, and how to tailor the explanation so you also don't start from Adam and Eve but you explain just enough context and strip irrelevant tangents.

I don't think this is just about intention and willingness, it's just simply hard.

▲skydhash 5 hours ago | parent | prev [-]

Or maybe people see how complex the code is and all the failure points, and don’t feel it’s ethical to use the output. In most of the comments, the most relevant point is that the poster is not an expert in the domain they got helped. While they can observe the result, they don’t have a causal model of the situation.

▲amelius 4 hours ago | parent | prev [-]

I still would like to hear a public apology from the stochastic parrot crowd for their deceptive framing. Or maybe it was just incompetence.