[sockgrant]

“As a designer…”

IMHO the bleeding edge of what’s working well with LLMs is within software engineering because we’re building for ourselves, first.

Claude code is incredible. Where I work, there are an incredible number of custom agents that integrate with our internal tooling. Many make me very productive and are worthwhile.

I find it hard to buy in to opinions of non-SWE on the uselessness of AI solely because I think the innovation is lagging in other areas. I don’t doubt they don’t yet have compelling AI tooling.

[SoftTalker]

I'm a SWE, DBA, SysAdmin, I work up and down the stack as needed. I'm not using LLMs at all. I really haven't tried them. I'm waiting for the dust to settle and clear "best practices" to emerge. I am sure that these tools are here to stay but I am also confident they are not in their final form today. I've seen too many hype trains in my career to still be jumping on them at the first stop.

[01100011]

It's time to jump on the train. I'm a cranky, old, embedded SWE and claude 4.5 is changing how I work. Before that I laughed off LLMs. They were trash. Claude still has issues, but damn, I think if I don't integrate it into my workflow I'll be out of work or relegated to work in QA or devops(where I'd likely be forced to use it).

No, it's not going to write all your code for you. Yes your skills are still needed to design, debug, perform teamwork(selling your designs, building consensus, etc), etc.. But it's time to get on the train.

[noduerme]

I'm a SWE and also an art director. I have tried these tools and, the way I've also tried Vue and React, I think they're good enough for simple minded applications. It's worth the penny to try them and look through the binoculars, if only to see how unoriginal and creatively limited what most people in your field are actually doing if they find this something that saves them time.

[sanmon3186]

Why would you wait for dust to settle down? Just curious. Productivity gains are real in current form of LLMs. Guardrails and best practices can be learnt and self imposed.

[code51]

I'm surprised these pockets of job security still exist.

Know this: someone is coming after this already.

One day someone from management will hear about a cost-saving story at a dinner table, the words GPT, Cursor, Antigravity, reasoning, AGI will cause a buzzing in her ear. Waking up with tinnitus the next morning, they'll instantly schedule a 1:1 to discuss "the degree of AI use and automation"

[gherkinnn]

No harm in running them in isolated instances and see what happens.

Feed an LLM stack traces or ask it to ask you questions about a topic you're unfamiliar about. Give it a rough hypothesis and demand it poke holes in it. These things it does well. I use Kagi's auto summariser to distil search results in to a hand full of paragraphs and then read through the citations it gives me.

Know that LLMs will suck up to you and confirm your ideas and make up bonkers things a third of the time.

[reactordev]

>I really haven’t tried them.

You are doing yourself a huge disservice.

[devjam]

Nothing is in its "final form" today.

I'm a long time SWE and in the last week, I've made and shipped production changes across around 6 different repos/monorepos, ranging from Python to Golang, to Kotlin to TS to Java. I'd consider myself "expert" in maybe one or two of those codebases and only having a passing knowledge of the others.

I'm using AI, not to fire-and-forget changes, but to explain and document where I can find certain functionality, generate snippets and boilerplate, and produce test cases for the changes I need. I read, review and consider that every line of code I commit has my name against it, and treat it as such.

Without these tools I'd estimate being around 25% as effective when it comes to getting up to speed on unfamiliar code and service. For that alone, AI tooling is utterly invaluable.

[dcre]

The tools have reached the point where no special knowledge is required to get started. You can get going in 5 minutes. Try Claude Code with an API key (no subscription required). Run it in the terminal in a repo and ask how something works. Then ask it to make a straightforward but tedious change. Etc.

[tmikaeld]

I’m in the same position, but I use AI to get a second opinion. Try it by using the proper models, like Gemini 3 Pro that was just released and include grounding. Don’t use the free models, you’ll be surprised at how valuable it can be.

[jatora]

I hope I am never this slow to adapt to new technologies.

[bostik]

Right now I see "use AI" to be in the same phase as "add Radium" was shortly after Curie's discovery. A vial of magic pixie dust to sprinkle on things, laden with hidden dangers very few yet understand. But I also keep in mind that radioactivity truly transformed some very unexpected fields.[ß]

AI and LLMs are tools. The best tools tend to be highly focused in their application. I expect AI to eventually find its way to various specific tool uses, but I have no crystal ball to predict what those tools might be or where they will surface. Although I have to say that I have seen, earlier this week, the first genuinely interesting use-case for AI-powered code generation.

A very senior engineer (think: ~40 years of hands-on experience) had joined a company and was frustrated by lack of integration tests. Unit tests, yes. E2E test suite, yes. Nothing in between. So he wrote a piece of machinery to automatically test integration between a selected number of interacting components, and eventually was happy with the result. But since that was only a small portion of the stack, he would have had to then replicate that body of work for a whole lot of other pieces - and thought "I could make AI repeat this chore".

The end result is a set of very specific prompts, constraints, requirements, instructions, and sequences of logical steps that tell one advanced model what to do. One of the instructions is along the lines of "use this body of work I wrote $OVER_THERE as a reference". That the model is building iteratively a set of tests that self-validate the progress certainly helps. The curious insight in the workflow is that once the model has finished, he then points the generated body of work to another advanced model from a different vendor, and has that do an automated code review, again using his original work as a reference material. And then feeds that back to the first model to fix things.

That means that he still has to do the final review of the results, and tweak/polish parts where the two-headed AI went off the rails. But overall the approach saves quite a lot of time and actually scales pretty much linearly to the size of the codebase and stack depth. To quote his presentation note, "this way AI works as a highly productive junior that can follow good instructions, not as a misguided oracle that comes up with inventive reinterpretations."

He made modern AI repeat his effort, but crucially he had had to do the work at least once to know precisely what constraints would apply. I suspect that eventually we'll be seeing more of these increasingly sophisticated but very narrowly tailored tooling use cases to pop up. The best tools are after all focused, even surgical.

ß: Who could have predicted in 1900 that radioactive compounds would change fields ranging from medicine to food storage?

[perfmode]

How could you not at least try?

[mrweasel]

I work as an SRE, I have tried LLMs, they barely work. You're not missing out.

Or, more correctly, they don't work well for my problems or usage. They can at best answer basic questions, stuff you could lookup using a search engine, if you knew what to look for. They can also generate code for inspiration, but you'll end up rewriting all of it before you're done. What they can't do it solve your problem start to end. They really do need a RTFM mode, where they will just insult you if you're approach or design is plain wrong or at least just let you know that it will now stop helping as you're clearly of the rails.

We need to bubble to pop, it'll be a year or two, the finance bros aren't done extracting value from the stonks. Once it does, we can focus on what's working and what isn't and refine the good stuff.

Right now the LLMs are the product, and they can't be, it makes no sense. They need to be embedded within product, either as a built in feature, e.g. CoPilot in Visual Studio, or as plugins, like LSPs.

Clearly others are having more luck with LLMs than I do, and do amazing projects, but that sort of illustrates the point, their aren't ready and we don't have a solution for them to be universally useful (and here I'm even restricting myself to thinking about coding).

[Aeolun]

You don’t have to jump on the hype train to get anything out of it. I started using claude code about 4 months back and I find it really hard to imagine developing without now. Sure I’m more of a manager, but the tedious busywork, the most annoying part of programming, is entirely gone. I love it.

[alfiedotwtf]

> I'm not using LLMs at all

You’re deliberately disadvantaging yourself by a mile. Give it a go

… the first one’s free ;)

[monkaiju]

All I see it doing, as a SWE, is limiting the speed at which my co-workers learn and worsening the quality of their output. Finally many are noticing this and using it less...

[whstl]

I recently had a very interesting interaction in a few small startups I freelanced for recently.

In a 1-year company, the only tech person that's been there for more than 3-4 months (the CTO), only really understands a tiny fraction of the codebase and infrastructure, and can't review code anymore. Application size has blown up tremendously despite being quite simple. Turnover is crazy and people rarely stay for more than a couple months. The team works nights and weekends, and sales is CONSTANTLY complaining about small bugs that take weeks to solve.

The funny thing is that this is an AI company, but I see the CTO constantly asking developers "how much of that code is AI?". Paranoia has set in for him.

[overfeed]

Your probably bosses think it's worth it if the outcome is getting rid of the whole host of y'all and replace you with AWS Elastic-SWE instances. Which is why it's imperative that you maximize AI usage.

[lumost]

I think the question is whether those ai tools make you produce more value. Anecdotally, the ai tools have changed the workflow and allowed me to produce more tools etc.

They have not necessarily changed the rate at which I produce valuable outputs (yet).

[awinter-py]

can you say more about this? what do you mean when you say 'more tools' is not the same as 'valuable outputs'

[gniv]

When using AI to find faults in existing processes that is value creation (assuming they get fixed of course).

[bgwalter]

If you want to steal code, you can take it from GitHub and strip the license. That is what the Markov chains (https://arxiv.org/abs/2410.02724) do.

It's a code laundering machine. Software engineering has a higher number of people who have never created anything by themselves and have no issues with copyright infringement. Other professions still tend to take a broader view. Even unproductive people in other professions may have compunctions about stealing other people's work.

[a_bonobo]

I think that's also because Claude Code (and LLMs) is built by engineers who think of their target audience as engineers; they can only think of the world through their own lenses.

Kind of how for the longest time, Google used to be best at finding solutions to programming problems and programming documentation: say, a Google built by librarians would have a totally different slant.

Perhaps that's why designers don't see it yet, no designers have built Claude's 'world-view'.

[ihaveajob]

I'm curious if you could share something about custom agents. I love Claude Code and I'm trying to get it into more places in my workflow, so ideas like that would probably be useful.

[verdverm]

I've been using Google ADK to create custom agents (fantastic SDK).

With subagents and A2A generally, you should be able to hook any of them into your preferred agentic interface

[hagbarth]

If you read a little further in the article, the main point is _not_ that AI is useless. But rather than AGI god building, a regular technology. A valuable one, but not infinite growth.

[NitpickLawyer]

> But rather than AGI god building, a regular technology. A valuable one, but not infinite growth.

AGI is a lot of things, a lot of ever moving targets, but it's never (under any sane definition) "infinite growth". That's already ASI territory / singularity and all that stuff. I see more and more people mixing the two, and arguing against ASI being a thing, when talking about AGI. "Human level competences" is AGI. Super-human, ever improving, infinite growth - that's ASI.

If and when we reach AGI is left for everyone to decide. I sometimes like to think about it this way: how many decades would you have to go back, and ask people from that time if what we have today is "AGI".

[hollowturtle]

Where are the products? This site and everywhere around the internet, on x, linkedin and so is full of crazy claims and I have yet to see a product that people need and that actually works. What I'm experiencing is a gigantic enshittification everywhere, Windows sucks, web apps are bloated, slow and uninteresting. Infrastructure goes down even with "memory safe rust" burning millions and millions of compute for scaffolding stupid stuff. Such a disappointment

[redorb]

I think chatGPT itself is an epic product, Cursor has insane growth and usage. I also think they are both over-hyped, have too much a valuation.

[signa11]

> IMHO the bleeding edge of what’s working well with LLMs is within software engineering because we’re building for ourselves, first.

the jury is still out on that...

[morshu9001]

Yeah, I'll gladly AI-gen code, but I still write docs by hand. Have yet to see one good AI generated doc, they're all garbage.

[buu700]

Incidentally, I just spent some time yesterday with Gemini and Grok writing a first draft of docs for a complex codebase. The end result is far more useful and complete than anything I could have possibly produced without AI in the same amount of time, and I didn't even have to learn Mermaid syntax to fill the docs with helpful visual aids.

Of course it's a collaborative process — you can't just tell it to document the code with no other information and expect it to one-shot exactly what you wanted — but I find that documentation is actually a major strength of LLMs.

[toenail]

The AI docs are good enough for AIs, to throw them at agents without previous context.

[dr_dshiv]

Agree. I also wonder whether this helps account for why some people get great value from AI and some terrible value.

[voidhorse]

Did you read the essay? It never claimed that AI was useless, nor was the ultimate point of the article even about AI's utility—it was about the political and monetary power shifts it has enabled and their concomitant risks, along with the risks the technology might impose for society.

This ignorance or failure to address these aspects of the issue and solely focus on its utility in a vacuum is precisely the blinkered perspective that will enable the consolidations of power the essay is worried about...the people pushing this stuff are overjoyed that so few people seem to be paying any attention to the more significant shifts they are enacting (as the article states, land purchase, political/capital power accumulation, reduction of workforces and operating costs and labor power... the list goes on)

[pigpop]

As trite as it is, it really is a skill issue still due to us not having properly figured out the UI. Claude Code and others are a step in the right direction but you still have to learn all of the secret motions and ceremony. Features like plan mode, compact, CLAUDE.md files, switching models, using images, including specific files, skills and MCPs are all attempts to improve the interface but nothing is completely figured out yet. You still need to do a lot of context engineering and know what resources, examples, docs and scope to use and how to orchestrate the aforementioned features to get good results. You also need to bring a lot of your own knowledge and tools like being fastidious with version control and being able to write extremely well defined specifications and tasks. In short, you need to be an expert in both software engineering as well as LLM driven development and even then it's easy to shoot yourself in the foot by making a small mistake.

[insane_dreamer]

That's because LLMs are optimally designed for tasks like coding, as well as other text-prediction tasks such as writing, editing, etc.

The mistake is to project the same level of productivity provided by LLMs in coding to all other areas of work.

The point of TFA is that LLMs are an excellent tool for particular aspects of work (coding being one of them), not a general intelligence tool that improves all aspects (as we're being sold).

[smoody07]

This. Design tends to explore a latent space that isn't well documented. There is no Stack Overflow or Github for design. The closest we have are open sourced design systems like Material Design, and portfolio sites like Behance. These are not legible reference implementations for most use cases.

If LLMs only disrupt software engineering and content slop, the economy is going to undergo rapid changes. Every car wash will have a forward deployed engineer maintaining their mobile app, website, backend, and LLM-augmented customer service. That happens even if LLMs plateau in six months.

[elevatortrim]

I disagree. I think, as software developers, we also mostly speak to other software developers, and we like to share around AI fail stories, so we are biased to think that AI works for swe better than other areas...

However, while I like using AI for software development, as also a middle-manager, it increased my output A TON because AI works better for virtually anything that's not software development.

Examples: Update Jira issues in bulk, write difficult responses and incident reports, understand a tool or system I'm not familiar with, analyse 30 projects to understand which of them have this particular problem, review tickets in bulk to see if they have anything missing that was mentioned in the solution design, and so on ... All sorts tasks that used to take hours, now take minutes.

This is in line with what I'm hearing from other people: My CFO is complaining daily about running out of tokens. My technical sales relative says it is now taking him minutes to create tech specs from requirements of his customers, while it used to take hours.

While devs are rightfully "meh" because they truly need to review every single line generated by AI and type-writing the code is not their bottleneck anyway. It is harder to realise the gains for them.

[lomase]

Can you show something built with those tools.

The only reply I have got to this question was: it created a sap script.

[muldvarp]

> IMHO the bleeding edge of what’s working well with LLMs is within software engineering because we’re building for ourselves, first.

How are we building _for_ ourselves when we literally automate away our jobs? This is probably one of the _worst_ things someone could do to me.

[DennisP]

Software engineers been automating our own work since we built the first assembler. So far it's just made us more productive and valuable, because the demand for software has been effectively unlimited.

Maybe that will continue with AI, or maybe our long-standing habit will finally turn against us.

[jstanley]

This is kind of a myopic view of what it means to be a programmer.

If you're just in it to collect a salary, then yeah, maybe you do benefit from delivering the minimum possible productivity that won't get you fired.

But if you like making computers do things, and you get joy from making computers do more and new things, then LLMs that can write programs are a fantastic gift.