hyperadvanced

The robot will output text like “Oh, I see, the user wants me to make a Lovecraftian horror with asynchronous subprocess calls instead of HTTP endpoints, so I better suggest we reinstall the dependencies that are already installed so we can sacrifice this project to Mammoth”

It is at this point where you can say “NONONO YOU ABSOLUTE DONKEY stop that we just want a FastAPI endpoint!!” And it will go “You’re absolutely right, I was over complicating this!”

In retrospect, it was crazy hearing stories about how SF UX designers would be paid $250 to essentially do what Figma does now.

If you wanted to tell such a story, you’d have to find examples of companies spending bazillions on new AI tooling, but failing to hit their top level OKRs. I suspect there will be at least a few of these by the end of 2026 - even a great technology can seem like an abacus in the hands of a disorganized and slow moving org.

Being optimistic is a bad way to get good outcomes

Perhaps I’m misunderstanding but a lot of people (ok, well, a few, but you know) make a lot of money on relatively mundane stuff. Technocapitalism’s Accursed Share is sacrificing wealth for myth making about its own future.

Thank god it’s not just me. It really makes me feel insane reading some of the commentary online.

Totally. I’m a huge fan of it, but it rarely “just” works and I do have to babysit it to make sure it’s actually doing something good for the world

Perhaps I’m not using the latest and greatest in terms of models. I tend to avoid using tools that require excessive customization like this.

I find it infinitely frustrating to attempt to make these piece of shit “agents” do basic things like running the unit/integrations tests after making changes.

I don’t think it’s “just” that easy. AI can be great at generating unit tests but it can and will also frequently silently hack said tests to make them pass rather than using them as good indicators of what the program is supposed to be doing.

Am I stupid or do these agents regularly not read what’s in the agents.md file?

Fwiw I’m not making a comment on companies which have transitioned from private to public or doing any sort of strategic analysis related to that. I’m merely saying if we index on publicly traded companies which have made substantial AI investments, the story isn’t super clear, i.e. as an investor there is not yet a solid positive thesis for the economics of LLM that would rate the tech as a buy. It doesn’t help that information is sparse here and that AI investments in general is clustered around a few megacap stocks with other interests outside of AI writ large

Presumably one of these high profile people will eventually get pwned if the risks really are that high.

This is my understanding as well. If GPT made money the companies that run them would be publicly traded?

Furthermore, companies which are publicly traded show that overall the products are not economical. Meta and MSFT are great examples of this, though they have recently seen opposite sides of investors appraising their results. Notably, OpenAI and MSFT are more closely linked than any other Mag7 companies with an AI startup.

https://www.forbes.com/sites/phoebeliu/2025/11/10/openai-spe...

Metacognition As A Service, you say?

This is a plain fact. The amount of obviously GPT’d text out there is so breathtaking. The good news is that all of this shit people are squeezing out of GPT is really reinvigorating my desire to work on novel creative projects.

In nearly every form of entertainment over the past 2 years, quality has degraded rapidly, to the point where most Reddit threads really aren’t worth reading - they’re all chock full of It’s Not X It’s Y! Most movies are heavily re-explained plots and reboots of old IP, CGI, dialogue glued on after the fact. Pop music has been on life support for 10 or 20 years, and that was before anyone could AI generate whatever derivative sonic slop they wanted to create. Books and publishers are holding strong to avoid GPT, but the desire for Dragon smut will ultimately overwhelm any aesthete’s preference for originality.

Correct, this is a marketing document, not a government document or a legal agreement.

Oh that’s good. I’m an emacs guy. I don’t like change.

zsh is still crazy to me. I use bash for everything. I have no idea what I’m missing out on.

I’m a hobbyist math guy (with a math degree) and LLMs can at least talk a little talk or entertain random attempts at proofs I make. In general they rebuke my more wild attempts, and will lead me to well-trodden answers for solved problems. I generally enjoy (as a hobby) finding fun or surprising solutions to basic problems more than solving novel maths, so LLMs are fun for me.

If you don’t know k8s, or any tech really, you can RTFM, you can generate or apply some premade manifests, you can feed the errors into the LLM and ask about it, you can google the error message, you can do a lot of things. Often times, in the “real world” of software engineering, you learn by having zero idea of how to do something to start with and gradually come up with ideas from screwing around with a particular tool or prototyping a solution and seeing how well it works.

I agree that some of the above basically amounts to: it’s easier to learn new things. Which itself might sound ho-hum, but it really is a fundamental responsibility of software engineers to learn new things, understand new and complex problems, and learn how to do it correctly and repeatable. LLMs unquestionably help with this, even with their tendency to hallucinate: usually proof by contradiction (or the failure of an over-confident chaos machine) is even better than just having a thing that spits out perfect solutions without needing the operator to understand it.

However, I will say that there is a very large gulf between learning how to reason about complex systems or code and learning how to use the entropy machine to produce nominally acceptable work. Pure reliance and delegation of responsibility to the AI will torpedo a lot of projects that a good engineer could solve, and no amount of lines of code makes up for a poorly conceived product or a brittle implementation that the LLM later stumbles over. Good engineering principles are more important than ever, and the developer has to force the LLM to conform to those.

There are many things to question about agentic coding: whether it’s truly cost/effort effective, whether it saves time, whether it makes you worse at problem solving by handing you facile half-solutions that wither in the face of the chaos of the real world, etc. But they clearly aren’t a technology which “doesn’t do ANYTHING useful”, as some HN posters claim.

I’m strictly talking about “Agentic” coding here:

They are not a silver bullet or truly “you don’t need to know how to code anymore” tools. I’ve done a ton of work with Claude code this year. I’ve gone from a “maybe one ticket a week” tier React developer to someone who’s shipped entire new frontend feature sets, while also managing a team. I’ve used LLM to prototype these features rapidly and tear down the barrier to entry on a lot of simple problems that are historically too big to be a single-dev item, and clear out the backlog of “nice to haves” that compete with the real meat and bread of my business. This prototyping and “good enough” development has been massively impactful in my small org, where the hard problems come from complex interactions between distributed systems, monitoring across services, and lots of low-level machine traffic. LLM’s let me solve easy problems and spend my most productive hours working with people to break down the hard problems into easy problems that I can solve later or pass off to someone on my team to help.

I’ve also used LLM to get into other people’s codebases, refactor ancient tech debt, shore up test suites from years ago that are filled with garbage and copy/paste. On testing alone, LLM are super valuable for throwing edge cases at your code and seeing what you assumed vs. what an entropy machine would throw at it.

LLM absolutely are not a 10x improvement in productivity on their own. They 100% cannot solve some problems in a sensible, tractable way, and they frequently do stupid things that waste time and would ruin a poor developer’s attempts at software engineering. However, they absolutely also lower the barrier to entry and dethrone “pure single tech” (ie backend only, frontend only, “I don’t know Kubernetes”, or other limited scope) software engineers who’ve previously benefited from super specialized knowledge guarding their place in the business.

Software as a discipline has shifted so far from “build functional, safe systems that solve problems” to “I make 200k bike shedding JIRA tickets that require an army of product people to come up with and manage” that LLM can be valuable if only for their capabilities to role-compress and give people with a sense of ownership the tools they need to operate like a whole team would 10 years ago.

You can study the LLM output. In the “before times” I’d just clone a random git repo, use a template, or copy and paste stuff together to get the initial version working.

It’s crazy how fast the tables turned on SWE being barely required to do anything to SWE being required to do everything. I quite like the 2026 culture of SWE but it’s so much more demanding and competitive than it was 5 or 10 years ago

As a hn poster, I agree with this

You could argue (it certainly has been argued) that the ability for technology to dissolve the usually more coherent identities that we take on daily by granting unlimited role play, trolling, and exploration is simply too much for a lot of people, and makes it hard to maintain a coherent sense of self. This is especially true of people who are “internet addicts” - not that the designation means a whole lot as I’m here at the gym talking to you on the phone.

Don’t get me wrong, I mostly agree with your comment. I think even more dastardly is the tendency for the internet to market new personalities to you, based on what’s profitable

The response (usually) is “OK but whatabout the $X billion we spent on the military?”

Which isn’t wrong necessarily, but it doesn’t answer why or whether we should be spending so much money on everything else

Same. I find that if I can piecemeal explain the desired functionality and work as I would pairing with another engineer that it’s totally possible to go from “make me a simple wheel with spokes” to “okay now let’s add a better frame and brakes” with relatively little planning, other than what I’d already do when researching the codebase to implement a new feature

This is a good point, and I’m willing to concede that I may be wrong here, that predictive power can be gained from (possibly mis-) applications of abstractions onto a real space, which is what one of the real reasons to favor abstract thinking in business in the first place.

Source? Source? Got any source about me? Yeah well those statistics only deal with other people who aren’t me, so I guess you’re not really trusting the science :/

This is just plain wrong, I vehemently disagree. What happens if a payment fails on my API, and today that means I need to go through a 20-step process with this pay provider, my database, etc. to correct that. But what’s worse is if this error happens 11,000 times and I run a script to do my 20 step process 11,000 times, but it turns out the error was raised in error. Additionally, because the error was so explicit about how to fix it, I didn’t talk to anyone. And of course, the suggested fix was out of date because docs lag vs. production software. Now I have 11,000 pissed off customers because I was trying to be helpful.