I've always thought CSP as a robust design pattern where you have no shared state between components and they must communicate with each other using message passing. It also requires synchronous communication (rendezvous-style). If you follow those rules you can have a pretty robust system. Aside from these abstract rules, CSP has more formal research (algebra) but I'm not sure if there are any decent tools available.
TLA gives you a full toolbox and in theory can model whatever you can express. That's very different from a design pattern.
My manager who vibe coded our entire webapp in claude design. Has difficulty understanding why its still not production ready.
My job is to wire to our backend data, and a lot of these wiring require me to be in there and actually think about the features. These take time, and I just haven't figure out a way to speed this process up with Claude.
On the other hand. If his tool does the job why not. If you asked a 90s engineer how javascript triggers millions of cpu instructions for just a few code steps he would call you crazy.
> If you asked a 90s engineer how javascript triggers millions of cpu instructions for just a few code steps he would call you crazy.
I'm not a 90s engineer and that is crazy. The amount of inefficiency that people tolerate from JS and other web stuff is completely unreasonable in my view. It's also why software today is much worse than it was 20-30 years ago.
Why won't smarter and cheaper models in the future be able to automate this part for your manager as well? How novel is the feature set? Is it he has a knowledge gap or the model is incapable of something? What expertise are you bringing to bear that is beyond the scope of a future harness/model? Why wouldn't such a model simply fill in the blanks for your management, perhaps observing a diff of whatever you did? How do you verify the correctness of your thinking? Why could a future model not replicate this process?
I am just very puzzled by these sort of takes as we approach the end of 2026.
I'm not saying Its reassuring, and I think all your points are valid.
The only difference right now between my manager getting this claude design web app to production are the infrastructure and interfacing with it.
To get to prod it has to go through our monorepo pipeline, which currently requires using a cli, using a cli/command prompt requires using terminal, getting claude to use terminal also requires you to even know what a terminal is and spawning claude in there. Steering claude to do all that without knowing what or how to use a terminal is, and setting up your environment still all requires some technical knowledge or the language to tell claude to do that.
That's just getting to production. What about getting the claude design which is in a web environment without any context of all that monorepo with all its backend services. So all the interactivity that are all faked or mocked. Has to be converted to a real react components that's actually wired to the Rest API. How do you get the manager to speak to claude to do all that, with claude only being on the claude design harness?
What if manager designed a new feature that the backend service doesn't support? now your asking to make changes on the backend too.
So I think your points are valid that sure we may get to this at some point. But how exactly without the manager having to learn some technical language of steering claude and the infrastructure to support it.
We can speculate about the capability of AI models in 10 years all we want. Currently; It's not good enough for the task.
Taking a visual proof-of-concept and turning it into a real product with integration to an existing complex system requires the developer to re-do a-lot of the work. And reading code; especially AI code someone-else wrote, is a miserable experience.
mm. I could give a more proper answer. From the very short description of the original message, I don't doubt a long session with fable could get something up-and running even with the back-end integration. But if it needs to be maintained and trust-able in the long-run; or god-forbid sold to a costumer with any amount of accountability clause. Then there may be quite a bit of manual human labor involved.
Trust-able in particular is a growing issue i feel. Very polished looking AI made feature-rich software can have some atrocious bugs in the simplest of parts. Some features may not have been used at all since they were created. Testing never catches everything (even when written); humanity has probably been saved from countless billions of bugs from shower-thoughts of lunch discussions. (AI doesn't take showers). When AI made tests to verify AI made code based on the instructions of a single sleep deprived human using very fuzzy and ambiguous commutation media; can we trust that the software does what we expect it to do at all?
We had this discussion recently at work. "we spend X on our accounting and offer writing system; can we just replace it?". The answer was roughly; "yes, but would you trust it not to accidentally send us in an investigation with the IRS?". If we have to do it properly enough to trust, then AI ends up not being used for much more than user interface. (and that's still a glorified spreadsheet compared to most complex software systems). There is rather fascinating case of a UK cost tracking system convicting 900 sub-postmasters of theft (a few to prison and at-least one to self-inflicted death) over the span of 15 years because a software system was "perfect, tested, and not making any mistakes in its calculation"
Not to say AI is useless or inconsequential; we're wasting a lot of time writing low-consequence boilerplate for UI, code interfaces, API schemas, error handling and data parsing. If they don't work we notice, so they're perfect for AI. But when a "project manager" makes a "prototype app"; it sounds a-lot like a interactive prototype in javascript for the UI; closer to a modern-day figma design than a functional product.
I keep hoping for better answers to this. Your longer answer is frankly just as disappointing.
"AI doesn't take showers" was particularly unconvincing. An army of AIs can ruminate on a codebase indefinitely. They can explore a possibility space at scale in a number of dimensions beyond most humans. How do you think these math problems are being solved? Code is largely verifiable and testable, most of the things that are hard to test is with human interaction. But humans will increasingly be out of the loop.
Most apps are not very useful. Most startups fail. Most companies are copying each other. There's a reason AI is so good.
Trust will be solved with mitigations. There will be inventions in this regard like breakthroughs in formal verification and adversarial LLMs to hold the automations accountable. At a certain point AIs will be trusted more than humans. AI has no free will. Humans go rogue all the time. You can't wipe their brains or reset them or turn them off. How many AIs have shot up their workplace? I just am kind of baffled by people who say AI can't be trusted or controlled as if humans can be?
The question is about outcomes. Costs and benefits. It seems obvious to me within 10 years unless you are within the top 1% of software engineers an AI will make more economic sense than you on a basic cost/benefit breakdown. Maybe a few of the top 10% hang around to certify certain critical paths so if the AI makes a big enough goof you are around to have the rope be hung around your neck.
The end state of capital is automation of everything. The humans actually doing interesting things aren't making "prototype apps" or being "project managers" they are building these AIs or coming up with ways to engineer UBI and a future where knowledge workers cease to exist without the entire economy collapsing.
I'm sorry but it's just not a very good answer. Thanks for trying though.
You have an incredibly optimistic view of current and especially future AI capability, that i dont see warranted.
> breakthroughs in formal verification
this happens to be quite an interest of mine, and can quite confidently say; hah, no. Formal verification of software is such a disproportionately high difficulty compared to writing software; and there is duck all training data. Your lucky if the LLM knows the syntax or standard library.
I'm curious what you work with given your dismissal of software engineering as a displine existing within 10 years.
This would be a great point if his manager asked him to wash the dishes. Who knows, in 10 years time if he finds himself with such a job he might consider himself lucky.
Anyway I hope I get an answer to my actual questions. Engaging with your "it's just a calculator" denialism is an obvious dead-end. Have a good day buddy.
Completely missing my point there. I wasn't saying it's a calculator. I was saying it's not a human intelligence and there's no reason to think it'll stand in for every responsibility we have.
Name me one human that can describe grandmaster level chess strategy but also loses to a random-only chess bot - that was the case for LLMs for a long time
My point is LLMs aren't humans. They're not a toddler slowly getting smarter, and when they're smarter they'll be able to do everything a human can do. It's a different scale entirely.
I didn't ask for every responsibility I asked for his specific responsibility.
Why don't you calm down and read my question again and you will see the only one "completely missing the point" is you.
You're arguing with someone else. I didn't make a claim that LLMs were human. If you're going to respond to me then engage with what I'm exactly saying. Go back and read it again, if you can manage it.
A calculator used to be a human job. Are calculators smarter than humans?
I'm asking why is it reassuring about his job? Surely your big human brain can understand to be automated away doesn't mean the machine is "smart as a human" whatever that means. There is probably not a lot of utility in comparing synthetic and organic intelligence from such a reductive perspective.
Factory workers got automated by machines. Is a robotic arm smarter than a human? Is a tractor smarter than a bovine?
My question is simply what is he doing that is so "smart" or novel that it cannot be automated or mechanized. This should not be so hard to understand.
Why should they answer the question if you can't answer it yourself? What is he doing that is so "smart" or novel that it cannot be automated or mechanized right now? If you knew that answer you could put every one out of a job right now and you would be counting your trillions instead of asking "simple" questions on HN
Factory workers that did repetitive things that didn't require any thinking got automated away by machines. But there are still factory workers today that do things that require thinking.
Machines don't go deep on problems, humans do. A machine has no will or purpose to solve a novel problem on their own. Hope that answers your question.
It doesn't need will? Voyager has gotten farther than likely any human will ever reach. It doesn't have will. How did it happen? Because human will is not the only animus in the universe.
How are these math problems being solved? AI just needs to recursively self prompt. Why does it need a human will? As long as the power keeps flowing and the recursion is resilient, what computational problem discoverable by a human is out of reach?
Ive never read a paper cover to cover before but after wrestling with opus 5s english this paper is such a relief to read, its like my eyes has been washed off opus stink
Ive given up trying to fight claudes language, and Im afraid Im getting used to it and can even understand what claude is saying faster or should I say I can parse it faster now.
But slowing down to decipher text instead of single pass simple reading is the problem.
Random example from a session I have open:
> *One new failure mode the doubling opens, and how it is closed.* A ring never shrinks. With two 8-byte index words per record instead of one, the ring's doubling comes inside the budget's reach in the blank regime: a doubling taken [...]
It takes extra deciphering cycles to see "the doubling", "budget's reach", "the blank regime", etc. and figure out what it's referring to. I had to read that opening sentence multiple times. At first I parsed it like "One cat the table yawns".
Sometimes it's so encumbered I can't tell what it's saying at the directional level: good or bad, fast or slow? "Your blank regime negated the pre-armed run's dilemma but clawed back the overall metrics."
It's not how I'd phrase things if I were trying to be easily understood, though Claudese is probably great for LLMs due to ad hoc jargon usage.
Oh yeah, those examples are gibberish. Mine hasn't been that bad. More like the comment I responded to, where it's just being a bit clunky. Your examples read like Claude's gone a bit schizo.
whats troubling here is that one of the anthropic devs on twitter or github don't seem to acknowledge this issue, If they don't acknowledge there's nothing for them to fix
how is lean different from tlaplus for helping you with reasoning during the design phase?
I literally just discovered tlaplus last week after struggling with reasoning about the explosion of permutations about configuration policies Im designing, and Im still learning the math but Im finding it easier to reason with tlaplus than in code is lean like that?
You can embed TLA+ into Lean. I don't think that there's any benefit to formalization before coding unless you're working on like million dollar projects
