Sorry, this is a bit off-topic, but I have to call this out.
The area absolutely does change, you can see this in the trivial example from the first to second step in https://yagmin.com/blog/content/images/2026/02/blocks_cuttin...
The corners are literally cut away.
What doesn't change is the length of the edges, which is a kind of manhattan distance.
The length of the edge has a limit of the straight line, but does not actually approach the limit.
The area however absolutely does approach the limit, as in fact you remove half the "remaining" area each iteration.
It’s not like it changes our industry’s overall flavour.
How many SaaS apps are excel spreadsheets made production grade?
It’s like every engineer forgets that humans have been building a Tower of Babel for 300000 years. And somehow there is always work to do.
People like vibe coding and will do more of it. Then make money fixing the problems the world will still have when you wake up in the morning.
The current solution is to simply reroll the whole project and let the LLM rebuild everything with new knowledge. This is fine until you have real data, users and processes built on top of your project.
Maybe you can get away with doing that for a while, but tech debt needs to be paid down one way or another. Either someone makes sense of the code, or you build so much natural language scaffolding to keep the ship afloat that you end up putting in more human effort than just having someone codify it.
We are definitely headed toward a future where we have lots of these Frankenstein projects in the wild, pulling down millions in ARR but teetering in the breeze. You can definitely do this, but "a codebase always pays its debts."
Yea, the more things change the more they stay the same. This latest AI hype cycle seems to be no different. Which I think will become more widely accepted over the next couple of years as creating deployable, production-ready, maintainable, sellable, profitable software remains difficult for all the reasons besides the hands-to-keyboard writing of code.
LEt's see where it goes!
My intuition off reading what you wrote is... Nobody is gonna want to write PTLs and ISLs.
The generation of these meta documents will also be fixed by ai, not humans.
* We need a deterministic input language
* The LLM generates machine code
Isn't that just a compiler? Why do we need the LLM at that point?
Only one or two of those questions are actually related to programming. (Even though most developers wear multiple hats.) If an organization has the resources to have a six person meeting for adding dark mode, I'd sure hope at least one of them is a designer and knowledgeable on UX. Because most of those questions are ones that they should bring up and have an answer for.
That’s why you should always make your agent write tests first and run them to make sure they fail and aren’t testing that 1+1=2. Then and only then do you let it write the code that makes them pass.
That creates an enormous library of documentation of micro decisions that can be read by the agent later.
Your codebase ends up with executable documentation of how things are meant to work. Agents can do archaeology on it and demystify pretty much anything in the codebase. It makes agents’ amnesia a non-issue.
I find this far more impactful than keeping specs around. It detects regressions and doesn’t drift.
I like the idea that 'code is truth' (as opposed to 'correct'). An AI should be able to use this truth and mutate it according to a specification. If the output of an LLM is incorrect, it is unclear whether the specification is incorrect or if the model itself is incapable (training issue, biases). This is something that 'process engineering' simply cannot solve.
Though I have to push back on the idea of "code as truth". Thinking about all the layers of abstraction and indirection....hasn't data and the database layer typically been the source of truth?
Maybe I'm missing something in this iteration of the industry where code becomes something other than what it's always been: an intermediary between business and data.
I'm working on a tool that uses structured specs as a single source of truth for automated documentation and code generation. Think the good parts of SpecKit + Beads + Obsidian (it's actually vault compatible) + Backstage, in a reasonably sized typescript codebase that leverages existing tools. The interface is almost finalized, I'm working on polishing up the CLI/squashing bugs and getting good docs ready to do a real launch but if anyone's curious they can poke around the github in the meantime.
One neat trick I'm leveraging to keep the nice human ergonomics of folders + markdown while enforcing structure and type safety is to have a CUE intermediate representation, then serialize to a folder of markdown files, with all object attributes besides name and description being thrown in front matter. It's the same pattern used by Obsidian vaults, you can even open the vaults it creates in Obsidian if you want.
This structure lets you lint your specs, and do code generation via template pattern matching to automatically generate code + tests + docs from your project vault, so you have one source of truth that's very human accessible.
I've been exploring spec driven workflows, but from a different angle.
I've been thinking about how to describe systems with a standard format, recursively. Instead of one 10 page doc, you might get 10 one pagers, starting from the highest level of abstraction and recursing down into the parts and subsystems, all following the same format. Building out this Graph of domains provides certain reusable nodes/bits of context.
This then extends to any given bit of software, which is a system in itself composed of the intersections of a lot of different domains and subsystems.
Cue looks interesting and is something I'll be digging into more
I'm not sure I agree, you don't need to vibe document at all.
What I do in general is: - write two separate business requirements and later implementation markdown files - keep refining the first and second one as the work progresses, stakeholders provide feedback
Before merging I have /docs updated based on requirements and implementation files. New business logic gets included in business docs (what and why), new rules/patterns get merged in architectural/code docs.
Works great and better at every new pr and iteration.
This might also extend to runtime checks (e.g. some business invariant in the form of an assert that has such a "dependency-tracked" comment)
If your code does a shit job of capturing the requirements, no amount of markdown will improve your predicament until the code itself is concise enough to be a spec.
Of course you're free to ignore this advice. Lots of the world's code is spaghetti code. You're free to go that direction and reap the reward. Just don't expect to reach any further than mediocrity before your house of cards comes tumbling down, because it turns out "you don't need strong foundations to build tall things anymore" is just abjectly untrue
Do you actually need to build strong foundations, or can you just throw away all your work every time and start over from scratch?
What confounds me is why I am on Hacker News having to defend the idea of software architecture being a kind of capital. That's not supposed to make some sort of countercultural revolutionary. Where did everyone go!!
> What and how should not be kept too separate. You're asking for trouble if you try to decide what to do without understanding how to do it. But hacking can certainly be more than just deciding how to implement some spec. At its best, it's creating the spec-- though it turns out the best way to do that is to implement it.
As a developer, I would rather just write the code and let AI write the semi-structured data that explains it. Creating reams of flow charts and stories just so an AI can build something properly sounds like hell to me.
Well yeah, that's why businesses have all those other employees. :)
I'm still trying to understand what this whole thread and blog post are about. Is HN finally seeing the light that AI doesn't replace people? Sure if you're determined enough you can run a business all by yourself, but this was always true. I guess AI can make information more accessible, but so does a search engine, and before that so did books.
- The lowest layers are the most deterministic and the highest layers are the most vibe coded
- tooling and configuration is at the lowest layers and features are at the highest layer
https://en.wikipedia.org/wiki/Nyquist_rate
https://en.wikipedia.org/wiki/Noisy-channel_coding_theorem
Loosely this means that if we're above the Shannon Limit of -1.6 dB (below a 50% error rate), then data can be retransmitted some number of times to reconstruct it by:
number of retransmissions = log(desired confidence)/log(odds of failure)
confidence = 1 - phi(sigma)
Error rate Number of resends
0.1% 3
1% 4
10% 7
25% 11
49% ~650
Just so we have it, the maximum transmission unit (MTU), which is the 512 or 1500 bytes above, can be calculated by:
MTU in bits = (desired packet loss rate)/(bit error rate)
So (4%)/(10^-5) = 4000 bits = 500 bytes for dialup and (0.0000001)/(10^-11) = 10000 bits = 1250 bytes for ethernet. 512 and 1500 are close enough in practice, although ethernet has jumbo frames now since its error rate has remained low despite bandwidth increases.
So even if AI makes a mistake 10-25% of the time, we only have to re-run it about 10 times (or run 10 individually trained models once) to reach a 5 sigma confidence level.
In other words, it's the lower error rate achieved by LLMs in the last year or two that has provided enough confidence to scale their problem solving ability to any number of steps. That's why it feels like they can solve any problem, whereas before that they would often answer with nonsense or give up. It's a little like how the high signal to noise ratio of transistors made computers possible.
Since GPU computing power vs price still doubles every 2 years, we only have to wait about 7 years for AI to basically get the answer right every time, given the context available to it.
For these reasons, I disagree with the premise of the article that AI may never provide enough certainty to provide engineering safety, but I appreciate and have experienced the sentiment. This is why I estimate that the Singularity may arrive within 7 years, but certainly within 14 to 21 years at that rate of confidence level increase.
I was trying to make a connection with random sampling as a way to maybe reduce the inherent uncertainty in how well AI solves problems, but there's still a chance that 10 AIs could come up with the wrong answer and we'd have no way of knowing. Like how wisdom of the crowd can still lead to design by committee mistakes. Plus I'm guessing that AIs already work through several layers of voting internally to reach consensus. So maybe my comment was more of a breadcrumb than an answer.
Some other related topics might be error correcting codes (like ECC ram), Reed-Solomon error correction, the Condorcet paradox (voting may not be able to reach consensus) and even the halting problem (zero error might not be reachable in limited time).
However, I do feel that AI has reached an MVP status that it never had before. Your post reminded me of something I wrote about in 2011, where I said that we might not need a magic bullet to fix programming, just a sufficiently advanced one:
https://web.archive.org/web/20151023135956/http://zackarymor...
I took my blog(s) down years ago because I was embarrassed by what I wrote (it was during the Occupy Wall Street days but the rich guys won). It always felt so.. sophomoric, no matter how hard I tried to convey my thoughts. But it's interesting how so little has changed in the time since, yet some important things have.
Like, I hadn't used Docker in 2011 (it didn't come out until 2013) so all I could imagine was Erlang orchestrating a bunch of AIs. I thought that maybe a virtual ant colony could be used for hill climbing, similarly to how genetic algorithms evolve better solutions, which today might be better represented by temperature in LLMs. We never got true multicore computing (which still devastates me), but we did get Apple's M line of ARM processors and video cards that reached ludicrous speed.
What I'm trying to say is, I know that it seems like AI is all over the place right now, and it's hard to know if it's correct or hallucinating. Even when starting with the same random seed, it seems like getting two AIs to reach the same conclusion is still an open problem, just like with reproducible builds.
So I just want to say that I view LLMs as a small piece of a much larger puzzle. We can imagine a minimal LLM with less than 1 billion parameters (more likely 1 million) that controls a neuron in a virtual brain. Then it's not so hard to imagine millions or billions of those working together to solve any problem, just like we do. I see AIs like ChatGPT more like logic gates than processors. And they're already good enough to be considered fully reliable, if not better at humans than most tasks already, so it's easy to imagine a society of them with metacognition that couldn't get the wrong answer if it tried. Kind of like when someone's wrong on the internet and everyone lets them know it!