The Missing Layer(yagmin.com)
yagmin.com
The Missing Layer
https://yagmin.com/blog/the-missing-layer/
55 comments
I find these throws of passionate despondency similar to the 1980s personal computing revolution. Oh dear. Giving mere mortals the power of computing?! How many people would abandon their computers or phones.
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.
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.
I am not against vibe coding at all, I just don't think people understand how shaky the foundation is. Software wants to be modified. With enough modifications the disconnect between the code as it is imagined and the code in reality becomes too arduous of a distance to bridge.
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."
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."
This hasn’t been my experience at all working on production code bases with LLMs. What you are describing is how it was more like in gpt 3.5 era.
Not using LLMs, but using them without ever looking at the code.
But this time is different! For reasons!
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.
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.
Jim nailed the core problem. I've been building exactly this "missing layer" for past few months. The challenge isn't just connecting product decisions to code. It's that product context lives in a format that's optimized for human communication, not machine consumption. When engineers feed this to LLMs, they spend massive effort "re-contextualizing" what stakeholders already decided.
I built TypMo (https://typmo.com) around two structured formats that serve as this context layer:
PTL (Product Thinking Language)- Structures product decisions (personas, objectives, constraints, requirements) in a format both humans can read/edit and LLMs can parse precisely. Think YAML for product thinking.
and Interface Structure Language (ISL) - Defines wireframes and component hierarchies in structured syntax that compiles into visual mockups and production-ready prompts. LLMs don't need more context, they need structured context.
The workflow Jim describes (stakeholder meeting → manager aggregates → engineer re-contextualizes for LLM) becomes: stakeholder meeting → PTL compilation → IA generation → production prompts.
LEt's see where it goes!
LEt's see where it goes!
Nice and I'm thinking along similar lines but not DSLs.
My intuition off reading what you wrote is... Nobody is gonna want to write PTLs and ISLs.
My intuition off reading what you wrote is... Nobody is gonna want to write PTLs and ISLs.
Exactly right, and that's the core point. Users don't write PTL or ISLs.
Let's say you have customer interactions (fetched from Zoom) or product/research notes. The AI structures that into PTL automatically. You see clean, editable notes and visual wireframes + high-fidelity prototypes and prompts. The structured formats exist in the background for token efficiency and interoperability.
Ah I see. When I say write.. I also mean review and revise.
... and understand.
Whenever I vibe code something and there is an issue with it, I either have to step in myself and have to learn what was generated or hand it back to the ai to fix it.
The generation of these meta documents will also be fixed by ai, not humans.
The generation of these meta documents will also be fixed by ai, not humans.
We need a language and a transpiler. Honestly the LLM has many uses. Agents have many uses. And we are narrowing down how to make them deterministic and predictable for programming machines and software. But that also means we need something beyond natural language for the actual implementation. Yes we've moved a level up, but engineers are not product managers, so as much as we can define the scope and outline a project like a 2 week sprint using scrum or kanban, the reality is deterministic input for deterministic output is still the way to go. Just as compilers and higher level languages opened the doors to the next phase, the LLM manages this translation and compilation, but it's missing a sort of intermediary language, a format that's going to be much better processed and compiled directly down to machine code. We're talking about LLVM. Why are asking LLMs to write Go code or Python, when we could much better translate an intermediary language to something far more efficient and performant. So I think there's still work to be done.
Am I understanding what you're saying correctly?
* 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?
* 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?
If the compiler only gets you 80% of the way there, but what it does is sufficient to put the LLM on rails, like programming language mad libs, I'd say that's a win.
Yup, that's the idea. Mad libs are still constrained
I feel like I'm still not understanding something. How does making the output from the LLM lower level help?
Concrete example: Next/Turborepo. These tools make your life easier if you drink some kool aid. Rather than have the agent scaffold the app you have the agent use a tool that scaffolds. Agents write specs to manage tools, and those tools scaffold the code, then the agents just sprinkle in business logic that is too bespoke for codegen.
> Let's say your organization wants to add "dark mode" to your site. How does that happen? A site-wide feature usually requires several people to hash out the concerns and explore costs vs. benefits. Does the UI theming support dark mode already? Where will users go to toggle dark mode? What should the default be? If we change the background color we will need to swap the font colors. What about borders and dividers? What about images? What about the company blog, and the FAQ area, which look integrated but run on a different frontend? What about that third-party widget with a static white background?
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.
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.
I am curious to know what he has in mind. This 'process engineering' could be a solution to problems that BPM and COBOL are trying to solve. He might end up with another formalized layer (with rules and constraints for everyone to learn) of indirection that integrates better with LLM interactions (which are also evolving rapidly).
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.
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.
I'm also curious about what a process engineering abstraction layer looks like. Though the final section does hint at it; more integration of more stakeholders closer to the construction of code.
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.
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.
Yes, the database layer and the data itself are also sources of truth. Code (including code run inside the database, such as SQL, triggers, stored procedures and other native modules) defines behaviour. The data influences behaviour. This is why we can only test code with data that is as close to reality as possible, or even production data.
Spec driven development is great in theory, but it has a lot of issues, I rant about them here: https://sibylline.dev/articles/2026-01-28-problems-with-spec...
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'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.
This sounds quite interesting!
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'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
This is my approach. I use the C4 software model, it's pretty general. Entities can be represented either by markdown files, or folders with README.md files (sort of like an index.js or __init__.py). Folder hierarchy gives you a basic project object model.
hierarchical specs? Sounds hard to understand where they overlap. Or, they are independent?
Arbiter is looking nice. I like the composability. Pretty tough for anything except a developer to author it though, which was maybe an accepted tradeoff.
Thanks. My philosophy with it is to be minimal and adaptable to people's existing workflows, I went through a lot of iterations to land on something that was both expressive and "human." The Obsidian compatibility was a sign for me that I was on the right track.
My gut says it's too far downstream
[deleted]
There are many impediments to scaling vibe coding. We built a tool to internally scale vibe coding to vibe engineering: https://mfbt.ai/blog/vibe-coding-vs-vibe-engineering/
> Documentation is hard to maintain because it has no connection to the code. Having an LLM tweak the documentation after every merge is "vibe documenting."
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.
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.
The devil is always in the details.
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.
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've been recently experimenting with using a Prolog-based DSL [0] as the missing layer: Start with a markdown document, "compile" it into the DSL, so that you obtain an "executable spec". Execution still involves LLMs, so it's not entirely deterministic, but it's probably more reliable than hoping your markdown instructions get interpreted in the right way.
[0] https://github.com/deepclause/deepclause-sdk
[0] https://github.com/deepclause/deepclause-sdk
THE CODE IS THAT LAYER.
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
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
This is fundamentally flawed. Code cannot always capture the requirements, nor the reasoning or scenario which led to these requirements.
Nevertheless, it captures what the system does. That makes it the true spec even if it's hard to read or figure out the intent behind. Writing an intent document is basically having a fap if the system doesn't do what you intend.
Well sure but the discussion isn't "is the code is correct".
Right it's whether your aspirations need to be grounded in reality.
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!!
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!!
Just to remind you what Paul Graham said when he defined what the "Hacker" in Hacker News means:
> 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.
https://www.paulgraham.com/hp.html
> 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.
https://www.paulgraham.com/hp.html
Creating massive amounts of semi-structured data is the missing layer? I can see an argument for that if you're a non-programmer who wants to create something. Although, at some point, it's a form of programming.
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.
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.
> 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.
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.
[deleted]
- one of the reasons why i am working on a semi deterministic production grade typescript application generator.
- 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
- 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
Good call. This is a hobby of mine as well. What's your approach?
I'd be interested in hearing how the OP's project compares to UML.
https://en.wikipedia.org/wiki/Unified_Modeling_Language
https://en.wikipedia.org/wiki/Unified_Modeling_Language
Maybe I'm missing something, or we do it differently here, but I think "spec" is defined to narrowly in that article. Start writing the first part of that document in that meeting, and everything ties together neatly.
Upvoted for that animated gif alone. Best visual I've seen of AI coding results.
It looks like a trackmania shortcut.
We can borrow some math from Nyquist and Shannon to understand how much information can be transmitted over a noisy channel and potentially overcome the magic ruler uncertainty from the article:
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:
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.
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 appreciate the detailed response and I certainly haven't studied this, but part of the reason I made the measurement/construction comparison is because information is not equally important, but the errors are more or less equally distributed. And the biggest issue is the lack of ability to know if something is an error in the first place, failure is only defined by the difference between our intent and the result. Code is how we communicate our intent most precisely.
You're absolutely right. Apologies if I came off as critical, which wasn't my intent.
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!
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!
anupamchugh(2)
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.