LLM-is-a-compiler is indeed a simplistic approach. I wrote a rebuttal to the yesterday's Cursor post, may reuse it here https://replicated.live/blog/follow-up
The idea that a 835-page spec "just exists" and we run an LLM to implement it is completely flawed. Specs do not appear out of nowhere, they co-evolve with the code. If you have the code, why do you want to generate it again?
Good software is made as a product of numerous feedback loops and LLMs let you operate those loops faster. They do not supplement the entire process though.
In the end, a good product is a barrel of distilled feedback.
gritzko
The argument being made here is really incredible when you unpack it.
1) The construction of the Empire State Building was particularly effective due to the depth of human-to-human collaboration.
2) Isn't it great that we can burn a bunch of dinosaur blood to convince ourselves that we don't need other humans?
couchand
I've heard something along the lines of "Claude is like a compiler: source code is the new object code, you don't look at that anymore" many times.
And I don't really think this is true. Compilers are usually deterministic, and whilst we can find edge cases, it's nothing like an AI agent writing all the code for you.
I think you have two choices, given the Claude is a code generator and not a compiler: (a) you review most or all of code to make sure it makes sense, or (b) you trust but verify via a strong test suite, potentially also created by Claude.
The problem with (a) is that you lose a lot of the speed-up. The problem with (b) is that you have no human oversight and the code may be incomplete, badly designed, or plain wrong.
Currently we review all code because correctness is extremely important to what we do, but that comes at a cost.
I don't know what the answer is here, in general. Does trust build over time? Do the models just get so good we can trust them to make zero mistakes?
davidpapermill
Specs might become one solution for coping with the need to review increased volume of code. A spec is a higher level of abstraction than code, which is a higher level of abstraction than machine code. The industry made the transition to higher-level once, paradigm is changing so it might happen again.
The workflow I imagine is either deriving specs from the conversation or reverse engineering the code to spec, review and edit the spec which should be tighter and much more compressed, then deterministically compile to code. Of course we don't want to be spec-first only, that would be going back waterfall, but doing iterations back and forth.
Now, Claude is not a compiler because it is closed and non-deterministic (they do opaque processing on server, hiding reasoning tokens), but LLMs might be. We refer to a piece of code from npm/pip by name to get some code by downloading it. We then have lockfiles with hashes to ensure integrity. Currently we are vibing it, but in the future we could refer to a piece of code by prompt/spec and getting the code by inferring it. To ensure integrity, the lockfile would be hashes of open weights and inference code (and ironing out implementation details like non-determinism due to GPU scheduling, etc.).
kloud
> Claude wasn’t just a compiler here. I never handed off a task and let an agent make a bunch of decisions in order to reduce it to practice.
> I’d say that, in all the ways that matter, I understand the code.
I think the dissonance here is really important, and not a bad thing at all. A lot of the decision _were_ handed off the the AI, but they weren't the decisions the author cared about. This is a big selling point of AI! If something is doable with a computer, it’ll figure it out. 30 minutes and 200m tokens later, it’ll take any idea and declare “the feature is fully implemented.”
The hard part is figuring out where to inject that friction, so you can see where it's making decisions for you that matter. The author approached this by incrementally building the thing, reviewing and poking and prodding at every step. A week of attention following a bunch of design discussions is fast, but that's still not trivially cheap.
I want to see us talk more about the decision exhaust of agents, because the better the models get, the more decisions we'll want them to make.
I really like what exe.dev is doing. And their philosophy of how cloud services could work is amazing. I have a monthly subscription and it's really cool to just vibe code a little website with Shelley and have it running in minutes. I feel like I have so much fun using their service, and spend so much less time thinking about what things will cost, or what cloud service to use. It's just so much simpler and the friction is so much less compared to other cloud services.
eigencoder
> I read a vanishingly small amount of the actual code.
This sentiment kind of saddens me. I'm all for burning tokens to write throwaway code just to prototype a solution, but I don't get not reading (or at least familiarizing yourself with) the code that you will deploy to prod.
xav_authentique
> But our VMs start fast, so fast that even if we created the DNS entries before creating the VM, our users still had to sit around waiting for DNS to propagate, which occasionally took minutes, not seconds.
To my (limited) understanding, this is not a good idea, and is an unfixable problem from the server side. Companies, VPNs or ISPs or routers, often use their own DNS servers, and those can have caching logic, which means it doesn't matter how fast your own DNS implementation is, as the users lookup request wont hit your DNS server, it'll hit an intermediate cache.
torginus
A compiler is an algorithm and Claude isn't*. A compiler almost never produces a wrong output, even when compiling an extremely complicated program. But a compiler must be clearly defined and is limited to input/commands it's defined for. A compiler will "correctly" process input into unintuitive output, but that's not always what the user intended (e.g. omitting large sections of code that are undefined behavior). Claude makes mistakes, but can process any input/command expressible in training data, and has more potential to infer when something is unintentional.
Tools like Claude are great at building algorithms, then tests to verify them. The algorithms they build aren't just compilers, but any software that can be verified. Even though Claude may not be a compiler, it has the potential to (at least if it improves) create production-grade software that can be verified, like other compilers. But maybe not good UX without human input (expanded to any subjective experience, like video games).
* Not a "source language to target language" deductive algorithm. Technically Claude is an algorithm to predict the next token, but acting as a compiler or anything else it's an inductive heuristic, because it guesses (https://stackoverflow.com/q/2334225)
armchairhacker
> exe.dev VMs have nice domain names: vm-name.exe.xyz. When we start a new VM, we add a CNAME entry or three. Easy, right?
> But our VMs start fast, so fast that even if we created the DNS entries before creating the VM, our users still had to sit around waiting for DNS to propagate, which occasionally took minutes, not seconds.
> We did the obvious thing: We wrote our own DNS server, so that DNS always immediately matched the source of truth. And life was good.
> But latency matters, so we added regions. And just like that, DNS became the long pole again, because all DNS was served out of Oregon. Also, deployments caused tiny DNS outages. To fix this, all we needed now was a geographically distributed but fully consistent DNS server.
> We did what a sensible engineer does when faced with a hard problem: cheat. We vibe-engineered a distributed DNS server tuned to our specific needs.
I'm sure they have already considered this, but what problem does this solve that couldn't be solved by using dnsmasq, unbound or something like that? Why reinvent the wheel?
comments (10)
The idea that a 835-page spec "just exists" and we run an LLM to implement it is completely flawed. Specs do not appear out of nowhere, they co-evolve with the code. If you have the code, why do you want to generate it again?
Good software is made as a product of numerous feedback loops and LLMs let you operate those loops faster. They do not supplement the entire process though.
In the end, a good product is a barrel of distilled feedback.
gritzko
1) The construction of the Empire State Building was particularly effective due to the depth of human-to-human collaboration.
2) Isn't it great that we can burn a bunch of dinosaur blood to convince ourselves that we don't need other humans?
couchand
And I don't really think this is true. Compilers are usually deterministic, and whilst we can find edge cases, it's nothing like an AI agent writing all the code for you.
I think you have two choices, given the Claude is a code generator and not a compiler: (a) you review most or all of code to make sure it makes sense, or (b) you trust but verify via a strong test suite, potentially also created by Claude.
The problem with (a) is that you lose a lot of the speed-up. The problem with (b) is that you have no human oversight and the code may be incomplete, badly designed, or plain wrong.
Currently we review all code because correctness is extremely important to what we do, but that comes at a cost.
I don't know what the answer is here, in general. Does trust build over time? Do the models just get so good we can trust them to make zero mistakes?
davidpapermill
The workflow I imagine is either deriving specs from the conversation or reverse engineering the code to spec, review and edit the spec which should be tighter and much more compressed, then deterministically compile to code. Of course we don't want to be spec-first only, that would be going back waterfall, but doing iterations back and forth.
Now, Claude is not a compiler because it is closed and non-deterministic (they do opaque processing on server, hiding reasoning tokens), but LLMs might be. We refer to a piece of code from npm/pip by name to get some code by downloading it. We then have lockfiles with hashes to ensure integrity. Currently we are vibing it, but in the future we could refer to a piece of code by prompt/spec and getting the code by inferring it. To ensure integrity, the lockfile would be hashes of open weights and inference code (and ironing out implementation details like non-determinism due to GPU scheduling, etc.).
kloud
> I’d say that, in all the ways that matter, I understand the code.
I think the dissonance here is really important, and not a bad thing at all. A lot of the decision _were_ handed off the the AI, but they weren't the decisions the author cared about. This is a big selling point of AI! If something is doable with a computer, it’ll figure it out. 30 minutes and 200m tokens later, it’ll take any idea and declare “the feature is fully implemented.”
The hard part is figuring out where to inject that friction, so you can see where it's making decisions for you that matter. The author approached this by incrementally building the thing, reviewing and poking and prodding at every step. A week of attention following a bunch of design discussions is fast, but that's still not trivially cheap.
I want to see us talk more about the decision exhaust of agents, because the better the models get, the more decisions we'll want them to make.
I wrote a bit more here: https://tern.sh/blog/compiler-never-says-no/
trjordan
eigencoder
This sentiment kind of saddens me. I'm all for burning tokens to write throwaway code just to prototype a solution, but I don't get not reading (or at least familiarizing yourself with) the code that you will deploy to prod.
xav_authentique
To my (limited) understanding, this is not a good idea, and is an unfixable problem from the server side. Companies, VPNs or ISPs or routers, often use their own DNS servers, and those can have caching logic, which means it doesn't matter how fast your own DNS implementation is, as the users lookup request wont hit your DNS server, it'll hit an intermediate cache.
torginus
Tools like Claude are great at building algorithms, then tests to verify them. The algorithms they build aren't just compilers, but any software that can be verified. Even though Claude may not be a compiler, it has the potential to (at least if it improves) create production-grade software that can be verified, like other compilers. But maybe not good UX without human input (expanded to any subjective experience, like video games).
* Not a "source language to target language" deductive algorithm. Technically Claude is an algorithm to predict the next token, but acting as a compiler or anything else it's an inductive heuristic, because it guesses (https://stackoverflow.com/q/2334225)
armchairhacker
> But our VMs start fast, so fast that even if we created the DNS entries before creating the VM, our users still had to sit around waiting for DNS to propagate, which occasionally took minutes, not seconds.
> We did the obvious thing: We wrote our own DNS server, so that DNS always immediately matched the source of truth. And life was good.
> But latency matters, so we added regions. And just like that, DNS became the long pole again, because all DNS was served out of Oregon. Also, deployments caused tiny DNS outages. To fix this, all we needed now was a geographically distributed but fully consistent DNS server.
> We did what a sensible engineer does when faced with a hard problem: cheat. We vibe-engineered a distributed DNS server tuned to our specific needs.
I'm sure they have already considered this, but what problem does this solve that couldn't be solved by using dnsmasq, unbound or something like that? Why reinvent the wheel?
nunez