4 ms·

What's the largest software project AI can complete on its own?

106 points by yusufozkan 2mo ago

looksjjhg 2mo ago
Why just the commercial ones !??
jdw64 2mo ago
I'm curious about that too. I'm building a language with AI, and when I leave the AI running on its own, it keeps generating code in strange directions. I wonder what kind of structure AI-only code would have.
- ivan888 2mo ago
  Is it like self driving cars, where 100% non-human code would be arguably safer? But then it always comes down to having some human who is accountable for verifying the behavior. I think ultimately we just need a better set of languages and frameworks to minimally encode the types of applications we want to build today, so that human comprehension and verification of the source code becomes easier.
  - LetsGetTechnicl 2mo ago
    How would 100% non-human code possibly be safer?
    - micromacrofoot 2mo ago
      well if driving is supposedly safer when automated, why not code
      - ModernMech 2mo ago
        It’s supposedly safer than the average driver, not safer than professional drivers.
        micromacrofoot 2mo ago
        the average american spends 60 minutes a day driving, that's thousands of hours by the time you reach 25... at what point is that not a professional level of experience?
      - dd8601fn 2mo ago
        The qualities of computation that make autopilots safer than human drivers aren’t the same qualities that produce better designed software products.
        micromacrofoot 2mo ago
        to err is human
      - LetsGetTechnicl 2mo ago
        Is it actually safer? If you can't understand the code you can't verify it's safety, and AI code often has bugs in it. You can't trust implicitly.
        skeledrew 2mo ago
        You don't need to understand code to verify its safety if you have a good QA framework.
        micromacrofoot 2mo ago
        Same applies to driving cars
  - jdw64 2mo ago
    I've noticed that when only AI writes the code, there are cases where it ends up being unusable by humans. For small code snippets or function-level units, AI is often much better than me. But when I look at the whole picture, AI tends to perform poorly.
  - roncesvalles 2mo ago
    Category error. Self-driving cars are still meticulously coded by humans.
    - fragmede 2mo ago
      They use trained models in the system though, so I don't know if you can really claim that.
- onlyrealcuzzo 2mo ago
  Also building a language with AI. It's decent at design and implementation, but strangely terrible at language consistency - and expectedly architecture. Steering it on language consistency is easy. Steering it on architecture has required me to learn a lot more about compilers than I wanted to do for this project (since that's a lot more than I wanted to take on for something that was supposed to be a "fun" "side" project).
  - jdw64 2mo ago
    I have a similar problem. I'm not sure about architecture. When I first implemented the slot system in my language, I designed it around that implementation, and in the end, the architecture suffered. There were quite a few cases where consistency was simply unavoidable. It's not easy to create a completely new architecture with something that doesn't exist in existing languages. Consistency doesn't come together as easily as you'd think. In that sense, I really admire the people who first introduced something new. When I started implementing it, I focused on one killer feature and just went for it—with the feeling that if this succeeds, my language will succeed. But once I started building around it, it turned out to be very different from other compiler architectures. With CRUD or other well-established domains, it's easier to maintain consistency. But achieving internal consistency in a program seems to be a different kind of challenge.
- lonelyasacloud 2mo ago
  > I'm building a language with AI, and when I leave the AI running on its own, it keeps generating code in strange directions. In the linked paper the researchers state "AI solutions must match the original program’s output exactly on end-to-end tests, including held-out tests. " And a bit further on "The AI agent is given execute-only access to the original program and a set of visible test cases, but does not have access to the original source code. " At a guess, their agents are not going off into the weeds (like mine do occasionally) is because they have both access to a reference runtime implementation to use as a definitive end to end test to guide development. And also a relatively large token budget to run enough characterisation tests to allow them to home in on solutions.
thisisauserid 2mo ago
I believe they can make ANY software project the largest it can be! This solution should look for more problems.
- marginalia_nu 2mo ago
  Yeah came here to say the same thing. The Hard Problem of software engineering isn't making a complex monstrosity with millions of lines of code and endless feature creep, but not doing that. This was as true 20 years as it is today.
  - AlotOfReading 2mo ago
    I apologize for such a big program — I didn't have enough tokens to write a small one. - my coworkers
- linsomniac 2mo ago
  You say that, but I asked Codex to build a Asteroids (but FPS) as a web app, and it wrote it in under 4K LOC, and a Tempest implementation in 6.5K LOC. Those seem like really reasonable line counts to me. I also built a caching proxy server for apt packages, that's a bigger project, and that is 30.5K lines of golang, which doesn't seem bad compared to apt-cacher-ng which is 19.3K of C++ (my project does significantly more).
  - thisisauserid 2mo ago
    [flagged]
    - linsomniac 2mo ago
      I'm going to take it that you were being sarcastic. However, in the case of the apt cacher, the DDoS of Ubuntu infrastructure showed that the existing apt cachers left a *LOT* of room for improvement, not to mention that apt-cacher-ng has regularly (over the last 13 years of use) gotten wedged and needed intervention. This cacher understands Deb package repo structure, and when it finds new meta-data it grabs and checks the meta-data, downloads new version of any package you regularly download (configurable), and then makes a snapshot of that, which it then promotes for cache users to see. I can shut down my Internet connection and then install a fresh Ubuntu from ISO, then "apt update; apt dist-upgrade" successfully via the cache. If you are saying that AI isn't solving novel problems, you are waaaaaay off. But you're also missing the point: This branch of the conversation is about AI tools writing wildly inflated code. Which I proposed a few data-points refuting.
jph 2mo ago
I'm experimenting with large-ish software projects using Claude Fable. The results are surprising to me. - Project portfolio management software. Good results so far. This is unguided experimentation: Fable researches feature ideas and plans them, then Opus 5 implements them, without me in the loop. The features include things such as an auth server, full-text search, audit logging, resource leveling, HA/DR, IaC, and more. - FHIR & SNOMED medical software experimentation in demo-only environments. This has mixed results so far. This is guided experimentation: the FHIR specifications and SNOMED specifications are thorough and I'm in the loop. Fable plans OK, but Opus 5 is thrashing a lot, going down many rabbit holes.
swframe2 2mo ago
Running a Loop on the well defined goal seems promising: https://www.youtube.com/shorts/EtxdB7gpFqY https://www.youtube.com/shorts/EtxdB7gpFqY
- JauntyHatAngle 2mo ago
  I'm a bit suspicious of this as there are open source excel clones already. It's impressive AI can put together it's own version, but I can't help but feel it's probably just relying on existing excel clones as it's a well trodden space. Novel applications are what impress me. Not a clone.
  - swframe2 2mo ago
    Agreed. Novel would be very impressive. AI tech is struggling with that. It is important not to wait for today's AI tech to get to AGI. It is better to exploit the useful things it can do today while waiting for it to get better tomorrow. Cloning existing software might be useful today. It allows the model to 1) learn from its mistakes 2) mix known solutions (embed product A inside product B) 3) mine value (turn product A into a set of reusable libraries) etc.
Jdstanhope 2mo ago
I have been using Claude to build a clone of Bash in Rust (https://github.com/jdstanhope/huck https://github.com/jdstanhope/huck). It has been goin going on now for 81 days, 2600 commits and I have covered a good chunk of the features such that I can source by .bashrc with a number of extensions. It has been doing pretty good but it will sometimes spend hours to do few line fix and other times pushback on making big changes despite have over 3000 tests. That being said, I am still guiding it on a regular basis and only recently let it run open ended trying fixing small to medium size bugs. The next stage will be try let it run in a loop trying to fix the 100 issues it has identified as well as getting most of the bash test suite to pass.
- rco8786 2mo ago
  > it will sometimes spend hours to do few line fix and other times pushback on making big changes despite have over 3000 tests. you ever worked with human engineers before? :D
  - Jdstanhope 2mo ago
    I was going to say that it's pretty much exactly like working with an experienced software engineer ... except it never seems to take a step back and look at the bigger picture. It will happily fix the same class of bugs over and over without realizing a bigger change is required.
    - deleted 2mo ago
      [deleted]
    - francoisdevlin 2mo ago
      Wait, are you talking about llms or people?
      - boothby 2mo ago
        It's sad that LLMs are learning to devalue human life from people like you.
gipp 2mo ago
"Reproduce an existing piece of software" doesn't seem like a problem that generalizes well to "develop a new piece of software," for a fair number of reasons.
- dougSF70 2mo ago
  I agree...if there is a manual that documents well specified software including edge/corner cases i would kinda consider it a waste of tokens unless the aim is to build a better version of the software
stared 2mo ago
I am curious how Claude Opus 5 fares - similar, better, or (my guess) worse than Fable 5.
- logicchains 2mo ago
  For really hard problems (maths) it's far far worse than Fable 5, not even as good as Sol 4.6. Current benchmarks are inadequate as they fail to capture how much worse Opus 5 is than Fable 5.
  - zsoltkacsandi 2mo ago
    I agree, Opus 5 is terrible. Worse than Opus 4.8.
- linsomniac 2mo ago
  I thought Fable 5 was pretty good, but with all the shenanigans (specifically the we're pulling it on X, j/k, you get it another week; too bad you burned all your Fable tokens)... I decided to try Codex+gpt-5.6-sol-xhigh, and it's really good. It reminds me a lot of Fable 5, but it also is a LOT harder to use all your tokens with. Plus they give you a free reset a week right now. I'd even go so far as to say I think it's better than Fable 5. With an asterisk... That asterisk: For anything very big, I'm having it do review loops using Claude Code + Opus 5. For example, I gave it a few paragraphs describing a bi-directional rsync without the Unison database. It worked solo for 4-5 hours and in my testing it works great. I gave it a few small feedbacks (give me progress in human units not bytes, there's a bug syncing a specific file rather than directory, etc).
muglug 2mo ago
> MirrorCode only covers programs with plain-text output. MirrorCode does not test AI abilities in software that is inherently visual It’s an interesting eval but this is a distinct limitation — most applications that consumers use directly are not purely text-based.
mkagenius 2mo ago
Couldn't find what exact tests they are running. The GitHub repo is very obscure to be read by my human brain.
franze 2mo ago
my biggest so far was https://apps.apple.com/at/app/aifcc-ai-first-computer/id6782364574?l=en-GB&mt=12 https://apps.apple.com/at/app/aifcc-ai-first-computer/id6782... a whole linux distro in a mac app. claude worked for about 2 weeks.
sreekanth850 2mo ago
We (Team of 3) built a retrieval engine from scratch in .NET for investigative search, with AI doing most of the backend implementation while humans handled the design and architecture and front end. It was developed using a task based, incremental approach. Includes more than 3,000 tests and extensive documentation, and is optimized for asynchronous ingestion using NATS and a disk backed WAL. It includes a custom WAL, structure aware semantic chunking, ingestion and indexing pipelines, hybrid (rrf), text and vector retrieval, ACL aware filtering at query level, query classifier, citation tracking, and a CrateDB backend that performs fused retrieval over a single table. total LOC: 180 K. Status: evaluation by State agencies for investigative search on legacy documents. tool used - Codex, Luna, and earlier codex 5.3, and gpt 4 mini.
- AlotOfReading 2mo ago
  How long did it take you to review things for correctness? Like an incremental query engine is a few thousand lines of reasonably difficult code on its own, a reliable WAL is another few thousand, etc, and each of those might take me days or weeks of thinking to have any serious confidence in them. I guess you can get provenance and ACL tracking "for free" from a query engine, but it still seems like a very ambitious project.
  - sreekanth850 2mo ago
    It took about four months. Correctness was reviewed continuously rather than only after implementation. We built the system incrementally in small slices, with humans handling the architecture, and review while AI performed much of the implementation and front end is fully doe by humans. The ingestion side includes durable asynchronous processing, recovery and replay, idempotency, strict ordering, structure aware segmentation, parent child relationships, and batched local embedding. The retrieval side includes hybrid (rrf), text and vector search, document versioning, collection and category scoping, document level ACL enforcement, parent expansion, provenance, and citation tracking. Your estimate is fair. But, reliable WAL and retrieval layer are each substantial projects. We did not build the underlying database query engine from scratch, but the correctness work around ingestion, recovery, hierarchy, security, ranking, and provenance was still significant. The project now has more than 3,000 automated tests, including coverage for recovery, concurrency, ordering, ACLs, versioning, and retrieval behaviour. Tests are not proof of correctness, but incremental implementation, explicit invariants, failure testing, and continuous human review are how we built confidence in it. We also had script to automate live endpoint tests with real data samples, this help largely to mitigate issues after a refactoring or enhancement. total production code is 80k LOC and rest is test coverages. One thing to say after seeing the results are, many of the RAG framework does it wrong. We got exceptional results and we also have agentic retrieval for answering complex queries. We designed it in our own way because government systems cannot afford false results, especially when the product is intended for crime investigation or compliance management in banks. Edit: I forgot to mention that we also support PostgreSQL. After building this system, we concluded that vector dimensionality is only a small part of retrieval accuracy. We use 384 dim embeddings and still achieve excellent results.
colechristensen 2mo ago
I'm trying to build a competitor to Parasolid and... it's a struggle getting towards the end of the first 90% of the work. It's getting back to the point where I'm buying textbooks and reading papers because moderate domain knowledge is not enough to drive LLMs to completion here. The space is too big and the code too extensive for a 1M context window and the capabilities of the current SOTA models.
ErenayDev 2mo ago
Seems like most of developers and humans vibecoding new projects with LLMs. But I can see they have problems in the codebase with AI generated code, or security breachs etc. Whats the limit of it? In other words, I'm curious about what the future holds.
- Art9681 2mo ago
  Given all of the CVE's being published for software that's been around decades and being tended to by the most talented businesses and individuals in the world, the least of my concerns are vibe coded projects that will never be used. All software has problems. Most engineers spend their careers fixing issues caused by previous engineers. This was the case before LLMs, and it will continue to be the case for the foreseeable future.
NewEntryHN 2mo ago
> without access to the original source code All models in the leaderboard probably have had access to the original source code in their training data.
- alienbaby 2mo ago
  Which is discussed in the article, tbf
manoDev 2mo ago
This actually proves the hardest part of software engineering is defining requirements.
- wannabe44 2mo ago
  That makes it sound like a PM problem. The hardest part is building the mental model which doesn't crumble. I have worked with humans who had the same failure modes as LLMs.
_doctor_love 2mo ago
What size project an AI can complete on its own is IMHO not an interesting question. The AIs and harnesses are getting good enough that quite large things can be built, primarily as a function of cost. The interesting question in my view is what is the largest system an AI can maintain over a long time horizon -- while keeping a coherent architecture that adapts and responds well to change. All the while remaining performant, scalable, and understandable to humans. I am unaware of what is the currently longest-lived vibe coded project but it cannot be more than a few years old. So to my mind, we don't have very much real data yet.
- htrp 2mo ago
  isn't this a harness engineering problem at its core (and the human equivalent being a product org problem)?
  - _doctor_love 2mo ago
    I don't believe it is. If we think of AI-maintained software as automated gardening / automated agriculture, the harness is all the machinery that can plow, sow, water, monitor, harvest, etc. There still has to be a human stewarding the overall ecosystem and tending the garden. The human is 'on top' of loop rather than 'in it' but I don't think a harness by itself can be a good steward of architecture. Product org problems also is very context-dependent. Product orgs can work significantly different, in some orgs humans desire to be deep in the details, in other orgs the product folks are happy to let engineering drive the boat beyond a certain level of complexity. So one size harness can't necessarily fit all.
- arstructinc 2mo ago
  I think reproducing existing software and building a new product are quite different tasks. In a benchmark, there are usually clear tests and a correct reference. In actual product development, requirements are often unclear, and we do not always know what the correct result is. Passing tests is also not enough to confirm security, maintainability, or operability. I would like to see a benchmark where an AI continues changing the same product for several months. It would be interesting to see whether the architecture remains understandable and safe after many changes.
guybedo 2mo ago
i've been working on several rather large projects these past few months, and i'm trying to write as little code as possible. I don't think i wrote more than 10 lines of code in the largest project i'm working on. Lines of code: Java: 900_635, typescript: 725_418, C++: 180_445, Dart: 96_181. It's been obvious from the start that no model, as good as it is, can do large(-ish) amounts of work by its own without supervision, control, criticism, etc ... If left unsupervised, models usually do half the work, leaving stubs and todos everywhere. Quality comes from applying software engineering principles as much as possible, just like you would do with teams of junior devs: planning sessions and implementation sessions with adversarial critiques, specifying as much as possible upfront, planning unit/smoke/integration tests, etc ... Many systems rely on swarm of agents to build software but i've found it very difficult to get good results without lots of overhead/token waste because of inter agent communications mostly. So instead i built what is mostly a workflow engine to structure / organize processes into workflows with different agents assigned different roles. I've setup a basic landing page here https://kodfactory.com https://kodfactory.com if anyone wants to follow along.
eithed 2mo ago
I'm looking at these reports and then at my sessions with Claude. In latest one, it got stuck in a loop fixing one thing and breaking another for 10 iterations, until I've noticed and gave it the insight. Or where it started continuously writing "echo", I don't know for what purpose. And I ask how, how can you not be the driver; how can you let these agents roam free and actually accomplish things.
- dnnehgf 2mo ago
  we bomb schools and walk past beggars and devote our surplus time and money to scones and brain rot and gym memberships. our c code probably looks at us and says: how, how can you let these agents roam free. llms are not code and are incompetent and inefficient relative to code, but on the other hand you cannot roam without a little echo echo echo in your blood.
  - IncandescentGas 2mo ago
    Thanks, your comment got this song stuck in my head. (I like my gym membership, thank you very much. I even visited the gym once several months ago.) We wasted every second dime On diets, lawyers, shrinks, and apps, and flags And plastic surgery - So long & thanks for all the fish, a perfect circle
andai 2mo ago
Would be nice if they also tested other models instead of several old versions of GPT.
cobbzilla 2mo ago
whatever it is, it will be heavily skewed in favor of whatever there is a lot of examples of. AI could bolt together many cookie-cutter pieces into a very large YOLO system. The trouble comes when you now want that system to do something unique for your business, and those ripple effects through the entire system you didn’t design become very painful.
tulio_ribeiro 2mo ago
This one is also very interesting: https://programbench.com/ https://programbench.com/
vcryan 2mo ago
It really depends on how you define "complete". It's possible the answer in "not large at all" if we're looking for high-quality, reliable, user-friendly software that works near perfectly after being written, tested, deployed completely by AI with no human intervention. People who try to get AI to do a lot of un-manned work seem to have a bit of a bias toward viewing LLM work as "good" or "good enough" in a way that they wouldn't have accepted in the pre-AI era.
mikewarot 2mo ago
It's smaller than an actualization of the Memex, that's for sure. It's been 80 years since it was seen as a good solution to the greatest problem facing science at the time. 80 years, yet we still can't mark up hypertext without copying and then modifications to the source.
vivzkestrel 2mo ago
- I ll consider any of these models as god tier the day one of these 2 happen - They build a browser engine from scratch that implements every whatwg spec for the last 15 yrs, fixes every bug chromium has and runs 10x faster than chromium - they build a super highly optimized version of windows that runs much much faster than windows 11
- inigyou 2mo ago
  People who are 100x more productive than before should seriously consider doing one of these. Should only take a few months at 100x productivity.
Terratrader 2mo ago
So I can not code. I just tell an AI the concept of what I want my software to do and execute in blocks. Most operations are well defined and depend on a central mechanism and that is where everything starts. The rest is like an octopus : the body defines the concept, the arms the consequences. This way there is no need for long code since every segment has a clear beginning and end. It is also quite efficient overall. My point is that long code can be circumvented by clever architecture.
SwtCyber 2mo ago
I think the best part about this benchmark is that they finally stopped pretending you can evaluate a complex engineering task with $ 5 worth of inference. If a task takes a human weeks, you need to give the agent a comparable search space to find solutions
cyanregiment 2mo ago
Accurately? About a ~500 line React component or API endpoint given lots of very good context Which is not bad I don’t bother with the agentic stuff anymore than I would run a calculator on auto - what is the actual point if you think of what that really means
harrouet 2mo ago
It is very hard to make sense of these benchmarks given that at least some of these models have probably been trained on the code base of these projects.
dividedcomet 2mo ago
I’m making an in-terminal IDE over at https://github.com/paradise-runner/toast https://github.com/paradise-runner/toast and deep seek flash has been doing a wonderful job since the last weights release. Makes me think we’re past expensive AI to do general engineering work.

106 points by yusufozkan 2mo ago

Why just the commercial ones !??

jdw64 2mo ago

I'm curious about that too. I'm building a language with AI, and when I leave the AI running on its own, it keeps generating code in strange directions. I wonder what kind of structure AI-only code would have.

ivan888 2mo ago
Is it like self driving cars, where 100% non-human code would be arguably safer? But then it always comes down to having some human who is accountable for verifying the behavior. I think ultimately we just need a better set of languages and frameworks to minimally encode the types of applications we want to build today, so that human comprehension and verification of the source code becomes easier.
- LetsGetTechnicl 2mo ago
  How would 100% non-human code possibly be safer?
  - micromacrofoot 2mo ago
    well if driving is supposedly safer when automated, why not code
    - ModernMech 2mo ago
      It’s supposedly safer than the average driver, not safer than professional drivers.
      - micromacrofoot 2mo ago
        the average american spends 60 minutes a day driving, that's thousands of hours by the time you reach 25... at what point is that not a professional level of experience?
    - dd8601fn 2mo ago
      The qualities of computation that make autopilots safer than human drivers aren’t the same qualities that produce better designed software products.
      - micromacrofoot 2mo ago
        to err is human
    - LetsGetTechnicl 2mo ago
      Is it actually safer? If you can't understand the code you can't verify it's safety, and AI code often has bugs in it. You can't trust implicitly.
      - skeledrew 2mo ago
        You don't need to understand code to verify its safety if you have a good QA framework.
      - micromacrofoot 2mo ago
        Same applies to driving cars
- jdw64 2mo ago
  I've noticed that when only AI writes the code, there are cases where it ends up being unusable by humans. For small code snippets or function-level units, AI is often much better than me. But when I look at the whole picture, AI tends to perform poorly.
- roncesvalles 2mo ago
  Category error. Self-driving cars are still meticulously coded by humans.
  - fragmede 2mo ago
    They use trained models in the system though, so I don't know if you can really claim that.
onlyrealcuzzo 2mo ago
Also building a language with AI. It's decent at design and implementation, but strangely terrible at language consistency - and expectedly architecture. Steering it on language consistency is easy. Steering it on architecture has required me to learn a lot more about compilers than I wanted to do for this project (since that's a lot more than I wanted to take on for something that was supposed to be a "fun" "side" project).
- jdw64 2mo ago
  I have a similar problem. I'm not sure about architecture. When I first implemented the slot system in my language, I designed it around that implementation, and in the end, the architecture suffered. There were quite a few cases where consistency was simply unavoidable. It's not easy to create a completely new architecture with something that doesn't exist in existing languages. Consistency doesn't come together as easily as you'd think. In that sense, I really admire the people who first introduced something new. When I started implementing it, I focused on one killer feature and just went for it—with the feeling that if this succeeds, my language will succeed. But once I started building around it, it turned out to be very different from other compiler architectures. With CRUD or other well-established domains, it's easier to maintain consistency. But achieving internal consistency in a program seems to be a different kind of challenge.
lonelyasacloud 2mo ago
> I'm building a language with AI, and when I leave the AI running on its own, it keeps generating code in strange directions. In the linked paper the researchers state "AI solutions must match the original program’s output exactly on end-to-end tests, including held-out tests. " And a bit further on "The AI agent is given execute-only access to the original program and a set of visible test cases, but does not have access to the original source code. " At a guess, their agents are not going off into the weeds (like mine do occasionally) is because they have both access to a reference runtime implementation to use as a definitive end to end test to guide development. And also a relatively large token budget to run enough characterisation tests to allow them to home in on solutions.

thisisauserid 2mo ago

I believe they can make ANY software project the largest it can be! This solution should look for more problems.

marginalia_nu 2mo ago
Yeah came here to say the same thing. The Hard Problem of software engineering isn't making a complex monstrosity with millions of lines of code and endless feature creep, but not doing that. This was as true 20 years as it is today.
- AlotOfReading 2mo ago
  I apologize for such a big program — I didn't have enough tokens to write a small one. - my coworkers
linsomniac 2mo ago
You say that, but I asked Codex to build a Asteroids (but FPS) as a web app, and it wrote it in under 4K LOC, and a Tempest implementation in 6.5K LOC. Those seem like really reasonable line counts to me. I also built a caching proxy server for apt packages, that's a bigger project, and that is 30.5K lines of golang, which doesn't seem bad compared to apt-cacher-ng which is 19.3K of C++ (my project does significantly more).
- thisisauserid 2mo ago
  [flagged]
  - linsomniac 2mo ago
    I'm going to take it that you were being sarcastic. However, in the case of the apt cacher, the DDoS of Ubuntu infrastructure showed that the existing apt cachers left a *LOT* of room for improvement, not to mention that apt-cacher-ng has regularly (over the last 13 years of use) gotten wedged and needed intervention. This cacher understands Deb package repo structure, and when it finds new meta-data it grabs and checks the meta-data, downloads new version of any package you regularly download (configurable), and then makes a snapshot of that, which it then promotes for cache users to see. I can shut down my Internet connection and then install a fresh Ubuntu from ISO, then "apt update; apt dist-upgrade" successfully via the cache. If you are saying that AI isn't solving novel problems, you are waaaaaay off. But you're also missing the point: This branch of the conversation is about AI tools writing wildly inflated code. Which I proposed a few data-points refuting.

jph 2mo ago

I'm experimenting with large-ish software projects using Claude Fable. The results are surprising to me. - Project portfolio management software. Good results so far. This is unguided experimentation: Fable researches feature ideas and plans them, then Opus 5 implements them, without me in the loop. The features include things such as an auth server, full-text search, audit logging, resource leveling, HA/DR, IaC, and more. - FHIR & SNOMED medical software experimentation in demo-only environments. This has mixed results so far. This is guided experimentation: the FHIR specifications and SNOMED specifications are thorough and I'm in the loop. Fable plans OK, but Opus 5 is thrashing a lot, going down many rabbit holes.

swframe2 2mo ago

Running a Loop on the well defined goal seems promising: https://www.youtube.com/shorts/EtxdB7gpFqY https://www.youtube.com/shorts/EtxdB7gpFqY

JauntyHatAngle 2mo ago
I'm a bit suspicious of this as there are open source excel clones already. It's impressive AI can put together it's own version, but I can't help but feel it's probably just relying on existing excel clones as it's a well trodden space. Novel applications are what impress me. Not a clone.
- swframe2 2mo ago
  Agreed. Novel would be very impressive. AI tech is struggling with that. It is important not to wait for today's AI tech to get to AGI. It is better to exploit the useful things it can do today while waiting for it to get better tomorrow. Cloning existing software might be useful today. It allows the model to 1) learn from its mistakes 2) mix known solutions (embed product A inside product B) 3) mine value (turn product A into a set of reusable libraries) etc.

Jdstanhope 2mo ago

I have been using Claude to build a clone of Bash in Rust (https://github.com/jdstanhope/huck https://github.com/jdstanhope/huck). It has been goin going on now for 81 days, 2600 commits and I have covered a good chunk of the features such that I can source by .bashrc with a number of extensions. It has been doing pretty good but it will sometimes spend hours to do few line fix and other times pushback on making big changes despite have over 3000 tests. That being said, I am still guiding it on a regular basis and only recently let it run open ended trying fixing small to medium size bugs. The next stage will be try let it run in a loop trying to fix the 100 issues it has identified as well as getting most of the bash test suite to pass.

rco8786 2mo ago
> it will sometimes spend hours to do few line fix and other times pushback on making big changes despite have over 3000 tests. you ever worked with human engineers before? :D
- Jdstanhope 2mo ago
  I was going to say that it's pretty much exactly like working with an experienced software engineer ... except it never seems to take a step back and look at the bigger picture. It will happily fix the same class of bugs over and over without realizing a bigger change is required.
  - deleted 2mo ago
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  - francoisdevlin 2mo ago
    Wait, are you talking about llms or people?
    - boothby 2mo ago
      It's sad that LLMs are learning to devalue human life from people like you.

gipp 2mo ago

"Reproduce an existing piece of software" doesn't seem like a problem that generalizes well to "develop a new piece of software," for a fair number of reasons.

dougSF70 2mo ago
I agree...if there is a manual that documents well specified software including edge/corner cases i would kinda consider it a waste of tokens unless the aim is to build a better version of the software

stared 2mo ago

I am curious how Claude Opus 5 fares - similar, better, or (my guess) worse than Fable 5.

logicchains 2mo ago
For really hard problems (maths) it's far far worse than Fable 5, not even as good as Sol 4.6. Current benchmarks are inadequate as they fail to capture how much worse Opus 5 is than Fable 5.
- zsoltkacsandi 2mo ago
  I agree, Opus 5 is terrible. Worse than Opus 4.8.
linsomniac 2mo ago
I thought Fable 5 was pretty good, but with all the shenanigans (specifically the we're pulling it on X, j/k, you get it another week; too bad you burned all your Fable tokens)... I decided to try Codex+gpt-5.6-sol-xhigh, and it's really good. It reminds me a lot of Fable 5, but it also is a LOT harder to use all your tokens with. Plus they give you a free reset a week right now. I'd even go so far as to say I think it's better than Fable 5. With an asterisk... That asterisk: For anything very big, I'm having it do review loops using Claude Code + Opus 5. For example, I gave it a few paragraphs describing a bi-directional rsync without the Unison database. It worked solo for 4-5 hours and in my testing it works great. I gave it a few small feedbacks (give me progress in human units not bytes, there's a bug syncing a specific file rather than directory, etc).

muglug 2mo ago

> MirrorCode only covers programs with plain-text output. MirrorCode does not test AI abilities in software that is inherently visual It’s an interesting eval but this is a distinct limitation — most applications that consumers use directly are not purely text-based.

mkagenius 2mo ago

Couldn't find what exact tests they are running. The GitHub repo is very obscure to be read by my human brain.

franze 2mo ago

my biggest so far was https://apps.apple.com/at/app/aifcc-ai-first-computer/id6782364574?l=en-GB&mt=12 https://apps.apple.com/at/app/aifcc-ai-first-computer/id6782... a whole linux distro in a mac app. claude worked for about 2 weeks.

sreekanth850 2mo ago

We (Team of 3) built a retrieval engine from scratch in .NET for investigative search, with AI doing most of the backend implementation while humans handled the design and architecture and front end. It was developed using a task based, incremental approach. Includes more than 3,000 tests and extensive documentation, and is optimized for asynchronous ingestion using NATS and a disk backed WAL. It includes a custom WAL, structure aware semantic chunking, ingestion and indexing pipelines, hybrid (rrf), text and vector retrieval, ACL aware filtering at query level, query classifier, citation tracking, and a CrateDB backend that performs fused retrieval over a single table. total LOC: 180 K. Status: evaluation by State agencies for investigative search on legacy documents. tool used - Codex, Luna, and earlier codex 5.3, and gpt 4 mini.

AlotOfReading 2mo ago
How long did it take you to review things for correctness? Like an incremental query engine is a few thousand lines of reasonably difficult code on its own, a reliable WAL is another few thousand, etc, and each of those might take me days or weeks of thinking to have any serious confidence in them. I guess you can get provenance and ACL tracking "for free" from a query engine, but it still seems like a very ambitious project.
- sreekanth850 2mo ago
  It took about four months. Correctness was reviewed continuously rather than only after implementation. We built the system incrementally in small slices, with humans handling the architecture, and review while AI performed much of the implementation and front end is fully doe by humans. The ingestion side includes durable asynchronous processing, recovery and replay, idempotency, strict ordering, structure aware segmentation, parent child relationships, and batched local embedding. The retrieval side includes hybrid (rrf), text and vector search, document versioning, collection and category scoping, document level ACL enforcement, parent expansion, provenance, and citation tracking. Your estimate is fair. But, reliable WAL and retrieval layer are each substantial projects. We did not build the underlying database query engine from scratch, but the correctness work around ingestion, recovery, hierarchy, security, ranking, and provenance was still significant. The project now has more than 3,000 automated tests, including coverage for recovery, concurrency, ordering, ACLs, versioning, and retrieval behaviour. Tests are not proof of correctness, but incremental implementation, explicit invariants, failure testing, and continuous human review are how we built confidence in it. We also had script to automate live endpoint tests with real data samples, this help largely to mitigate issues after a refactoring or enhancement. total production code is 80k LOC and rest is test coverages. One thing to say after seeing the results are, many of the RAG framework does it wrong. We got exceptional results and we also have agentic retrieval for answering complex queries. We designed it in our own way because government systems cannot afford false results, especially when the product is intended for crime investigation or compliance management in banks. Edit: I forgot to mention that we also support PostgreSQL. After building this system, we concluded that vector dimensionality is only a small part of retrieval accuracy. We use 384 dim embeddings and still achieve excellent results.

colechristensen 2mo ago

I'm trying to build a competitor to Parasolid and... it's a struggle getting towards the end of the first 90% of the work. It's getting back to the point where I'm buying textbooks and reading papers because moderate domain knowledge is not enough to drive LLMs to completion here. The space is too big and the code too extensive for a 1M context window and the capabilities of the current SOTA models.

ErenayDev 2mo ago

Seems like most of developers and humans vibecoding new projects with LLMs. But I can see they have problems in the codebase with AI generated code, or security breachs etc. Whats the limit of it? In other words, I'm curious about what the future holds.

Art9681 2mo ago
Given all of the CVE's being published for software that's been around decades and being tended to by the most talented businesses and individuals in the world, the least of my concerns are vibe coded projects that will never be used. All software has problems. Most engineers spend their careers fixing issues caused by previous engineers. This was the case before LLMs, and it will continue to be the case for the foreseeable future.

NewEntryHN 2mo ago

> without access to the original source code All models in the leaderboard probably have had access to the original source code in their training data.

alienbaby 2mo ago
Which is discussed in the article, tbf

manoDev 2mo ago

This actually proves the hardest part of software engineering is defining requirements.

wannabe44 2mo ago
That makes it sound like a PM problem. The hardest part is building the mental model which doesn't crumble. I have worked with humans who had the same failure modes as LLMs.

_doctor_love 2mo ago

What size project an AI can complete on its own is IMHO not an interesting question. The AIs and harnesses are getting good enough that quite large things can be built, primarily as a function of cost. The interesting question in my view is what is the largest system an AI can maintain over a long time horizon -- while keeping a coherent architecture that adapts and responds well to change. All the while remaining performant, scalable, and understandable to humans. I am unaware of what is the currently longest-lived vibe coded project but it cannot be more than a few years old. So to my mind, we don't have very much real data yet.

htrp 2mo ago
isn't this a harness engineering problem at its core (and the human equivalent being a product org problem)?
- _doctor_love 2mo ago
  I don't believe it is. If we think of AI-maintained software as automated gardening / automated agriculture, the harness is all the machinery that can plow, sow, water, monitor, harvest, etc. There still has to be a human stewarding the overall ecosystem and tending the garden. The human is 'on top' of loop rather than 'in it' but I don't think a harness by itself can be a good steward of architecture. Product org problems also is very context-dependent. Product orgs can work significantly different, in some orgs humans desire to be deep in the details, in other orgs the product folks are happy to let engineering drive the boat beyond a certain level of complexity. So one size harness can't necessarily fit all.
arstructinc 2mo ago
I think reproducing existing software and building a new product are quite different tasks. In a benchmark, there are usually clear tests and a correct reference. In actual product development, requirements are often unclear, and we do not always know what the correct result is. Passing tests is also not enough to confirm security, maintainability, or operability. I would like to see a benchmark where an AI continues changing the same product for several months. It would be interesting to see whether the architecture remains understandable and safe after many changes.

guybedo 2mo ago

i've been working on several rather large projects these past few months, and i'm trying to write as little code as possible. I don't think i wrote more than 10 lines of code in the largest project i'm working on. Lines of code: Java: 900_635, typescript: 725_418, C++: 180_445, Dart: 96_181. It's been obvious from the start that no model, as good as it is, can do large(-ish) amounts of work by its own without supervision, control, criticism, etc ... If left unsupervised, models usually do half the work, leaving stubs and todos everywhere. Quality comes from applying software engineering principles as much as possible, just like you would do with teams of junior devs: planning sessions and implementation sessions with adversarial critiques, specifying as much as possible upfront, planning unit/smoke/integration tests, etc ... Many systems rely on swarm of agents to build software but i've found it very difficult to get good results without lots of overhead/token waste because of inter agent communications mostly. So instead i built what is mostly a workflow engine to structure / organize processes into workflows with different agents assigned different roles. I've setup a basic landing page here https://kodfactory.com https://kodfactory.com if anyone wants to follow along.

eithed 2mo ago

I'm looking at these reports and then at my sessions with Claude. In latest one, it got stuck in a loop fixing one thing and breaking another for 10 iterations, until I've noticed and gave it the insight. Or where it started continuously writing "echo", I don't know for what purpose. And I ask how, how can you not be the driver; how can you let these agents roam free and actually accomplish things.

dnnehgf 2mo ago
we bomb schools and walk past beggars and devote our surplus time and money to scones and brain rot and gym memberships. our c code probably looks at us and says: how, how can you let these agents roam free. llms are not code and are incompetent and inefficient relative to code, but on the other hand you cannot roam without a little echo echo echo in your blood.
- IncandescentGas 2mo ago
  Thanks, your comment got this song stuck in my head. (I like my gym membership, thank you very much. I even visited the gym once several months ago.) We wasted every second dime On diets, lawyers, shrinks, and apps, and flags And plastic surgery - So long & thanks for all the fish, a perfect circle

andai 2mo ago

Would be nice if they also tested other models instead of several old versions of GPT.

cobbzilla 2mo ago

whatever it is, it will be heavily skewed in favor of whatever there is a lot of examples of. AI could bolt together many cookie-cutter pieces into a very large YOLO system. The trouble comes when you now want that system to do something unique for your business, and those ripple effects through the entire system you didn’t design become very painful.

tulio_ribeiro 2mo ago

This one is also very interesting: https://programbench.com/ https://programbench.com/

vcryan 2mo ago

It really depends on how you define "complete". It's possible the answer in "not large at all" if we're looking for high-quality, reliable, user-friendly software that works near perfectly after being written, tested, deployed completely by AI with no human intervention. People who try to get AI to do a lot of un-manned work seem to have a bit of a bias toward viewing LLM work as "good" or "good enough" in a way that they wouldn't have accepted in the pre-AI era.

mikewarot 2mo ago

It's smaller than an actualization of the Memex, that's for sure. It's been 80 years since it was seen as a good solution to the greatest problem facing science at the time. 80 years, yet we still can't mark up hypertext without copying and then modifications to the source.

vivzkestrel 2mo ago

- I ll consider any of these models as god tier the day one of these 2 happen - They build a browser engine from scratch that implements every whatwg spec for the last 15 yrs, fixes every bug chromium has and runs 10x faster than chromium - they build a super highly optimized version of windows that runs much much faster than windows 11

inigyou 2mo ago
People who are 100x more productive than before should seriously consider doing one of these. Should only take a few months at 100x productivity.

Terratrader 2mo ago

So I can not code. I just tell an AI the concept of what I want my software to do and execute in blocks. Most operations are well defined and depend on a central mechanism and that is where everything starts. The rest is like an octopus : the body defines the concept, the arms the consequences. This way there is no need for long code since every segment has a clear beginning and end. It is also quite efficient overall. My point is that long code can be circumvented by clever architecture.

SwtCyber 2mo ago

I think the best part about this benchmark is that they finally stopped pretending you can evaluate a complex engineering task with $ 5 worth of inference. If a task takes a human weeks, you need to give the agent a comparable search space to find solutions

cyanregiment 2mo ago

Accurately? About a ~500 line React component or API endpoint given lots of very good context Which is not bad I don’t bother with the agentic stuff anymore than I would run a calculator on auto - what is the actual point if you think of what that really means

harrouet 2mo ago

It is very hard to make sense of these benchmarks given that at least some of these models have probably been trained on the code base of these projects.

dividedcomet 2mo ago

I’m making an in-terminal IDE over at https://github.com/paradise-runner/toast https://github.com/paradise-runner/toast and deep seek flash has been doing a wonderful job since the last weights release. Makes me think we’re past expensive AI to do general engineering work.