I have been using Claude to build a clone of Bash in Rust (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.
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.
I find most of the time it would be fine on its own, but every now and again you avoid a really bad degradation through guidance. Some kinds of big change definitely problematic.
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.
"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.
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.
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.
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.
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.
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).
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.
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.
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.
> 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.
I have been using Claude to build a clone of Bash in Rust (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.
> 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
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.
I find most of the time it would be fine on its own, but every now and again you avoid a really bad degradation through guidance. Some kinds of big change definitely problematic.
I believe they can make ANY software project the largest it can be!
This solution should look for more problems.
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.
"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.
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.
I am curious how Claude Opus 5 fares - similar, better, or (my guess) worse than Fable 5.
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.
Running a Loop on the well defined goal seems promising: https://www.youtube.com/shorts/EtxdB7gpFqY
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.
Why just the commercial ones !??
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.
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).
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.
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.
How would 100% non-human code possibly be safer?
well if driving is supposedly safer when automated, why not code
The qualities of computation that make autopilots safer than human drivers aren’t the same qualities that produce better designed software products.
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.
It’s supposedly safer than the average driver, not safer than professional drivers.
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.