The port I couldn't ship

I recognize this part:

> I don’t recall what happened next. I think I slipped into a malaise of models. 4-way split-paned worktrees, experiments with cloud agents, competing model runs and combative prompting.

You’re trying to have the LLM solve some problem that you don’t really know how to solve yourself, and then you devolve into semi-random prompting in the hope that it’ll succeed. This approach has two problems:

1. It’s not systematic. There’s no way to tell if you’re getting any closer to success. You’re just trying to get the magic to work.

2. When you eventually give up after however many hours, you haven’t succeeded, you haven’t got anything to build on, and you haven’t learned anything. Those hours were completely wasted.

Contrast this with you beginning to do the work yourself. You might give up, but you’d understand the source code base better, perhaps the relationship between Perl and Typescript, and perhaps you’d have some basics ported over that you could build on later.

Funny to see this show up today since coincidentally I've had Claude code running for the past ~15 hours attempting to port MicroQuickJS to pure dependency-free Python, mainly as an experiment in how far a porting project can go but also because a sandboxed (memory constrained, to us time limits) JavaScript interpreter that runs in Python is something I really want to exist.

I'm currently torn on whether to actually release it - it's in a private GitHub repository at the moment. It's super-interesting and I think complies just fine with the MIT licenses on MicroQuickJS so I'm leaning towards yes.

Its got to 402 tests with 2 failing - the big unlock was the test suite from MicroQuickJS: https://github.com/bellard/mquickjs/tree/main/tests

Its been spitting out lines like this as it works:

  I see the issue - toFixed is using
  Python’s default formatting which uses
  round-half-to-even rounding, but
  JavaScript uses round-half-away-from-zero.

It's amusing to think that claude might be better at generating ascii diagrams than generating code to generate diagrams, despite it being nominally better at generating code.

I'm generating a lot of PDFs* in claude, so it does ascii diagrams for those, and it's generally very good at it, but it likely has a lot of such diagrams in its training set. What it then doesn't do very well is aligning them under modification. It can one-shot the diagram, it can't update it very well.

The euphoric breakthrough into frustration of so-called vibe-coding is well recognised at this point. Sometimes you just have to step back and break the task down smaller. Sometimes you just have to wait a few months for an even better model which can now do what the previous one struggled at.

* Well, generating Typst mark-up, anyway.

It is really important that such posts exist. There is the risk that we only hear about the wild successes and never the failures. But from the failures we learn much more.

One difference between this story and the various success stories is that the latter all had comprehensive test suites as part of the source material that agents could use to gain feedback without human intervention. This doesn’t seem to exist in this case, which may simply be the deal breaker.

I won't deny OP learned something in this process, but I can't help but wonder: if they spent the same time and effort just porting the code themselves, how much more would they have learned?

Specially considering that the output would be essentially the same: a bunch of code that doesn't work.

While there's not a lot of meat on the bone for this post, one section of it reflects the overall problem with the idea of Claude-as-everything:

> I spent weeks casually trying to replicate what took years to build. My inability to assess the complexity of the source material was matched by the inability of the models to understand what it was generating.

When the trough of disillusionment hits, I anticipate this will become collective wisdom, and we'll tailor LLMs to the subset of uses where they can be more helpful than hurtful. Until then, we'll try to use AI to replace in weeks what took us years to build.

As always, the answer is "divide & conquer". Works for humans, works for LLMs. Divide the task into as small, easy to verify steps as possible, ideally steps you can automatically verify by running one command. Once done, either do it yourself or offload to LLM, if the design and task splitting is done properly, it shouldn't really matter. Task too difficult? Divide into smaller steps.

> The port I couldn't ship

It turns out that having a "trainer" to "coach" you is not a coincidence: these two words evolved together from the rail industry to the gym. Do "port" and "ship" have a similar history, evolving together from the maritime industry to software?

I wonder how well Claude would do at porting Heimdal's ASN.1 compiler to Rust, Swift, Java, etc. I wonder how well it would do at porting Heimdal's lib/hx509. I think the latter would be much easier than the former. But I'd expect that porting the krb5 code would be much harder still.

How many hours until someone else is able to get it to work?

I consider myself a bit of an expert vibe engineer and the challenge is alluring :D

It should be doable. This WAS doable: https://github.com/7mind/jopa

Claude was able to write a NES emulator for my engine from scratch, on 3rd try

https://github.com/willtobyte/NES

fun to read this in the context of the recent news that microsoft wants to port all their C/C++ to Rust in 5 years with "AI and algorithms"[0].

I'm sure the MS plan is not just asking Claude "port this code to rust: <paste>", but it's just fun to think it is :)

0: https://www.theregister.com/2025/12/24/microsoft_rust_codeba...

You don't know what the model is capable of until you try. Maybe today's models are not good enough. Try again next year.

Easy come easy go

This is unfortunate. I thought porting code from one language to another was somewhere LLMs were great, but if you need expertise of the source code to know what you are doing that's only an improvement in very specific contexts: Basically just teams doing rewrites of code they already know.

Our team used claude to help port a bunch of python code to java for a critical service rewrite.

As a "skeptic", I found this to demonstrate both strengths and weaknesses of these tools.

It was pretty good at taking raw python functions and turning them into equivalent looking java methods. It was even able to "intuit" that a python list of strings called "active_set" was a list of functions that it should care about and discard other top level, unused functions. The functions had reasonable names and picked usable data types for every parameter, as the python code was untyped.

That is, uh, the extent of the good.

The bad: It didn't "one-shot" this task. The very first attempt, it generated everything, and then replaced the generated code with a "I'm sorry, I can't do that"! After trying a slightly different prompt it of course worked, but it silently dropped the code that caused the previous problem! There was a function that looked up some strings in the data, and the lookup map included swear words, and apparently real companies aren't allowed to write code that includes "shit" or "f you" or "drug", so claude will be no help writing swear filters!

It picked usable types but I don't think I know Java well enough to understand the ramifications of choosing Integer instead of integer as a parameter type. I'll have to look into it.

It always writes a bunch of utility functions. It refactored simple and direct conditionals into calls to utility functions, which might not make the code very easy to read. These utility functions are often unused or outright redundant. We have one file with like 5 different date parsing functions, and they were all wrong except for the one we quickly and hackily changed to try different date formats (because I suck so the calling service sometimes slightly changes the timestamp format). So now we have 4 broken date parsing functions and 1 working one and that will be a pain that we have to fix in the new year.

The functions look right at first glance but often had subtle errors. Other times the ported functions had parts where it just gave up and ignored things? These caused outright bugs for our rewrite. Enough to be annoying.

At first it didn't want to give me the file it generated? Also the code output window in the Copilot online interface doesn't always have all the code it generated!

It didn't help at all with the hard part: Actual engineering. I had about 8 hours and needed find a way to dispatch parameters to all 50ish of these functions and I needed to do it in a way that didn't involve rebuilding the entire dispatch infrastructure from the python code or the dispatch systems we had in the rest of the service already, and I did not succeed. I hand wrote manual calls to all the functions, filling in the parameters, which the autocomplete LLM in intellij kept trying to ruin. It would constantly put the wrong parameters places and get in my way, which was stupid.

Our use case was extremely laser focused. We were working from python functions that were designed to be self contained and fairly trivial, doing just a few simple conditionals and returning some value. Simple translation. To that end it worked well. However, we were only able to focus the tool into this use case because we already had the 8 years experience of the development and engineering of this service, and had already built out the engineering of the new service, building lots of "infrastructure" that these simple functions could be dropped into, and giving us easy tooling to debug the outcomes and logic bugs in the functions using tens of thousands of production requests, and that still wasn't enough to kill all errors.

All the times I turned to claude for help on a topic, it let me down. When I thought java reflection was wildly more complicated than it actually is, it provided the exact code I had already started writing, which was trivial. When I turned to it for profiling our spring boot app, it told me to write log statements everywhere. To be fair, that is how I ended up tracking down the slowdown I was experiencing, but that's because I'm an idiot and didn't intuit that hitting a database on the other side of the country takes a long time and I should probably not do that in local testing.

I would pay as much for this tool per year as I pay for Intellij. Unfortunately, last I looked, Jetbrains wasn't a trillion dollar business.

Good luck to Microsoft trying to port a billion lines of C++ mazes to Rust with their bullshit machines, I'm sure they won't give up on that one after half a week

You don't ship a port. You ship to a port.