Are you implying a direct bug for bug port is more expensive than converting to an intermediate representation and then converting via generators? Depending on the "fidelity" of the representation I'd figure the costs are about the same.

You are correct, that this is not the best Rust learning material.

$400 are subsidized into the subscription, and this was mainly an experiment to prove the theory about data conversion step. I call it a success and I use rune editor daily.

To me, running multiple agents is not very different from managing multiple teams — I won't be able to keep up with the changes by reading the code.

I may make certain architectural decision, and I need to act based on some signals.

The simplest example is clusters of bugs are signaling that certain modules are dirty. Sometimes I read a plan and understand that the agent is trying to workaround some auwful engineering.

I work on a C++ codebase. I frequently prototype in c and then have the ai model slopify it back to c++. Nice b/c I am faster at reading c, but hard b/c you miss some of the features/nuances of c++.

I realized that I haven't answered the question. These $400 also include the tests. Fable ported "human fuzzing session" (the best bug hunter) from Go to Rust and used it to validate everything else. I used hierarchical state machines, so a lot of my QA gates were encoded into the implementation — impossible states are, well, impossible.

(I ported first 80% practically in one shot, planning and then leaving Fable overnight to orchestrate). Then I added a bunch of features, so at the end I ported more like 150% of the original code, I added tree-sitter, and a bunch of syntaxes highlighters. At the end with all that, price went up to ~$650

All very good questions.

Agents are actively destroy QA gates in many ways, usually by cheating ("the test is buggy, not my changes" — changes the test), or just rot QA slowly by writing buggy overcomplicated tests

What works for me 10/10 is fuzzing and my own constant usage. For this project specifically (text editor), I asked LLM to create human-like fuzzing session, it sends keystrokes like: "the user is searching for a file, editing, <ordering a lizard>, saves changes".

On top of it, I run https://mutants.rs/ which is kind of tests fuzzing. It flips random switches in the app itself, and if tests are silent - they missed a bug.

The downside of this, is I usually find bugs after 1-2 hours of running. I use local Qwen to babysit these sessions, to make initial investigation, a repro case, and file a ticket.

Well the author “cannot simply dye my hair blue” so maybe they can’t confirm semantic equivalence or behavior? Poke aside (and unserious intro?) seems like a general and loose question of if the conversion can happen.

*be me over eager

Thank you. Yes I would like to hear more about that - I’d be interested to read details about limiting some of its native tool use.

I found this part to be interesting/clever:

1. Extract the data representation

Ask the LLM to represent your code as any combination of:

    graphs
    ontologies
    hierarchical state machines
    UML process charts
    constraints
    math formulae

3 Convert representations back to code

honestly it is hard to believe that seeing your replies and this heading: "The secret sauce".

Giving the benefit of doubt, we all might be writing a bit like claude nowadays.

If that is the case, I'd recommend reviewing the content before publishing to see if it sounds like a LLM.

Or if you are trying to create "better" AI slop and think that is enough to say the text is human-written, don't do that, just say it was AI-generated or assisted.