I'm sure there are other caveats. Also cost of the more or less straightforward Bun port was $165000 for 500KLoC.
Another point is, Fable is reasonably cheap if you don't allow it to read or write.
If the experiment was "spend 400 bucks to see if it'll work" then that's awesome, and fun, and a cool use of AI. It's impressive that AI can do that.
If it was to make something useful ... has it?
$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.
My friend once sent me a snippet, maybe 10 lines of C+++, asking "can you spot the UB?".
So I'm staring at these 10 lines, I KNOW there is an UB. I wasn't able to find it without a hint.
(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
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.
It is probably because I read tons and tons of LLM output.
*be me over eager
I haven't played with GLM 5.3, only with 5.2, so I cannot say for sure.
GLM is at the level of Opus. Fable is something different entirely. It is capable of tracing the data flows of the app, I even tried it in a huge PHP codebase, it works.
PHP is a weird beast because it allows something like
$v = 'SomeClass' + 'Controller'
... // and later
new($v)
And another distinct feature of Fable — it is an amazing orchestrator. I prohibit it basically read and write, and it operates a swarm of haiku and sonnet.
LLM-powered rewrites and huge refactors are better done using 1 additional step "convert the code to <something> that represents it best".
The simplest example is, for a CRUD app it can be swagger description. The more complex behaviour exhibit the app, the more raw information should be provided.
Like ontologies, "A is a child of B" model can derive and enforce that "B is a parent of A", and so on.
On top of that, I write that Fable is reasonably cheap if one uses it solely for agent orchestration.
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 formulae3 Convert representations back to code
My gut feeling is this is doing way too much, and it would've figured it out.
The whole point of this experiment was to try and make the rewrite as cheap as possible.
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.
Probably I need some fresh air and a good fiction book.