The real problem is not terms like "load-bearing," which communicate clearly enough. It's the constant invention of cryptic shorthand terms and phrases that have no referent, and end up acting like a puzzle to be decoded. This is often paired with hyphenation, but not always:
"The current behavior paper" -> The behavior in the running system that was previously described as papered over.
"Marker transport over-claim" -> The inaccurate review finding on the object's sentinel flag in the API response.
I suppose the cryptic/invented language problem is about token efficiency? But this sort of token efficiency is extremely difficult to deal with when it comes to conversation with a human about complex system. It might be efficient inside reasoning blocks, but when the model generates the final turn text, it should avoid this, as it's brutally inefficient due to the time spent wondering what each uniquely coined phrase means and having to ask for constant clarifications, which then you have to wait for another turn, eating up time and context while it burns more xhigh reasoning just thinking about how to explain its own awful language.
I have this exact problem with 4.8 and Fable. Sometimes I can barely understand what it’s saying. I’m no english first-language speaker, but I don’t consider myself bad at English either, and it’s gotten increasingly hard to understand Claude’s claims and explanations.
Take it to sonnet 5 or gpt and ask it to explain this to a layman. If you still don’t get it ask it for the why it matters or the how it relates.
You can also ask fable/4.8 to do it but I find it helps to keep the working model surrounded by the complexity rather than drawing it out. Simplifying text is something that takes relatively low effort in comparison to technical tasks. Sometimes I use Gemini, deepseek, grok, and recently meta just to see if they have any added perspectives, sometimes they do. Meta is really good at turning a technical mess into a story that paints a picture in my head.
I hope it's actually talking to itself at higher bandwidth, and hope this is because of training that succeeds better given quality inter-agent communication.
For those who care to read everything rather than walk away, Fable can be extraordinarily dense. I suspect they'll pull the promo before I learn to read it.
Still, I feel I must read rather than correct it, as results are that much better if I let it do this "with" itself: orchestrator to agents back to orchestrator.
You're absolutely right to flag this. We could enhance the authors approach by adding a belt-and-suspenders system using Claude.md as ledger and robust sidecar process to create a load-bearing idempotent production-ready system. The comments here provide a smoking gun and sharpens my previous conclusions. I'll get started on a quick smoke-test and let you know when it's landed.
Want me to take a first pass looking at other surfaces this vocabulary change could effect? Or would you like me to find other methods of reducing my vernacular to more terms that are more concise rather than verbose.
"The current behavior paper" -> The behavior in the running system that was previously described as papered over.
"Marker transport over-claim" -> The inaccurate review finding on the object's sentinel flag in the API response.
I suppose the cryptic/invented language problem is about token efficiency? But this sort of token efficiency is extremely difficult to deal with when it comes to conversation with a human about complex system. It might be efficient inside reasoning blocks, but when the model generates the final turn text, it should avoid this, as it's brutally inefficient due to the time spent wondering what each uniquely coined phrase means and having to ask for constant clarifications, which then you have to wait for another turn, eating up time and context while it burns more xhigh reasoning just thinking about how to explain its own awful language.