I’ve been working on an experimental fork of Cotabby, with plenty of help from GPT-6 Astra along the way. I’ve made some fairly substantial changes around prompt-cache reuse, finishing partially typed words, ghost-text stability, refreshed local screen context, and how dismissed suggestions are handled.
I also put together a 1,337-case phrase-prediction benchmark. On that benchmark, the changes brought exact next-word accuracy from roughly the upper 20s/low 30s to around 37%. It’s a controlled synthetic test—not a claim about every model or writing situation—but it’s been useful for checking whether an idea actually helps.
I'm using the 1,337-case benchmark as part of an agentic hypothesis-driven development loop: come up with an idea, test an idea, measure it against the baseline, keep what works, and iterate again.
I’ve also made signed and notarized release builds. If you’re curious, you’re welcome to try one:
https://github.com/mc-hamster/cotabby/releases
I’ve been working on an experimental fork of Cotabby, with plenty of help from GPT-6 Astra along the way. I’ve made some fairly substantial changes around prompt-cache reuse, finishing partially typed words, ghost-text stability, refreshed local screen context, and how dismissed suggestions are handled.
I also put together a 1,337-case phrase-prediction benchmark. On that benchmark, the changes brought exact next-word accuracy from roughly the upper 20s/low 30s to around 37%. It’s a controlled synthetic test—not a claim about every model or writing situation—but it’s been useful for checking whether an idea actually helps.
I'm using the 1,337-case benchmark as part of an agentic hypothesis-driven development loop: come up with an idea, test an idea, measure it against the baseline, keep what works, and iterate again.
I’ve also made signed and notarized release builds. If you’re curious, you’re welcome to try one:
https://github.com/mc-hamster/cotabby/releases