Changelog

A note from the maker

Today, developing an app isn't so much about the technology — it's about the thought process behind it, and the time spent refining and perfecting what the product puts out. That effort is what makes Fabled Flow what it is in privacy, speed, and accuracy.

I created Fabled Flow because I wanted a non-cloud alternative: private, local, and genuinely affordable. I tested many dictation products, and none met my speed and accuracy requirements. So I built one that would — and benchmarked it until dictating felt almost magical: your words appear in the window quickly and accurately, with no audio ever sent to the cloud, and formatting only when you ask for it.

My goal is to make products that close a real gap while staying private and accessible to as many people as possible, on as modest a computer as possible — which is why Fabled Flow is proudly developed on a first-generation Citrus MacBook Neo. I believe in local tools like Apple Intelligence, and where they fall short, in local backup models that do the same job — sometimes a touch slower, sometimes covering languages Apple's model doesn't. Everything below is the record of that refinement.

Since the first version on July 6th, Fabled Flow has been through 148 recorded rounds of refinement across more than 130 numbered releases — about 164 hours of recorded, hands-on build sessions in the first three and a half weeks alone (the session transcripts are kept, so that number is measured, not guessed), and the daily refinement has continued since. Very little of that was adding features. Most of it was process — human decisions about how dictation should behave, made by using the app hard and refusing to accept "close enough." Some of what came out of it you won't find elsewhere: the end of every long dictation gets a second listen, because trailing words are the easiest words to lose — and when the engine can't hear the same last words twice, the app tells you instead of pasting a guess. The microphone is genuinely off between dictations — we took on real engineering pain to keep it that way rather than soften the promise. Every dictation keeps its receipts, so when something goes wrong we can prove where — engine, pipeline, or app — in seconds. Every refinement started life as a real dictation that went wrong on a real day, kept verbatim as a test the product must pass forever. And every rule the product applies to your words fits on one page, on purpose — rule sprawl is the quiet killer of dictation tools. None of those calls came from AI; they came from caring about what lands at your cursor. The record below is what that looks like, release by release.

Privacy has a price, and I chose to pay it. Because your voice never leaves your Mac, there's no cloud model to lean on — every gain in accuracy had to be earned by hand, rule by rule, through decisions that have nothing to do with coding and everything to do with how dictation should behave. When the app smooths a stumble ("c… customer"), no language model did that — none can, and there's no standard to download; it came from hours of testing real speech. Even the features still to come are made this way: your Mac quietly notices — locally, only for you — which sound-alike words you personally fix ("are", "our"), so that one day the app can offer you corrections built from your own patterns instead of imposing someone else's. And through all of it, every word you speak is kept: no rewriting, no quiet deletions, a complete record. Try dictating A47_MAN/59-138(B) in any other dictation app — the difference you'll see is where all those hours went.