"Use the graphics card" was a flag and nothing else: whisper got -ng
when it was off, llama got -ngl 99 or 0, and nobody looked at what
happened next. A build without a GPU backend runs on the processor
while the box stays ticked, which is what the release archives for
Linux do, every time. Nothing anywhere reported whether the local
server was even up.
Both servers already say where the model went, in the log Dikte
captures. It is read back once the server reports ready and turned
into a backend, a card and, for llama, the layers it offloaded. The
verdict comes from what whisper committed to -- "using X backend" and
the model buffer -- rather than from the devices it merely listed: a
card that is found and then fails to initialise sends it back to the
processor, and the listing alone would have called that a graphics
card. A log that says nothing stays "could not tell" instead of being
guessed at; a hand-built macOS whisper has Metal compiled in and
prints no backend line at all.
The state is then somewhere to be seen. Server.state() is a snapshot
of the process and what it settled on, and it reaches `dikte status`,
`dikte doctor` and a line under each local model box in the settings
window. doctor reads the log from disk when no instance is running, so
it still answers on a machine where Dikte is closed, and it says which
of the three it is: what the last run used, that the last run said
nothing, or that none ever ran here. Where the card was asked for and
not obtained, the line says which of the two it is -- none was found,
or this build carries none -- because only the second is worth
replacing a download over.
The tests grew a second isolation. They read ggml's own data
directory, which is the real one on the machine running them, and
program_path prefers a whisper-server on the PATH, so the suite
answered from whatever the developer happened to have installed. Both
are now the test's own.
Co-Authored-By: Claude Opus 5 <[email protected]>
Claude-Session: https://claude.ai/code/session_019zqCqhmeaNT6m1GPp8ZWPp
Google's compatibility layer has no word for off. Sending
reasoning_effort "none" is refused outright, so choosing Thinking → Off
made every cleanup fail and paste the raw transcript instead:
HTTP 400: Request contains an invalid argument. (INVALID_ARGUMENT)
Measured against gemini-3.5-flash-lite, asking it to reply "ok":
nothing sent 50.59s
reasoning_effort "none" 400
reasoning_effort "minimal" 15.40s
reasoning_effort "low" 53.35s
reasoning_effort "high" 63.83s
So "none" lands on "minimal", which is both accepted and the quickest of
them, and quickest is what cleanup wants. The thinking_config route the
documentation offers is an SDK wrapper and is not a field this endpoint
knows: sending it is "Unknown name \"google\"".
Co-Authored-By: Claude Opus 5 <[email protected]>
Google answers some failures with a JSON array holding the object every
other provider sends on its own. _extract_error called .get() on it and
raised AttributeError, which is not the ApiError every caller is holding,
so a 503 from Google took the whole dictation down instead of pasting the
raw transcript with the failure shown beside it.
Found by dictating against a Google AI Studio outage:
HTTP Error 503: Service Unavailable
AttributeError: 'list' object has no attribute 'get'
It runs while an exception is being raised, so it now ends in a string
whatever arrives.
Co-Authored-By: Claude Opus 5 <[email protected]>
OpenRouter's free tier rate-limits and carries no free Gemini model, and
cleaning up through Claude Code costs a fixed few seconds because it opens
a whole CLI session to drop three "uh"s. Google's own free tier suits a
short, frequent request, and its OpenAI-compatible endpoint answers
/chat/completions, so cleanup there is one request and the same code path
OpenRouter already takes.
The one thing that is not shared is the thinking level. Google reads
OpenAI's flat reasoning_effort rather than OpenRouter's object, and "none"
is how thinking is turned off, so it is sent rather than skipped: a Flash
model left to think spends exactly the second this provider was chosen to
save. Its top two rungs land on "high", which is as far as Google goes.
Speech to text stays where it was. That endpoint has no
/audio/transcriptions behind it, audio only goes in as base64 inside a
chat message, and what comes back has none of the segment times a subtitle
file or a meeting transcript is built out of.
Antigravity joins as well, on cleanup and as an agent. It is a CLI like the
other two and costs the same session, so it is here for people who already
pay for it rather than as an answer to the speed. It takes neither an empty
tool list nor a read-only sandbox, and cleanup.py now says so plainly
instead of implying parity; what it gets is a project of its own, the home
directory, and its slash commands off.
Three things were already wrong and are fixed on the way past, because the
new providers walk the same paths: doctor raised KeyError on the local
model, whose executable is ""; the history recorded Claude's model whoever
answered; and every agent row read "asked Claude".
Co-Authored-By: Claude Opus 5 <[email protected]>
