Fifteen changes aimed at the first day of real use:
- Import and media jobs persist to <data>/jobs-*.json and reload on start
(running ones settle as errors); the Register button no longer vanishes
because the gateway restarted.
- Docker ships the cortiq binary (built in the builder stage) — local .cmf
chat models and image/video generation now work in containers, where
auto-install never could (no cargo in the runtime image). libvulkan1 added
for optional GPU offload.
- Open admin console on 0.0.0.0 without a token now warns in the log and
shows a dismissible banner — the VPS footgun is at least labeled.
- "Restart gateway" button in Settings: stops managed model servers, spawns
a fresh copy (bind retried while the port frees), Docker relies on the
container restart policy instead.
- Dashboard "System" panel: CPU sparkline + load, RAM (with the gateway's
own RSS), free disk at the models dir — sampled by sysinfo every 2 s.
- Featured model cards warn "not enough disk" / "may not fit in RAM" before
the download, from the same live snapshot.
- Log viewer in the sidebar: a 500-line in-memory ring fed by a second
ANSI-free tracing layer; filter, level, pause, copy.
- Update check: crates.io polled twice a day, the console shows an
"update available" notice.
- Media generations queue up (with position shown) instead of being
rejected; progress gained an honest ETA from measured throughput.
- Generation gallery under the image/video tabs: lazy PPM thumbnails,
click to reopen with seed/steps, delete.
- Playground chat is a real dialogue now: history is resent, bubbles,
Clear, Cmd/Ctrl+Enter.
- "Connect your app" snippets (curl / Python / JS) on the keys page, with
the real key inlined right after creation.
- One-click benchmark on the Models page via `cortiq bench --json`.
- Config export now downloads the actual gateway.toml (comments intact);
import accepts TOML (validated, hot-reloaded) and the legacy JSON.
- Streaming token counts are exact when the model server provides usage
(cortiq-server patch, shipped separately); the gateway fallback now
counts content chunks (1/token on cortiq) instead of chars/4.
Wizard's Finish lands on the Playground. 37 new strings in all 7 languages.
The infosave collection now ships media models (Lumina-Image-2.0, MiniMax-H3
Turbo) that cortiq runs via `imagine`/`animate` CLI, not `cortiq serve`. The
gateway grows a media API: local .cmf files are classified by `cortiq info`
(chat/image/video, cached), generations run as tracked jobs with step progress
parsed from stdout, one at a time, cancellable, outputs under <data>/media.
The playground becomes three tabs. Image: width/height/steps/CFG/seed, live
progress, result rendered from P6 PPM to a canvas in the browser (PNG export
via toBlob). Video: frames/steps/quality/seed plus optional first/last
keyframes (rasterized to PPM client-side); the MJPEG+PCM AVI is decoded in JS
and played on a canvas synced to the wav — no ffmpeg on either side (mp4 remux
happens opportunistically when ffmpeg exists). A metrics card shows resolution,
steps, seed, total time, time per step/frame and file size.
Chat metrics: the local cortiq server sends no usage chunk in streams, so
token counts showed 0/0. tap_stream now injects one OpenAI-shaped usage event
ahead of [DONE] with the gateway's ~4-chars/token estimate, flagged
`cortiq_estimated`; the UI renders it as "≈ N / M" and adds tok/s and
time-to-first-token. Registration skips media packs (a `cortiq serve` entry
for a diffusion model would only fail) in both Register and the wizard's
Finish.
36 new UI strings translated across all 7 languages.
`cargo fmt --all -- --check` and `cargo clippy --all-targets -- -D warnings` are
CI gates and both were failing on this release's code.
- rustfmt across the files touched by the release
- `.split('/').last()` walks the whole iterator on a DoubleEndedIterator; use
`.next_back()` (4 sites in the setup/featured + delete handlers)
- `set_progress` had two identical branches; the intent is "take the first
reading, then only move forward", which is one condition
release.yml published unconditionally on a version tag, so a version already on
crates.io (released by hand, or a re-run of the job) failed the workflow — a
version can never be re-uploaded. It now checks the registry and skips instead.
Startup no longer needs arguments: the config is created on first launch, a busy
port is swapped for a free one, the console is open by default, and the browser
lands on a setup wizard that downloads a ready-made .cmf model and wires it into
routing (managed server + routing.default) when you press Finish.
Download progress was reported from curl's own meter, which prints a full
0→100% bar for the -L redirect hop before the transfer starts — so every job
showed 100% and then hung. Progress now comes from bytes on disk against the
size from the HuggingFace tree (or a HEAD request).
The UI and the gateway also disagreed about the output file name, so a finished
download never marked its card as downloaded; the gateway now returns the exact
stem and the UI sends it back verbatim for download, progress and delete.
Featured models are read live from the infosave HF account in both the wizard
and Import, and all 432 UI strings now exist in every one of the 7 languages.
Run models in the CMF (.cmf) format locally, via the native `cortiq`
CLI from crates.io — no Python and no external model server.
- cmf_runtime: install/update `cortiq-cli` from crates.io, spawn and
health-check `cortiq serve` as a managed local backend.
- import: model-import jobs shell out to the native `cortiq convert`
(HF repo -> .cmf, quant mapped Q8_2F->q8_2f / Q8_ROW->q8 / Q4->q4);
the Python converter is used only for advanced/exotic options.
- pipeline: local-first routing — serve from the local CMF model when
the router is disabled/unreachable, with a "local only" hard switch.
- registry/state: register the local CMF server as an OpenAI provider
and track local candidates.
- config: data-dir resolution + CmfCfg (auto-install/update, managed
server, host/port/model) and relaxed validation for the managed model.
- admin + settings UI (en/ru): "Local models (CMF)" settings card.
- cache.rs: cosine-similarity store (threshold, TTL, ring buffer), partitioned by
routing signature; tracks hit rate and model cost saved
- pipeline: embed routing text, look up before the model call (route_source=cache on
hit), insert successful responses; stats record cache hits
- config [cache] + admin settings card; dashboard shows hit-rate and savings
- embedding model resolved from the pool (or first kind="embedding")
infosave2007