Editing a file from the library used to silently supersede it: the worker auto-saved every result as a new version and the leaf-only listing hid the original, which read as a destructive overwrite. Tool pages now show a per-edit choice for library-sourced files. The default saves the result as an independent new file and keeps the original; picking overwrite keeps the old superseding-version behavior.
The client sends a saveMode multipart field next to fileId, validated with a 400 on unknown values, and autoSaveToLibrary branches on it. Every hand-written route that honors fileId parses the field the same way as the factory. The review panel shows where an auto-saved result went instead of offering a second, duplicate save. Tools whose route or submitter ignores fileId keep the selector hidden via a shared unsupported-tools set, and the choice resets to the non-destructive default whenever a new file is staged.
Closes#495
Comprehensive telemetry quality improvements across Sentry and PostHog, grounded in an audit of the live data plus current best-practice research.
Sentry: job_id/instance_id tags, operational fingerprinting, PII-safe settings context on bug events, web tag population + extension-noise filtering, an early-crash buffer, http status/method kept on breadcrumbs, and a gated-off-by-default performance-tracing re-enable (tracesSampler that zeroes db/redis/queue-poll root spans + drops the Redis integration) with worker job spans and canonical-host cron monitors.
PostHog: history_change SPA pageviews, instance_id super property for fleet rollups, enriched tool_used (formats, byte sizes, is_batch, execution_hint, real error_kind taxonomy), the previously-dead result_saved/batch_processed/ai_bundle_prompted events fired, search click-through, editor + Automate authoring + auth instrumentation, a before_send PII boundary, and minimal opt-in landing-site pageviews.
Every worker tool error now carries an input_format tag (file extension only, never the filename) for triage, derived once at the worker error path and added to the scrubber allowlist.
Postgres auth (28xxx), permission (42501), resource (class 53), and operator-intervention (class 57) failures now classify as operational via a cause-chain walk, not bug. pg query bugs (e.g. 42601) stay bugs.
Wrap convert and gif-tools process functions so a Sharp .toBuffer() failure carries an authored SafeError title (and the original as cause) rather than a scrubbed "Error: Error".
Fixes#533, found while working on #529/#531.
OIDC and SAML logins hard-blocked on the MFA policy with zero check of whether the user actually enrolled TOTP, and no challenge step at all. Once an admin turned on an MFA-required policy, every SSO user was permanently locked out regardless of enrollment status.
- Extract the post-auth MFA decision (challenge / enrollment-required / proceed) into a shared, unit-tested function so OIDC and SAML can't independently diverge again
- An already-enrolled user now gets a real challenge (reusing the existing, auth-method-agnostic MFA completion flow) instead of being blocked
- An unenrolled user under a required policy gets a distinct, correctly mapped error instead of the old generic one
- Fix a real fail-open regression caught in review: a transient DB error during the enrollment-status check could have silently skipped MFA entirely for an enrolled user; now it fails closed and logs
- Strip the one-time challenge token from the URL after consuming it
Target-size compression had only a coarse DPI lever, so it undershot badly (a 350KB target could land at 216KB) and silently missed unreachable targets. Adds JPEG quality as a second lever (forced re-encode so it bites on JPEG scans), folds both into one monotonic quality axis that target-size binary-searches, reports targetMet honestly in the panel across 21 locales, and flips the tool to async for the extra passes. Quality-mode output sizes shift intentionally (slider now drives JPEG quality at full resolution in its top half).
Add a once-per-boot instance_started event (arch, os, deploy_mode,
gpu_present) so the fleet architecture mix is measurable. It reuses the
existing per-instance instance_id and is exempt from the volume sample
rate, since a census that fires once per boot must not be thinned.
Restore useful capture depth now that the sponsored plan removes the
quota pressure behind the earlier hardening:
- PostHog sample rate 0.1 to 1.0 (full analytics when enabled); the
property allowlist still blocks file data.
- Sentry per-instance ceiling 20 to 500/hr, breadcrumb trail restored
(sanitized: urls/paths redacted, data payloads dropped), full stack
paths kept; local vars, request bodies, and PII still dropped. Both
api and web.
Honor ANALYTICS_ENABLED=false as an opt-out alias: it was documented on
the Docker Hub README but never wired in 2.x, so anyone who set it was
still tracked.
All capture stays behind the analytics opt-out gate.
Adds a dedicated remove-gif-background AI tool: removes the background from an animated GIF, WebP, or APNG frame by frame and reassembles a transparent (or composited) animation in WebP, APNG, or GIF, with full per-frame effects. Reuses the background-removal bundle. Verified end-to-end with the real rembg model.
Closes#496.
Follow-ups to the v2.1.0 Sentry telemetry overhaul, found by reviewing live release:2.1.0 events:
- error_code tag was empty because reportError read only the top-level err.code; add extractErrorCode() to walk the cause chain (pg SQLSTATE, node E-code, else first short code).
- InputValidationError from a tool's processV2 in the worker was logged as error_class=bug; classify it as expected for any source. Worker-side ZodError stays a bug (schema drift).
- AI dispatcher timeouts rejected with a bare Error, which the sanitizer scrubbed to a message-less "Error: Error"; reject with an operational SafeError (code "timeout") at both timeout sites.
Each fix written failing-test-first; affected and adjacent unit suites green plus full CI (integration + e2e).
Shared Claude Code translation pipeline (scripts/i18n, no API key) plus Astro/VitePress/Scalar i18n wiring. Landing and API reference translated into all 20 languages; docs i18n wiring + English source anchors. The translated docs markdown (apps/docs/<locale>/**, 3,620 files) follows in a companion PR because it exceeds GitHub's per-PR CI file limit.
Removes Sentry tracing entirely (BullMQ idle polling burned 4.8M transactions in 2 days at the baked 0.1 rate), decouples PostHog sampling, and replaces the type-only error scrub with a vetted-field sanitizer plus SafeError/ToolInputError contracts. One classified capture path with per-signature throttles and a per-process ceiling makes storms impossible (NODE-1E was 4,541 events from one 30s loop). Browser errors move to a dedicated web Sentry project with their own source maps. Adds the SNAPOTTER_TELEMETRY runtime kill switch and silences test fleets.
Crash fixes: remote 204/304 SSRF process kill (NODE-20), conversion-preset boot crash loop (NODE-21), Redis version preflight + unhandled subscribe rejection (NODE-1T), Sign PDF on plain-http origins (NODE-1K/1M), wavesurfer/pdf.js teardown rejections (NODE-1P/1N), bundle-import ZlibError to 400 (NODE-1Z), chart-maker input errors declassified (NODE-1H/1J), asset requests skip the session DB lookup (NODE-1D).
Make on-demand AI feature-bundle installs reliable and self-healing, closing
the failure modes behind most "some tool doesn't work" reports.
Multi-bundle installs: tools needing more than one bundle (Passport Photo,
Enhance Faces) install every required bundle from one action and stay
not-installed until all are present. Verified across all 19 AI tools.
Downloads: self-heal the accelerated Hugging Face (Xet) client so an upgraded
venv no longer silently falls back to slow urllib; restart instead of
corrupting a resumed partial when a proxy ignores Range and returns 200;
verify the completed size; fail fast on disk-full and HTTP 4xx; retry
transient errors five times; add hf_transfer fallback and document Xet egress.
Install integrity: crash-atomic venv writes so a killed or out-of-space
install can no longer tear the shared venv and break other tools; a boot
breadcrumb reseeds a torn venv to a clean state automatically; a post-install
smoke import test refuses to record a bundle whose libraries cannot load; an
install watchdog stops a wedged installer that would otherwise hold the venv
writer lock forever.
Adds unit and end-to-end tests for every failure mode above.
* fix(api): prevent a crash when an over-limit upload stream has no consumer yet
busboy's "limit" handler destroyed the file stream with an error but never
attached its own error listener, relying entirely on whatever consumes
part.file downstream to do so. On a fast enough connection (or a fully
buffered body, e.g. Fastify inject()), busboy can process enough bytes to
hit the size limit before the route handler's receiveUpload() call has
attached its own stream listener, leaving the resulting "error" event with
zero listeners -- which crashes the whole process by default in Node.
Surfaced by tonight's FULL_MATRIX+FUZZ integration run (880 uncaught
exceptions, all the same root cause). Reproduces deterministically in
isolation; unrelated to this release's actual code delta (file untouched
since PR #413, well before the baseline QA pass).
Fix: attach a baseline no-op error listener the moment the stream is
created, guaranteeing at least one listener always exists. EventEmitter
delivers "error" to every registered listener, so the real consumer's own
error handling is unaffected.
* fix(ai-bundles): rebuild upscale-enhance and photo-restoration to reconcile scipy ABI
upscale-enhance and photo-restoration both depend on codeformer-pip, whose
transitive closure (basicsr -> realesrgan -> gfpgan) pulls in an unpinned
scipy. Both bundles were last built ~June 18-19, before PR #437 added the
manifest's `constraints` array (numpy==1.26.4, scipy==1.12.0, etc.) to pin
exactly this kind of dependency during bundle builds. Only the ocr bundle
was rebuilt after that fix landed.
install_feature.py has no pip install step -- it's a raw tarfile extraction
with no cross-bundle conflict resolution, so installing OCR alongside either
stale bundle left three incompatible scipy versions' files mixed in the same
site-packages directory (a compiled _rotation.*.so from one release next to
Python files expecting a different release's API), breaking the `upscale`
tool and OCR's higher-quality tiers with an ImportError.
Rebuilt both bundles for amd64-gpu and arm64-cpu from the current manifest,
verified scipy/scikit-learn/scikit-image/pandas all resolve to the pinned
versions in the tarballs themselves, then verified end-to-end on real
hardware (Mac arm64 CPU and ubuntu_gpu .248 RTX 4070): installing all
affected bundles together now yields exactly one version of each constrained
package, `upscale` produces correct output, and OCR's balanced/best tiers
correctly use PaddleOCR-GPU instead of erroring out.
Published the rebuilt tarballs to the public deepsafe/feature-bundles
HuggingFace repo and updated this manifest's sha256/sizes to match.
Also adds verify-bundle-compatibility.sh: verify-bundle.sh checks each
bundle in isolation (a fresh venv per bundle), which is exactly why this
shipped twice -- nothing ever checked that bundles built at different times
agree once layered into the one shared venv real installs use. The new
script installs every bundle for an arch into one venv and asserts each
constrained package has exactly one, correct version.
Known follow-up (not fixed here, needs separate discussion): uninstalling a
bundle only removes its downloaded model weights, never the site-packages
it added, so existing installations that already hit this bug have no clean
self-service fix via uninstall+reinstall -- they need a full AI-venv wipe.
* fix(docker): bake a real rate limit default for the all-in-one one-liner
The documented single-container `docker run` install had RATE_LIMIT_PER_MIN=0
(effectively unlimited, ~50k/min) baked in, since only docker-compose.yml
carried a hardened override. A self-hoster following the one-liner path got
no meaningful throttling anywhere, including auth-adjacent routes with no
dedicated per-route limit. Bakes a generous-but-real 1000/min default into
the Dockerfile, raises both compose files' fallback to match so the two
documented install paths converge on the same posture, and updates the Zod
schema default plus docs that quoted the old value.
* fix(api): boot log undercounted tool routes by the conversion-preset total
The "Tool routes: N active" line logged before registerConversionPresets(app)
ran, so it only ever reported the base 158 tools, 83 short of the real
241-tool total. Presets have to register after the base loop (they delegate
to each base tool's own processV2), so the fix moves the log line to after
that call and has registerConversionPresets return its count instead of
reordering the dependency.
* fix(ai): forward {info}/{warning} stderr JSON instead of dropping it
The dispatcher stderr parser only recognized {ready} and {progress,stage}
shaped JSON lines; anything else that parsed as valid JSON (like ocr.py's
GPU-to-tesseract downgrade notice, an {"info": ...} line) matched neither
branch and fell through silently, never reaching docker logs. Adds explicit
{info}/{warning} handling that forwards to console.log/console.warn, same as
the existing [prefix]-tagged non-JSON path.
* fix(api): fall back to a lower OCR tier when PaddleOCR itself is unusable
ocr.ts already retries lower quality tiers on a crashed dispatcher, but the
condition only matched crash-style messages (segfault, exited unexpectedly).
ocr.py's own ImportError/exception handlers already produce messages telling
the caller to use a lower tier (e.g. on the scipy ABI conflict class of bug),
but nothing ever acted on them, so a broken PaddleOCR hard-failed with 422
instead of degrading to Tesseract like ocr-pdf effectively does. Broadens the
retry condition to also catch PaddleOCR-engine-unusable messages.
Note: ocr-pdf's tesseract-only behavior turned out to be an unrelated,
pre-existing, deliberate design choice (PaddleOCR segfaults on rasterized PDF
pages on arm64), not a graceful-fallback mechanism to copy -- the two tools
weren't actually solving the same problem, so this fixes ocr.ts's own gap
rather than trying to mirror ocr-pdf.
* fix: remove all automatic third-party egress (OSM tiles, Scalar fonts, editor Google Fonts, AI model download fallbacks)
Phone-home audit follow-up. The product no longer makes any automatic
third-party request; user-initiated click-outs stay, and production now
fails closed on missing AI models.
1. GPS leak via OSM tiles: the strip-metadata panel auto-loaded
tile.openstreetmap.org tiles encoding the photo's GPS position. The
Leaflet mini-map is gone; coordinates render as text plus an explicit
View on map link (openstreetmap.org, opens on click only). Removed
tile.openstreetmap.org from the CSP img-src, dropped the leaflet
dependency, added the viewOnMap i18n key to all 21 locales.
2. Scalar docs fonts: /api/docs loaded Inter and JetBrains Mono from
fonts.scalar.com. Scalar now renders with withDefaultFonts: false and
both --scalar-font and --scalar-font-code pinned to system stacks;
fonts.scalar.com removed from the docs CSP font-src. Verified by
injecting GET /api/docs/: config carries withDefaultFonts false and
the served page has no fonts.scalar.com reference.
3. Editor Google Fonts: the editor font picker built
fonts.googleapis.com stylesheet URLs for 25 web fonts the served CSP
already blocked. The remote loading path is deleted; the picker now
offers system fonts only, with a SELF_HOSTED_FONTS seam (FontFace API,
same origin) for bundling fonts later. Unknown families saved in old
documents fall back to the browser default.
4. Python sidecar fails closed on model downloads: new
packages/ai/python/offline_guard.py gates every runtime download
fallback (inpaint, outpaint, restore, noise_removal, detect_faces,
enhance_faces, face_landmarks, red_eye_removal, remove_bg, ocr,
transcribe, upscale) behind SNAPOTTER_ALLOW_MODEL_DOWNLOAD=1 with an
actionable error. Bundled models keep working untouched.
5. OCR and transcription library-internal downloads: unbundled PaddleOCR
language and detection fallbacks now raise the guard error naming the
language instead of resolving models over the network; faster-whisper
gets local_files_only when downloads are off.
6. GFPGAN and CodeFormer cwd-relative weights: facexlib and
codeformer-pip resolve helper weights relative to the process cwd and
fetch them from GitHub when absent. They are now symlinked from the
installed bundle files under MODELS_PATH/gfpgan/facelib before the
libraries load, failing closed when unresolvable.
Defense in depth: HF_HUB_OFFLINE=1 and TRANSFORMERS_OFFLINE=1 are set in
the runtime image and in the sidecar spawn env; install_feature.py lifts
them for user-initiated bundle installs and restores them afterwards
(it can run in-process inside the dispatcher). SNAPOTTER_ALLOW_MODEL_DOWNLOAD
is documented in .env.example, default off.
Validation: typecheck 9/9 workspaces, Biome clean on touched files,
5178 unit tests pass, py_compile on all touched scripts, guard behavior
exercised in both dispatcher exec and per-request import modes, zero
remaining runtime references to the three hosts. Docker build and live
AI inference need post-merge verification on the GPU host.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
* fix: allow AI model downloads by default, make strict offline mode opt-in
Product call: ease of use first. The download gating from the previous
commit inverts its default: runtime model fetches (public model weights
only, never user data) are allowed out of the box so AI tools self-heal,
and SNAPOTTER_ALLOW_MODEL_DOWNLOAD=0 becomes the explicit strict offline
mode for airgapped deployments, where every fallback raises the
actionable error instead of fetching.
Changes: offline_guard blocks only on an explicit 0/false; the
unconditional HF_HUB_OFFLINE/TRANSFORMERS_OFFLINE image ENV is removed
and bridge.ts sets those flags for the sidecar only in strict mode;
.env.example documents the new default; install_feature's lift/restore
stays. All bundled-path preferences, pre-existence checks, and symlink
pre-placement remain, so installed bundles never trigger a download.
The OSM, Scalar font, and editor font fixes are unchanged.
Validation rerun: typecheck 9/9, Biome clean on touched files, 5178
unit tests pass, py_compile on touched scripts, guard behavior verified
for unset/1 (allowed) and 0/false (blocked with the new message).
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
Fixes found by manually testing a fresh install end to end:
- auth: the must-change-password gate returned 403 on public routes
including /api/v1/health, so every fresh install showed a false
"Reconnecting to server" banner on the forced password change
screen. Public routes are now exempt (they need no session at all).
Adds the gate's first direct tests.
- multipart: @fastify/multipart's parts() iterator (9.4.0 and 10.0.0)
ends on the request stream's "close", which on a reused keep-alive
connection fires while an earlier part is still streaming to storage,
silently dropping the parts behind it. The object eraser lost its
mask file on every second POST per connection. Replaced with a
busboy-driven iterator (lib/multipart-parts.ts) that ends on busboy's
own "finish", installed for all routes via a preValidation hook;
the tool-factory field-recovery workaround for the same bug is now
unnecessary and removed.
- eraser: the mask canvas backing store is natural resolution, but
"absolute inset-0" does not stretch replaced elements, so the
canvas rendered at intrinsic size and the brush ring, strokes, and
exported mask were all misscaled on photos larger than the viewport.
The canvas now gets an explicit CSS box at the fitted size.
- compare slider: solid white divider with a dark halo so it stays
visible over light images; still initialised at the painted region.
- tool page: the AI bundle install prompt now centers in the content
area instead of hugging the top.
- api docs: disabled Scalar's cloud features (Ask AI, Generate MCP,
Open API Client, dev toolbar), hid the "Powered by Scalar" footer
link, and set the page title to "SnapOtter API Reference". The docs
CSP blocks those cloud calls by design, so the buttons were dead UI.
- docker: embedded Redis comes from packages.redis.io pinned to the
8.x major (was Debian's 7.0.15), matching the Compose stack and the
documented claim. Build fails fast if the major ever drifts.
- docs: DOCKERHUB.md quick start now leads with the one-command docker
run (matching the README) with Compose as the production path;
README says embedded Postgres 17 + Redis 8.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
* fix(api): correct format/filename/container handling across tool routes
Found during a comprehensive QA sweep exercising every tool against its
full accepted-format matrix:
- watermark-image, compose: preserve the requested output format and a
matching download filename/extension instead of always emitting the
source format
- compose: crop oversized overlays to the visible base area instead of
crashing Sharp's composite, and reject only overlays fully outside the
base image instead of any oversized one
- compare, vectorize: switch to the shared image input handler so
filenames and formats like .svgz/.tga/RAW survive validation instead
of being rejected pre-processing
- tool-factory, images-to-video: normalize frames through Sharp before
handing them to FFmpeg, fixing GIF/AVIF/RAW image-to-video jobs that
previously failed or hung
- media-tool, replace-audio, embed-subtitles: fix legacy container
MIME/codec handling for MPEG sources and subtitle remux cases
- files: expand download MIME mapping for text/data/document/video/audio
outputs that were falling back to a generic content type
- convert-document/presentation/spreadsheet: same-format conversions now
return the original validated file instead of erroring or producing
corrupt tiny output
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(web): dropzone a11y, stale localStorage getter, dead code
- dropzone: stop making the whole drop-zone section clickable/focusable.
A section acting as an interactive element around a real upload button
is a nested-interactive-element anti-pattern that confuses screen
readers; drag-and-drop doesn't need focus semantics, only the button
fallback does. Keeps that button semantic and keyboard-reachable.
Updates the two e2e call sites that clicked the section directly.
- api, use-auth: read through window.localStorage via the existing API
storage helper instead of the bare global, which resolves to Node's
experimental localStorage getter under Vitest and threw
- find-duplicates-settings, info-settings, login-page: remove dead code
(unused zip-download handler, a stale mount-only effect dependency
that left cached info stuck at reused indices, an unused response
variable)
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(i18n): pt-BR, zh-CN, zh-TW were silently falling back to English
The locale loader looked up dynamic-import exports by the raw locale
code (mod["pt-BR"], mod["zh-CN"], mod["zh-TW"]), but those three modules
export camelCased bindings (ptBR, zhCN, zhTW) since identifiers can't
contain hyphens. The lookup returned undefined and every consumer
silently fell back to English for these three locales. Replaces the
generic lookup with explicit per-locale loaders so the mapping can't
drift out of sync again.
Also updates the dropzone helper copy across all 21 locales to match
the drag-only dropzone wording from the previous commit.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(docs): clear build warnings in the VitePress site
- config.mts: add an onwarn handler for the @vueuse INVALID_ANNOTATION
warnings emitted during the docs build
- deployment.md: the caddyfile code fence language isn't a shiki grammar
VitePress ships with, so it warned on every build; use txt instead
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* test(qa): update QA harness for the drag-only dropzone and regen metadata
- api-sweep, qa-helpers, verify-ai: add JSON-body tools, multi-input
secondary fixtures, async polling for slow valid jobs, 501
FEATURE_NOT_INSTALLED skip handling, and safer per-tool settings
- input-preview, pipeline-ui specs: update upload flow for the
drag-only dropzone surface
- add tests/fixtures/data/valid/chart.json, a valid chart fixture the
updated helpers route to
- regenerate tools-meta.json against current TOOLS[]
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(security): close a login timing side-channel, harden zip-slip tests
Found during a black-box security sweep of the real auth-enabled
production container: a nonexistent username returned 401 in ~3-10ms,
while a wrong password for a real user took ~35-42ms, because scrypt
verification only ran when a user row existed. That timing gap lets an
attacker enumerate valid usernames without ever guessing a password.
Now runs verification against a cached dummy hash on the unknown-user
path too, so both cases cost the same regardless of outcome.
extract-zip already had a relative-traversal regression test
(../evil.txt), but its absolute-path rejection branches
(name.startsWith("/") / startsWith("\\")) had none. Added the three
missing cases: deep relative traversal, absolute Unix path, and
Windows-style absolute path.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* test(qa): add UI-driven AI bundle install scripts
QA_PROMPT.md's Phase 2 requires installing AI models the way a user
does -- through the UI, on demand from HuggingFace -- and treats the
curl-based admin install endpoint as fallback-only. Nothing in the
harness actually drove that flow; tests/qa/seed-ai-models.sh installs
via docker exec + pip, which is further from a real user than even the
API fallback.
install-ai-bundles-ui.mts logs in, opens Settings > AI Features,
screenshots the pre-install state, clicks Install All, and screenshots
progress -- then exits, since installs continue server-side once
triggered. verify-ai-install-complete.mts polls bundle status,
screenshots the completed state, and runs one real tool per installed
bundle to prove the freshly-downloaded model actually executes.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(qa): correct the apiToolPath import in the AI verify script
Dynamic import of the package name failed under tsx's module resolution
from apps/api's node_modules context; use the same relative-path import
api-sweep.mts already uses successfully.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(web): correct AI bundle size estimates shown before install
Measured real downloads during GPU-node QA verification: photo-restoration
pulls ~4.4GB (was advertised as 800MB-1GB, off by 4-5x) and ocr pulls
~5.5GB (was advertised as 3-4GB). Both estimates only accounted for model
weights, not the pip dependencies (torch/paddle) that come down with them.
Updated to reflect actual total download size, since that's what a user
deciding whether they have the disk/bandwidth actually needs to know.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(web): make desktop Settings reachable when auth is disabled
AvatarDropdown (the only desktop entry point to Settings) was gated
behind `!isMobile && authEnabled`. With AUTH_ENABLED=false the synthetic
anonymous admin user should have full Settings access per how auth.ts
documents this mode -- and the mobile bottom nav already worked this way,
showing Settings unconditionally. Desktop just had a stray extra gate the
component doesn't need: AvatarDropdown already resolves its own username
internally (falling back to "admin") and reads authEnabled itself where
it actually matters (hiding the Logout button). Removed the outer gate;
verified end-to-end against a fresh AUTH_ENABLED=false instance -- avatar
now renders, Settings opens, shows the anonymous/Admin identity correctly.
Also documents (not changes) a related finding in install_feature.py:
detect_arch() always resolves amd64 hosts to the GPU-bundled archive
variant regardless of actual GPU presence, since no CPU-only amd64
archive is published to the bundle repo yet. Left as a code comment
rather than a behavior change, since requesting an unpublished archive
key would hard-fail installs entirely -- worse than the current
oversized-but-working download. Full detail in the QA report.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(ai): stop logging expected dispatcher reloads as crashes
After each AI bundle install the Python dispatcher reloads because the
venv changed, and after every app shutdown it's SIGTERMed. Both took the
close handler's `code !== 0` branch (SIGTERM makes the exit code null),
so they were counted as crashes -- producing an alarming "crash" line in
the logs and a pointless ~1s recovery backoff after each of 7 installs.
A `stopping` flag set in shutdown() lets the close handler tell an
intentional stop apart from a real crash. The request-timeout kill path
deliberately does not set it, so a genuinely hung script still records a
crash and the 5-in-60s permanent-disable threshold is untouched.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(api): return a clean message when content-aware resize times out
Carving a very high-resolution image down to a tiny target could exceed
the caire subprocess timeout, and the raw error forwarded to the user was
caire's terminal output -- ANSI color codes and progress-spinner control
characters -- instead of anything actionable. Now: the timeout path
throws a clear "timed out; try a smaller image or larger target" message
(keeping the raw stderr as `cause` for server logs); friendlyError()
strips ANSI/control chars centrally so any subprocess dump surfaced
through the shared sanitizer is plain text; and the content-aware-resize
route (a custom route that bypassed the sanitizer) now routes its error
paths through friendlyError like every other tool.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(ai): stop bundle installs from exhausting host disk
Installing an AI bundle on a tight-disk host could push the root
filesystem to zero bytes free after the preflight check had already
passed. Two root causes:
- move_tree used copytree+rmtree, so during the move the extracted
payload existed in both staging and the venv at once -- a full
transient doubling on disk. Rewrote it to rename entries (a cheap
metadata op on the same filesystem, no copy), falling back to a copy
only across filesystems.
- the preflight budget used the manifest's extractedSize verbatim, which
is 0 for several archives, collapsing the estimate to just the
compressed size. Added a conservative fallback (3x compressed) so a
missing value can't under-reserve.
Also added a real-on-disk re-check immediately before the first
destructive venv write (measuring the actual extracted payload and
whether the move needs extra space for a cross-filesystem copy), which
also now covers the offline-import path that previously skipped the disk
check entirely; wrapped the moves so an out-of-space failure returns a
clean actionable error instead of a traceback; and made the disk check
resolve the nearest existing ancestor so it never throws on a
not-yet-created venv path.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* feat(web): show the real per-arch AI bundle download size
The bundle cards and install prompt showed a hardcoded, architecture-blind
estimatedSize string. That's misleading: amd64 hosts always pull the
CUDA-inclusive archive (there's no CPU-only amd64 variant published), so a
bundle labelled "1-2 GB" can actually download several times that, while
arm64 pulls a much smaller archive for the same label. The manifest
already carries the real per-arch compressedSize (and extractedSize where
measured), so surface those: a new optional downloadBytes/installedBytes
on FeatureBundleState, populated in getFeatureStates() for this host's
arch (resolver mirrors install_feature.py detect_arch), shown by the UI
when present with estimatedSize kept as the fallback label. Also nudged
upscale-enhance's fallback string (4-5 -> 5-6 GB) to match its real
compressed size, consistent with the earlier photo-restoration/ocr fixes.
Fields are optional so demo/mock and existing tests stay compiling; the
manifest's extractedSize is 0 for a few archives, which now surfaces as
null rather than a bogus 0.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(web): move the AI install queue to the server so it survives tab close
Installing multiple bundles could silently lose all but the first. The
server rejected a concurrent install with 409, so the client worked
around it by queueing the rest in browser-local state and only POSTing
each once it saw the previous finish. A single POSTed install is durable
(the installer child is detached from the request), but a queued one had
zero server footprint -- close the tab mid-queue and those installs
vanished with no error, while the UI still showed them "Queued". The
client "mutex" didn't even serialize: the queued bundles' local waits all
resolved at once and raced into concurrent POSTs that 409'd each other.
Now the queue lives on the server (a small in-memory FIFO leaf module).
The install endpoint enqueues instead of 409-ing and returns
202 {jobId, queued}; a pump starts the next bundle when the current one's
child exits (and after an offline import releases the lock), all behind
the existing venv + file locks, which are unchanged. The client just
POSTs every bundle immediately and reflects the server-reported
queued/installing status; Install All fires all POSTs and lets the server
serialize them, keeping the one-shot retry-on-failure. Adds "queued" to
FeatureStatus (the bundle card already rendered that state) and surfaces
it from getFeatureStates. In-memory is deliberate: it matches the
existing contract (survives a tab close, not a server restart, which
already clears the lock on boot).
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
* fix(qa): don't log env-derived credentials in the AI-install script
CodeQL flagged clear-text logging of sensitive information: the login
status line interpolated the QA base URL and username (both read from
the process environment) into a console.log. Replaced with a static
message. QA helper only, but it's a real hygiene issue and cleared the
high-severity code-scanning alert on the PR.
Claude-Session: https://claude.ai/code/session_019fpSXhLGLXWwfyZY2tWhLG
AI feature installs now keep copied Python venv metadata (bin/pip shebang,
bin/activate, pyvenv.cfg) pointed at /data/ai/venv, so scripts no longer
silently fall back to the baked, read-only /opt/venv after the venv is
bootstrapped into /data. Fixes#127 (AI tools incompatible with PUID/PGID).
The entrypoint repairs both fresh bootstraps and already-stamped runtime
venvs (self-heals existing deployments on next restart, no reinstall
needed), with regression coverage for literal path replacement and binary
file safety.
Independently reviewed and verified: traced chown/gosu ordering in
entrypoint.sh to confirm no permission regression, reproduced the exact
issue #127 scenario (custom PUID + manual venv activation) in a live
container both before and after the fix, and ran the PR's own test suite
locally (16/16 passing).
Co-authored-by: SyntaxSawdust
Adds a prefilled 'Request a tool' affordance to the home search empty state and beneath weak results. Opens the in-app feedback dialog with a new search_miss source and a structured search_query when analytics is on; links to a prefilled GitHub Discussions (Ideas) post when off, so a request is never silently dropped. Reuses the existing feedback pipe, dialog, and analytics gate; no new storage. i18n across all 21 locales.
* fix(landing): correct PDF tool count to 28 in alternatives copy
The pdf section has 28 tools (section.test.ts asserts bySection('pdf')=28). PR #363 corrected the docs breakdown but the alternatives pages still said 40 PDF tools (the document-modality count, not the pdf section) with 200 for the rest. Update to 28 PDF tools and 212 for the non-PDF remainder.
* chore: use "200+ tools" for the tool-count claim across public surfaces
Replaces the exact '240 tools' count (which drifts as tools are added) with the stable '200+ tools' on README, the Docker Hub overview, landing pages, the docs site (meta, homepage, search), the API self-description, the demo OG tag, llms.txt, package.json, the branding readme, and the en/nl app strings. Per-modality breakdown tables stay exact. Leaves the architecture doc's technical 'tool routes' figure, an internal vitest comment, and a QA report line unchanged. Updates the two tests that assert the docs strings.
Lands five integrated branches: pipeline templates (#355), analytics opt-out (#354), 83 conversion presets bringing the catalog to 240 tools (#356), self-hosted positioning (#353), and e2e modernization (#351).
Integration fixes: aligned stale web analytics tests with the opt-out/allow-list model, closed 3 CodeQL incomplete-sanitization alerts in the i18n generator, resolved settings/index/docs/format-matrix conflicts, and corrected tool counts to 240.
The entrypoint only fixed volume permissions when started as root (chown +
gosu-drop to snapotter). Launched under a non-root/foreign UID (TrueNAS app
user, Kubernetes runAsUser, OpenShift) it did no permission setup, so /data and
/tmp/workspace -- owned by uid 999 from the image -- were not writable by the
running user. Uploads and processing then failed with a cryptic EACCES
("workspace folder is not writable") and AI bundle installs failed the same way,
while health checks still reported the container healthy.
- entrypoint: source new entrypoint-lib.sh; verify writability up front when
non-root, and as snapotter after chown when root (catches root-squashed
mounts), failing fast with an actionable message (which dir, uid/gid, how to
fix) instead of a late, cryptic EACCES
- Dockerfile: own /data and /tmp/workspace as snapotter:0, group-writable with
setgid, so an arbitrary UID with the root supplementary group (OpenShift /
Kubernetes fsGroup) can write; keep /opt/venv world-readable for the AI venv
bootstrap under arbitrary UIDs
- api: assert storage writability at boot (lib/storage-writable.ts), failing
fast with the same guidance even when the entrypoint is bypassed
- docs: add a Storage permissions section (named volumes, bind mounts, TrueNAS,
Kubernetes/OpenShift) and cross-link it from the security guide
Fixes#230
RAW (DNG) processing crashed on ImageMagick's deprecated ufraw-batch
delegate, which fails on modern formats such as iPhone ProRAW DNG.
Root cause: the dcraw_emu (LibRaw) decode tier read the wrong output path.
dcraw_emu APPENDS the output extension (raw-in-X.dng -> raw-in-X.dng.tiff)
but the code looked for raw-in-X.tiff (replaced extension), so readFile threw
on every RAW, the tier silently fell through to ufraw, and the 24MB TIFF
leaked into the temp dir on each attempt.
- Repair the dcraw_emu output path; clean it up in finally (fixes the leak)
- Prefer LibRaw full decode over embedded-preview extraction so a
full-resolution RAW is never silently returned as a reduced-size preview
(sample DNG: was 1024x683 preview, now 3474x2314 full)
- Add RAW decode regression tests (DNG full-resolution + all 6 RAW formats);
these were absent, which let the bug ship
- Install libraw-bin on CI test runners so dcraw_emu is actually exercised
Replace textual prefix matching in isPrivateIPv6() with numeric bit-prefix matching via ipaddr.js. Closes reachable classifier bypasses for IPv4-compatible IPv6 (::a.b.c.d), site-local fec0::/10, and the full fe80::/10 link-local span, on top of the IPv4-mapped forms. Embedded IPv4 (mapped and compatible) is run through the full isPrivateIPv4 classifier; unparseable input fails closed.
Follow-up hardening to f64cbdda.
Reported-by: tonghuaroot
Fixes a backlog of integration/unit specs that asserted pre-2.0 behavior and
were failing CI (not caused by recent feature work):
- modality-aware empty-input error is 'No file(s) provided', not /no image/i
(rotate, border, crop, resize, smart-crop, edge-cases, adversarial-extended,
api, tool-factory-route)
- input validation rejects pre-enqueue with a clean 400 in 'error' (was a worker
422 in 'details'): create-zip, extract-zip, merge-csvs
- resolveToolPool defaults unknown tools to the system pool (pool-routing)
- /upload and fetch-urls accept non-image content, validated per-tool at process
time (api, fetch-urls)
- color-adjust legacy aliases were consolidated into adjust-colors: drop the
removed-alias tests; retarget the format-preservation tests
- xml-to-csv gracefully converts a single non-repeating record to a 1-row CSV
- dropzone is multimodal; image-only filtering is opt-in via fileFilter
- factory-multi-input: register the synthetic test tools in the catalog so they
route correctly (file modality for concat; image for the validation-prefix test)
Verified locally: unit 4546 passed, integration 8332 passed, typecheck + lint green.
The full unit suite surfaced two issues from earlier commits on this branch. (1) Importing the app logger into media-input.ts pulled logger.ts -- which builds its pino file transport at module load via join(env.LOG_DIR, ...) -- into the unit-test import graph, throwing at collection time wherever LOG_DIR is unset (integration tests set it; unit tests do not). A low-level modality handler should not depend on the app logger, and a corrupt upload is an expected user error, so drop the import and keep the clean validation message. (2) tool-factory-route.test.ts mocked errors.js without the new friendlyError export; add it.
* docs: rebrand from image-only to multi-modality across docs and metadata
SnapOtter expanded from image-only to 157 tools across 5 modalities
(image, video, audio, document/PDF, data). Update all product-level
copy, metadata, and i18n that still framed it as an image-only tool.
- README, package.json, root llms.txt: multi-modality framing, 157 tools
- OpenAPI info + tags, generated /llms.txt tagline (docs.ts)
- VitePress docs site: hero, getting-started, architecture, security,
deployment, configuration, developer, supported-formats
- i18n: 10 product keys across all 21 locales (hero, app description,
privacy notes, AI features, progress messages, getting-started)
- web/demo/landing meta + privacy copy, COMMUNITY_GUIDE, .env.example
Stale tool counts (53/50+/52/70+/35) corrected to 157 throughout.
Database/container deployment claims left unchanged (out of scope).
* docs: fix stale post-rebrand test assertions and README language list
- tests/e2e-docs/homepage.spec.ts: assert the current docs homepage (file toolkit, 157 tools, 5 modalities) instead of the old image-only strings
- tests/unit/api/docs-route.test.ts: sync the reproduced llms.txt tagline with docs.ts
- README.md: 21 languages with the correct list (add Swedish and Chinese Traditional, drop Czech which is not supported)
* docs: correct 2.0 architecture references (Postgres 17 + Redis 8, 3-container stack)
The docs and metadata still described the 1.x stack (SQLite, single container, p-queue). Update them to the current 2.0 reality.
- README: replace the broken single-container `docker run` quick-start with the real Docker Compose stack (app + Postgres 17 + Redis 8); fix the "no Redis, no Postgres" feature bullet
- package.json: description no longer claims a single container
- apps/docs: rewrite database.md for Postgres; configuration.md DB_PATH -> DATABASE_URL + REDIS_URL; architecture.md SQLite/p-queue/better-sqlite3 -> Postgres/BullMQ/pg and add media-engine + doc-engine; developer/security/deployment/docker-tags/getting-started/contributing compose examples now include postgres + redis; index.md + api/ai.md AI count 16 -> 19
- SECURITY.md: Drizzle (SQLite) -> (PostgreSQL)
- landing: enterprise/FeatureHighlights single-container wording; TrustSignals/ToolGrid 150+ -> 157 (dynamic); Pricing/FAQ 15 -> 19 AI tools
* docs(api): document all video, audio, document, and data tool endpoints in OpenAPI
The spec covered only image tools; the Scalar UI and the generated /llms.txt and /llms-full.txt inherited that gap. Add the 104 missing tool endpoints so the API docs match the code.
- Video: 29 endpoints (most long/async; auto-subtitles is AI)
- Audio: 17 (transcribe-audio is AI)
- Document/PDF: 36 (ocr-pdf is AI; conversions are long/async)
- Data: 10
- Image: 12 newer tools (background-replace, blur-background AI; histogram/lqip-placeholder/sprite-sheet custom responses; barcode-generate uses a JSON body)
Each schema is derived from the tool's Zod validator and executionHint (fast -> 200, long -> 202+SSE, AI adds 501 FeatureNotInstalledError, multi-file inputs as arrays), referencing the existing shared schemas. Tool path entries: 64 -> 168. Spec parses as valid YAML with no duplicate paths and only known $refs.
SnapOtter spans five modalities now, but several code paths still assumed image input.
- dropzone: default to accept-all when no fileFilter is given (image tools still pass one); neutral "supported file types" error text instead of "image files"
- automate (pipelines): accept any modality in the file pickers and dropzones; render modality-aware previews (video player, audio waveform, document/data card) instead of always using ImageViewer/BeforeAfterSlider
- filename sanitizer: extend the double-extension allowlist beyond image extensions to video/audio/document/data so e.g. "report.csv.php" becomes "report.csv"; add tests
- thumbnail route: return 422 for non-rasterisable files (audio, data, non-PDF docs) instead of attempting a doomed Sharp decode
- pool: unknown tools fall back to the "system" pool, not the image pool
- a11y labels: "Previous/Next image", "Image viewer/area/controls/drop zone" are now modality-neutral, across all 21 locales
- copy: bulk-rename default, find-duplicates ZIP name, SSRF user-agent, fetch-urls fallback name, file-details MIME label, URL-import placeholder, help dialog
* feat(tracing): add OpenTelemetry dependencies and --import preload flag
* feat(enterprise): add distributed_tracing feature gate
* feat(tracing): add SDK bootstrap with enterprise gating
* fix(tracing): correct test coverage for enterprise-unavailable path and prevent double-init
Test 2 now mocks @snapotter/enterprise to throw an import error, exercising
the catch block in the preload. Test 3 imports with no endpoint so the preload
is a no-op, avoiding leaked SDK from double-initialization. Added idempotency
guard to initTracing() as a safety net.
* feat(tracing): add Pino trace mixin and shared logger
When OTel tracing is active, every Pino log line now includes traceId,
spanId, and traceFlags fields for log-to-trace correlation. The mixin
is a no-op when no SDK is registered (community users).
* feat(tracing): add _otel to ToolJobData and inject trace context at enqueue
Add optional _otel carrier field to ToolJobData for W3C trace context
propagation across BullMQ job boundaries. When an active OTel span exists,
propagation.inject() writes traceparent/tracestate into the job data before
queue.add(). When no SDK is registered (community edition), the carrier
stays empty and _otel remains undefined -- zero overhead.
* feat(tracing): extract trace context and create spans in BullMQ worker
* feat(tracing): inject trace context into Python sidecar calls
* feat(tracing): add trace context extraction to Python sidecar
* feat(tracing): add shutdownTracing to graceful shutdown sequence
* feat(tracing): enrich HTTP spans with tool_id and user_id attributes
* docs: add OpenTelemetry env var documentation to .env.example
* test(tracing): add lifecycle integration tests for trace propagation
* fix(tracing): inject trace context into pipeline and batch flow jobs
* fix(tracing): add sidecar.execute Node-side span and remove unnecessary comment
Wraps PythonDispatcher.run() with a sidecar.execute span on the Node
side so traces show the full round-trip (Node span -> Python span).
Also removes an obvious comment from logger.ts.
Move user resolution logic (match by externalId, auto-link by email,
auto-create with user limit check) into a shared module that both
OIDC and SAML callbacks can use. Includes sanitizeUsername and
findUniqueUsername helpers. Preserves all existing OIDC behavior
and audit events.
