Route desktop cloud-key saves/status through the OS credential store (keyring with per-platform native backends: apple-native / windows-native / sync-secret-service), migrate the legacy plaintext ~/.openjarvis/cloud-keys.env into it, remove browser localStorage persistence of provider keys, and push key updates to the running server via /v1/cloud/reload (legacy env-file fallback retained). Remove hardcoded Supabase anon JWTs from frontend/docs source and make VITE_SUPABASE_ANON_KEY a required build var. Adds libdbus-1-dev to the Linux desktop build and a CI build var. Closes#220.
NOTE (post-merge follow-ups, not covered by CI): add the VITE_SUPABASE_ANON_KEY repo secret with the rotated key (release/docs builds otherwise use a placeholder), rotate the previously-committed Supabase anon key, and run a desktop save->restart->read smoke test to confirm keychain persistence.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Pairs with the Supabase migration that added `methodology_version` to
`savings_entries` and demoted 43 corrupt rows (pre-fix telemetry from
March 2026) to version 0. Adds `methodology_version=gte.1` to the
public leaderboard fetch URL so the quarantined rows hide at the query
layer — fewer bytes over the wire than client-side outlier filtering,
and forward-compatible: new clients write `methodology_version >= 1`
and remain visible.
The leaderboard's existing client-side outlier thresholds stay in place
as a second line of defence in case any future v >= 1 row slips past
the bounds.
Smoke-tested live: anon-key fetch against
`mtbtgpwzrbostweaanpr.supabase.co` returns 291 rows (was 334), top
result is the largest-savings user with v=1. A separate `eq.0` probe
confirms quarantined rows are skipped only by the filter (no RLS
policy in place yet — flag for follow-up if you want hard isolation).
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
* fix(leaderboard): correct telemetry pipeline and outlier handling
The public leaderboard at /leaderboard showed a clear bimodal Wh/token
distribution: most users at ~3-5 J/token, ~30% inflated by 1000-4000×.
A 5-agent investigation workflow + 4-agent verification pass traced the
inflation to a cluster of related bugs across telemetry, server, and
display layers. This PR fixes the in-repo half. Backfilling existing
Supabase data is a separate follow-up.
Bug 1 — Dual telemetry recording (`server/routes.py`)
=====================================================
`_handle_direct` wrapped the engine with `instrumented_generate`
unconditionally. When `app.state.engine` was already an
`InstrumentedEngine` (the common case when telemetry is wired in),
BOTH layers published `TELEMETRY_RECORD` — once from the inner
`InstrumentedEngine.generate`, once from the outer wrapper. Every
chat-completion request was counted twice in the leaderboard pipeline.
Fix: detect the InstrumentedEngine and unwrap to `._inner` before
passing to the wrapper so only one layer fires.
Bug 2 — KV-cache fallback over-counts multi-turn (`server/savings.py`)
=====================================================================
`compute_savings` falls back from `prompt_tokens_evaluated` to
`prompt_tokens` when the KV-cache-aware count is missing. But routes.py
aggregates by summing each turn's full prompt — which counts the system
prompt N times for an N-turn conversation. The fallback inflated FLOPs
and energy by N×.
Fix: use 0 (conservative under-count) when the evaluated count is
missing rather than falling back to the inflated `prompt_tokens` sum.
Bug 3 — TelemetryRecord lacked methodology versioning
=====================================================
There was no per-record version tag, so legacy (pre-fix) and current
records were silently aggregated together in the public leaderboard
even though they used different methodologies.
Fix: add `token_counting_version: Optional[int]` field to the
`TelemetryRecord` dataclass + a nullable column to the SQLite schema
(with idempotent migration); the constant moves from `server.savings`
to `core.types` to avoid the server→telemetry layering. New records
write the current version; pre-fix rows remain NULL. The aggregator
gains a `current_methodology_only=True` flag that filters NULL rows out
of leaderboard sums — local dashboards leave it off so historical
aggregates still render.
Bug 4 — Leaderboard JS displayed missing telemetry as legit zeros
=================================================================
Rows with significant token counts but `energy_wh_saved = 0` and
`flops_saved = 0` rendered as `0.00 Wh / 0 FLOPs` — visually identical
to a user who genuinely did almost nothing. The headline totals also
included these rows.
Fix: new `isMissingTelemetry()` detector renders missing-telemetry
energy/FLOPs cells as `—` (with a tooltip explaining why); a new
`.lb-missing` CSS class differentiates the placeholder visually.
Bug 5 — Outlier filter was too generous
=======================================
`MAX_ENERGY_WH_PER_TOKEN = 10` left a 10,000× margin that admitted
every Group B row even though those values are physically impossible
(would imply a space-heater per token). Same for the FLOPs cap at
`1e17`.
Fix: tighten to `0.5` Wh/token (still 500× over a typical consumer GPU)
and `1e15` FLOPs/token (still 10,000× over typical). This removes
existing pre-fix corrupt rows from the public view without touching
Supabase.
Tests
=====
New regression tests (all pass, ruff clean):
- `tests/server/test_routes.py::test_instrumented_engine_unwrapped_to_avoid_dual_telemetry`
— pins the bug 1 fix; asserts exactly ONE TELEMETRY_RECORD event per
request when the engine is already an InstrumentedEngine.
- `tests/server/test_savings.py` (NEW file, 3 tests) — pins the bug 2
fix (FLOPs not inflated via fallback) and the cost-side invariant
(dollar savings still use full prompt_tokens because cloud providers
bill per input token even when local KV cache hit).
- `tests/telemetry/test_aggregator.py::TestMethodologyFilter` (3 tests)
— default behaviour includes legacy rows (local dashboard parity);
`current_methodology_only=True` excludes them; the summary surface
honors the same filter.
Unrelated test note
===================
`tests/telemetry/test_energy_wiring.py::TestTelemetryStatsEnergy::test_export_includes_energy_fields`
and `::TestEndToEndPipeline::test_ask_to_export_with_energy` are flaky
on this branch but ALSO flaky on `main` (confirmed via stash + the
banner-only run). Root cause: `_version_check.py:132-135` prints the
"new version of OpenJarvis is available" banner to stdout outside the
throttle window, contaminating `json.loads(result.output)` in those two
tests. Reproducible with `OPENJARVIS_NO_UPDATE_CHECK=1` → all pass.
That's a separate bug (the banner shouldn't write to the same stream
as machine-readable output); not addressing it here to keep this PR
focused on the leaderboard pipeline.
What this PR explicitly does NOT do
====================================
- Backfill ~30% of Supabase rows that already shipped with inflated
values from pre-fix clients. That requires Supabase write access and
is the natural follow-up — see PR comments for proposed dry-run audit
query and the additive `methodology_version` column migration.
- Distinguish which past submissions came from buggy vs correct clients
retroactively (no `app_version` tag on existing submissions).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* test(evals): fix test_energy_scales_linearly under leaderboard fix
CI on #498 failed in the slow `test` job:
tests/evals/test_use_case_benchmarks.py::TestSavings::
test_energy_scales_linearly — ZeroDivisionError: float division by zero
Root cause: the test (and `test_energy_wh_matches_direct_formula`)
called `compute_savings(N, 0)` without `prompt_tokens_evaluated`. Under
the OLD buggy fallback, an unset evaluated count silently became N,
energy scaled linearly with N, and the test passed by accident. Under
this branch's conservative fix the fallback is 0, FLOPs collapse to 0,
and the `p10.energy_wh / p1.energy_wh` ratio divides 0 by 0.
The test's own docstring says "evaluated tokens (KV-cache model)" — it
was always meant to exercise the explicit-evaluated path, the API
call just didn't match. Pass `prompt_tokens_evaluated` explicitly so
the test now expresses the invariant it claims to.
Same one-line fix for `test_energy_wh_matches_direct_formula` plus an
explicit `flops > 0` sanity assertion — that test was passing trivially
with `0 == 0` after the conservative fallback fix, masking whether the
formula was actually being exercised.
No production code change in this commit. Verified locally: all 6
TestSavings tests pass; the 5 new regression tests added on this branch
still pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
---------
Co-authored-by: krypticmouse <herumbshandilya123@gmail.com>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Replace the recompute approach from #146 with per-token outlier
detection. Entries whose energy, FLOPs, or dollar savings exceed
generous per-token thresholds (~1000x legitimate values) are hidden
from the leaderboard entirely. All displayed values come directly
from the database — no rewriting of submitted data.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Energy and FLOPs are now derived from total_tokens on the leaderboard
display using Claude Opus 4.6 constants, rather than trusting submitted
DB values. This prevents gaming (e.g. TotallyNoire submitting 14B Wh
from 15M tokens). Dollar savings are clamped at the theoretical max
($25/1M tokens) on both the leaderboard and frontend submission side.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The energy_wh_saved and flops_saved values were orders of magnitude too
high because a scaling factor that grows linearly with N was applied to
the energy calculation, making it scale as O(N³) instead of O(N²).
Replace the buggy scale-factor approach with a direct FLOP-to-energy
conversion using each provider's per-token constants. Add regression
tests to prevent recurrence.
Closes#95
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
