▚ TELEMETRY
Parallax Telemetry measures the structure of a model's output — how it's built, not whether a grader liked it. The kernel is deterministic: same input, same output, every coordinate backed by countable evidence. No judge model. No vibes. No variance.
Three operations, the three things an eval actually needs to answer. Paste text — it's measured locally on the server, deterministically.
Where does this output sit in structural space, and what evidence supports it?
Did my prompt change really improve the output — or just reformat it? This is the question A/B prompt testing usually can't answer.
Fit a neutral envelope from a few representative "good" outputs, then test whether a new output has drifted outside your model's normal structure. This is the CI hook.
Every incumbent eval tool runs on an LLM-as-judge: a second model grades the first. That's expensive, non-deterministic, and circular. Parallax Telemetry is a different category of instrument.
It does not judge whether an answer is correct, safe, or persuasive — and never claims to. It quantifies how the output is structured, so prompts, models, and multi-turn workflows can be compared on signal instead of vibes. Two correct answers can land in very different coordinates. That's the point.
Measured across Claude, GPT and Gemini on the same prompts, with no judge model. Read the Linguistic Kernel results or the full cross-engine study.
A plain JSON API. No SDK required — it's curl-able. The same calls the playground above makes.
One output → integrity verdict, 8 structural coordinates, basin label, reasoning signals, and the evidence counts behind them.
curl -s localhost:7434/v1/score \
-H 'content-type: application/json' \
-d '{"text":"your model output"}'
Two outputs → real_change or reformat_only. The novelty gate catches reformatting that masquerades as improvement.
curl -s localhost:7434/v1/compare \
-H 'content-type: application/json' \
-d '{"a":"baseline","b":"revision"}'
Fit a neutral envelope from your good outputs, then band any new output: stable / elevated / breach.
Integrity and drift are independent axes. Every result carries both: drift says how far the output moved from your normal; integrity says whether it structurally broke (collapsed / unreliable / low_confidence / sound). A collapsed output fails the CI gate even when it lands inside the envelope — a truncated answer that drifts nowhere is still a broken answer.
curl -s localhost:7434/v1/baseline \
-d '{"texts":["...","...","..."]}'
# → envelope, then:
curl -s localhost:7434/v1/monitor \
-d '{"text":"...","envelope":{...}}'
The point of a deterministic score is that you can assert on it. Drop the CLI into CI and gate merges when a prompt or model change pushes outputs out of envelope — no flaky judge model deciding your pipeline.
$ python3 cli.py baseline --from golden_outputs.txt --save envelope.json envelope saved to envelope.json (kernel lang-kernel-rebuild-v19, n=8, r95=0.27221) $ python3 cli.py monitor --envelope envelope.json --text "$(cat candidate.txt)" --fail-on breach band: breach drift_ratio: 3.688 integrity: sound top driver: void_ratio (rᵥ) -0.62 FAIL: band 'breach' >= threshold 'breach'. $ echo $? 1 # non-zero exit → CI fails the merge
# .github/workflows/eval.yml — envelope.json is committed, like a lockfile
- name: Structural drift gate
run: python3 products/parallax-telemetry/cli.py monitor \
--envelope tests/envelope.json \
--text "$(python3 run_model.py < tests/prompt.txt)" \
--fail-on breach
These numbers are real, not illustrative: the candidate is the same rambling cache answer you can run in the playground and the API console above, measured under lang-kernel-rebuild-v19 and pinned by the product's own test suite. A collapsed integrity verdict fails the gate even when drift is inside the envelope.