The first environment shipped through .claude/workflows/new-environment.js:
specification, three adversarial reviews (all 'fixable', none fatal), the
Python environment, the TypeScript port, captured rollouts, and the demo page.
Eleven agents, no errors.
The proof that the platform scales is one line long. Alert Triage has a
completely different shape from Word Five — JSON actions, priced lookups, an
analyst screen instead of a grid — and the only change under
src/components/demo/ is a comment edit, because the isolation lint refused the
word "wordle" there. Zero shell code changed. 415 contract checks now pass
against two demos, up from 206 against one.
The environment is honest by construction. Every alert is synthetic, generated
from the seed, and the banner saying so sits inside the board surface. Two of
the eleven scenario templates are hidden-suspicious: generated by the same code
as their benign twin with the signal overlaid only in lookup data, so the free
screen is identically distributed and a screen-only policy STRUCTURALLY cannot
tell them apart. The probe ladder measures it: `fast` catches 0.0 of hidden
seeds. That is the counterweight made real rather than asserted.
Twelve policies, thirteen ladder assertions, a genuine three-way trade:
fast 0.846 wins hours (0.85), misses every hidden case
targeted 0.894 wins the shipped total
thorough 0.820 wins evidence (1.00), spends 2.9 hours
None dominates. 92 Python tests, 35 TypeScript tests, 65 fixtures replaying at
delta 0, and conformance gated on world + scorer + protocol so the browser shows
the same alert for ?seed= that Python generated.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019mt6sHQHEnEYrJZvoMCJSB
The site does no live inference. Rollouts are captured once against spark-1 and
replayed at their recorded wall-clock — a public demo with no auth cannot hold
an API key, and a recorded run can be scrubbed, permalinked, blind-compared and
verified in ways a live one cannot. What stops it being a video is that the
browser re-derives every number from the recorded moves.
verify_fixtures.py is the Python half of that: it replays every committed
fixture through the engine and reproduces its own rewards. All 16 land at
delta 0.0. A fixture that cannot be regenerated is a claim with no receipt.
First real measurement, thinking off, 8 seeds: solved 0/8. The model repeats
guesses it has already played, invents words (trape, slith, postt, boomy),
and contradicts its own feedback — consistency 0.09 to 0.17. That is the
published failure taxonomy showing up in our own data on the first run, and it
is why `consistency` is a reward component rather than a footnote.
A capture failure is recorded as a turn with a null reply, never dropped. A
capture that silently discarded failed turns would be reporting a better model
than the one that ran.
CI gates both halves and four things that fail silently in production: the word
lists must rebuild byte-identically, the prerendered routes must carry their own
baked og tags (crawlers do not run JS, so without them every shared link
previews as the homepage), no blob: URL may reach the bundle (the site's CSP has
no worker-src, so it falls back to default-src 'self' and a blob worker is
blocked with no error), and the conformance digest must match across languages.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019mt6sHQHEnEYrJZvoMCJSB
karti-ai