Eighteen agents: three design directions judged on whether a COO on a phone
actually learns what an environment is, on craft, and on landing without a
rewrite; one spec; a foundation of measured tokens; six build lanes; three
browser verifiers; a final gate pass.
The materials are Prime Intellect's, measured from their site: near-black
grounds, one green, sharp radii, mono small-caps labels, Geist and Geist Mono
self-hosted because production CSP is font-src 'self'. Two of their own greys
fail contrast on their own ground (#737373 is 4.02:1, #6E6E6E is 3.73:1 on
#0F0F0F), so --muted is lifted and the CSS comment carries the number — or
someone will 'correct' it back. Every text-on-ground pair in both themes is
tabulated in src/index.css with its measured ratio.
The two rules that resolved every conflict: data is mono, sentences are sans;
the language wins on materials, the lesson wins on legibility. Light mode is a
finished paper theme, not an inversion.
What did not change: the derived-tabs contract, the honesty markers, the
isolation lint, every gate. 419 contract checks, entry chunk at 74% of budget,
zero horizontal overflow on any route at 390/1024/1440 in either theme.
Also flips Alert Triage to status 'live' — the pipeline built it but never
promoted it, so it was badged SPEC on its own playable page and the home page
counted one environment.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019mt6sHQHEnEYrJZvoMCJSB
The Python is the source of truth; src/demos/wordle/engine.ts will be a port of
it, and CI gates the two against a SHA-256 over all 21.2M (guess, answer)
pattern pairs rather than a hand-picked vector file — a vector file only ever
catches the cases somebody thought of.
The reward is three weighted components, and the third one is the reason this
demo is worth building. `solved` and `economy` pull toward winning. `consistency`
pulls against them, because a player maximising information deliberately guesses
words that cannot win — a word that splits the remaining candidates evenly
teaches more than a word that might happen to be right. That is good play, and
it costs consistency.
The probe ladder proves the tension is real rather than asserted:
inaction 0.0000 crude 0.0111 plausible 0.1224
candidate_only 0.8925 exhaustive 0.9031 oracle 0.9458
The two good policies are 0.05 apart and neither dominates — the entropy oracle
takes 1.00 economy and 0.73 consistency, the candidate-only player takes 0.75
and 1.00. Which one wins is a decision about what you want, which is the whole
argument the site exists to make. probe.py fails CI if either starts dominating.
Two traps found by building it. `consistency` is scored over turns SPENT, not
guesses accepted: counting only legal guesses hands a free 1.0 to a policy that
plays one word and then jams the parser five times — one guess, no
contradictions, perfect score. And `economy`'s denominator is the depth the
SHIPPED solver reaches, not a depth-optimal search: entropy-greedy is not
depth-optimal, so grading it against an exact optimum would make the oracle
rung fail its own assertion on some seeds.
The word lists are built from Wordnik (MIT) intersected with SCOWL, never from
the original game's 2,315 answers. 4,603 answers makes this materially harder
than the original, so the published SALET/3.4212 results are cited as belonging
to that list and our own reference player's TARES/3.72 is measured here.
verifiers is an optional extra. The engine, reward, solver and probe all run —
and gate — without an RL stack resolvable.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019mt6sHQHEnEYrJZvoMCJSB
karti-ai