git.karti.ai/PIG/PIG-Demo is now the source of truth, public and anonymously
cloneable. Every self-link in the site and the docs repoints there — Gitea
serves file paths at /src/branch/main/, not /blob/main/, so those needed
rewriting rather than a hostname swap.
CI moves with it. An archived GitHub repo is read-only and its Actions stop
firing, so leaving the workflow there would have meant a repo whose gates
silently never run. .github/ is deleted rather than kept for reference: a
workflow that can never execute is worse than no workflow, because it looks
like coverage.
The Gitea workflow is not a copy. That runner is aarch64 and installs pnpm
through corepack from `packageManager` rather than pnpm/action-setup, uses
checkout@v4 and setup-node@v4, and fetches uv from astral.sh directly. It is
also configured `container.network: host` — nothing here needs a service
container, but the comment says so, because that setting cost the sibling repo
three failed runs.
The header and footer icon changed from the GitHub mark to a neutral one. A
GitHub logo pointing at a Gitea instance is a small lie about where the code
lives, on a site whose argument is that you can go and check it.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019mt6sHQHEnEYrJZvoMCJSB
Two silent bugs in the prerender pass, and the second was caused by the fix for
the first.
`waitForSelector('#root > *')` defaults to waiting for VISIBILITY, and the app's
first child is the skip link, which is hidden until focused. So it burned the
full 30s timeout on every one of 17 routes — twelve minutes of a script that
printed nothing, because its output was buffered behind a pipe — while the page
had rendered the whole time. Switching to `state: 'attached'` then fired too
early instead: useSeo writes the head from an effect, so the title was still
index.html's for a tick, and every route would have baked the homepage's head.
That is the exact bug this script exists to prevent. It now waits for `main`,
then for readyState, then settles.
The board's yellow was --warning, 32 95% 31% — darkened until white text cleared
4.5:1, and at that lightness it renders BROWN. On a board where people arrive
knowing this square should be yellow, a brown square reads as a bug in the
scorer, which on a page arguing "the grader is correct" is the worst thing it
could look like. The fill is now a real yellow and the glyph went dark: more
expected AND higher contrast, 10.02:1 against 5.03:1.
Also measured something the Honesty page had honestly declined to claim. It said
our word list is easier than the original's because our dictionary rule keeps
plurals the original's editor removed by hand. Running the same greedy solver
over both pools, 250 sampled words each: original 2,315 needs 3.552 guesses
(opener RAISE, worst 5), ours 4,603 needs 3.700 (opener TARES, worst 6). The
doubled pool outweighs the plurals. Ours is harder, and the page now says so
with the table.
177 gate checks pass. Entry chunk 106.9 kB gzipped against 160 kB.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019mt6sHQHEnEYrJZvoMCJSB
The browser engine is a port of the Python one and CI proves it: all 21.2M
(guess, answer) pairs hashed on both sides to the same SHA-256. Six TS tests,
including the duplicate-letter table and the twelve pinned seed vectors that
keep ?seed= permalinks pointing at the same word the recording used.
Word lists are split by how they are used. answers.json is inlined because the
board needs it before first paint to turn a seed into a word, and a fetch there
means a visibly empty board on a cold cache. guesses.json is fetched, because it
is three times larger and only needed the first time somebody presses Enter;
until it lands, validation falls back to the answer list, which accepts strictly
fewer words. The failure mode is 'your real word was briefly rejected', not 'a
non-word was accepted' — the right way round.
The solver runs in a worker constructed from a same-origin module URL, never
Vite's ?worker&inline: that yields a blob:, and production CSP has no
worker-src, so it falls back to default-src 'self' and the worker is blocked
with no console error. It would fail in production only.
deploy.sh smoke-tests the real public hostname from the deploying machine and
fails on a body under 1 kB, because the bind bug's signature is a valid
certificate over an empty 200 and a local --resolve check passes anyway.
Co-Authored-By: Claude Opus 5 (1M context) <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
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