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
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