Eliminate the dual code path (raw fetch vs AI SDK) for text and vision.
All providers now go through createLanguageModel() + generateText(),
removing chatOpenAiCompatible/analyzeOpenAiCompatible, the manual Usage
type, summarizeUsage, and responseFormat plumbing from 8 call sites.
Key fix: @ai-sdk/openai v3 defaults to the Responses API (/responses);
DeepSeek only supports Chat Completions, so we use .chat() explicitly.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Painter composites exactly plan.entryActiveCharacters into the entry
frame (the same roster the Cinematographer framed). Phase B is told to
reuse that roster, but only the entry beat's id was code-enforced — so an
LLM slip could leave a character in the painted frame that the runtime
entry beat says isn't there. Pin activeCharacters onto the plan's entry
beat as a last line of defense, mirroring the existing id pin.
Speaker is intentionally left to the prompt: it's coupled to line/TTS, so
overwriting it could mis-attribute or orphan Phase B's dialogue.
Addresses Copilot review feedback on PR #27.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The Writer was the serial long pole: a single LLM call wrote the scene
skeleton AND the full beats[] graph before anything downstream could
start, so variable-length beat generation blew up tail latency.
Split it into two calls:
- Phase A (runWriterPlan): minimal skeleton the image pipeline needs
(sceneSummary, sceneKey, entryBeatId, cast, entry roster, entry speaker).
Serial, on the critical path, kept lightweight.
- Phase B (runWriterBeats): full beats[] + storyStatePatch, written to
honor the plan. Launched immediately, overlaps the ENTIRE image pipeline
(cards / cinematographer / portraits / painter), awaited last.
Critical path becomes PhaseA + max(imagePipeline, PhaseB), so the long
beat-writing is hidden behind image gen. A Phase B failure degrades to a
single playable beat synthesized from the plan.
Paired distinct-payload A/B (6 content-matched stories, baseline vs split):
- median end-to-end 42.6s -> 32.2s (-24%)
- mean 46.4s -> 33.1s (-29%)
- worst case 74.7s -> 37.6s (halved)
- no content regression: total Writer output tokens 12858 -> 13699
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Add a `tag` option to chat() and have it print one `[cache] <tag>
hit=X miss=Y rate=Z%` line per call. Three Usage-shape variants are
probed in order so the same logger works across providers:
- DeepSeek (v3+): usage.prompt_cache_hit_tokens / *_miss_tokens
- OpenAI / o-series: usage.prompt_tokens_details.cached_tokens
- Anthropic: usage.cache_read_input_tokens / *_creation_*
When none of them are present (MiMo / local Ollama / others) we still
print prompt + completion totals so the cost baseline is visible.
Tag every callsite so the log is greppable:
architect / writer / character-designer / cinematographer / insert-beat
This is the prerequisite for the prefix-cache reordering work that
follows — without per-agent visibility there's no way to tell if a
prompt rearrangement actually moved the needle.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Flatten the pnpm monorepo (apps/web + packages/*) into a single web package at the repo root.
- Move app/lib/components/scripts/public to root; drop apps/web and packages/* wrappers
- Rewrite tsconfig paths (@infiplot/*) to ./lib/*; turbopack.root = __dirname
- Update Vercel (no root-directory) and Cloudflare (pnpm build:cf at root) deploy paths
- Regenerate pnpm-lock.yaml to drop stale workspace importers
- Bump engines.node to >=22 to match wrangler
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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