The recap output looked notably different from recap-relay (bigger 15px font,
different palette, single-column cards). Match recap-relay's job-output view:
slate/indigo palette (--bg #0a0e1a, --accent #818cf8), 13px base type with the
Helvetica/Arial stack, monospace accent-soft timestamps, and the two-pane layout
— topic list on the left, full diarized transcript on the right, click a topic to
scroll + highlight its range (inline JS, data baked in; no backend fetch). The
summary/takeaways render as recap-relay-style cards in a band above the split.
markdown() output unchanged. 66 tests pass.
Takeaways categories are no longer hardcoded — they're editable templates. A
template = the always-on TLDR + an ordered list of sections, each with a title, a
type (attributed items / bulleted list / paragraph), and an instruction (the prompt
text for that category). The analyzer assembles the LLM prompt FROM the template
and parses generically, so adding/removing/renaming a category needs zero code and
the output always renders.
- RecapTemplate / TemplateSection / SectionKind + TopicGranularity; built-in
defaults (Internal Meeting, 1:1, Company/Sales Call), all editable.
- Generic extras: RecapExtras{tldr, primarySpeakers, sections:[RenderedSection]} +
RecapItem{text,who,when,note} replaces the fixed MeetingExtras. Analyzer builds
per-section sec_N fields + parses by kind; renderer + remap are generic.
- Topic granularity (coarse/auto/fine) answers 'should chunking be configurable' —
it scales the target topic count; raw window sizes stay as tuned defaults.
- AppSettings persists templates + defaultTemplateId (seeded once). Settings gets a
default-template picker + 'Manage…' → TemplatesView (CRUD, edit sections/
instructions, set default, **Preview prompt** for full transparency).
- Recap editor gains a template picker; Regenerate uses the chosen template. Auto
recap uses the default template.
54/54 XCTest (template prompt build, generic parse/remap/render updated).
New 'Recap' phase — turns speakers.json into a human-readable recap, leveraging
recap-relay's proven logic/prompts but calling the Spark gateway's OpenAI-compatible
/v1/chat/completions directly (same host/TLS as label-merge; Qwen3-35B). We start
from already-named speakers (label-merge), so recap-relay's speaker clustering +
name-inference are skipped entirely.
- GatewayLLMClient: /v1/chat/completions (JSON mode), model discovery via
/api/endpoints, TLS-skip reuse, 503 retry, sequential.
- RecapAnalyzer: speakers.json → numbered [N] (MM:SS) Name: text transcript →
time-windowed analyze (single window for short calls, 18min/2min overlap for long)
→ stitch/dedup topic sections → meeting extras (TLDR/decisions/action_items/
open_questions/key_quotes). Defensive JSON parsing of LLM output.
- RecapRenderer: writes transcript.md + a self-contained dark-theme recap.html
(topic sections w/ collapsible transcripts, extras panels, speaker color chips,
full timestamped speaker-attributed transcript, print styles).
- SessionController.buildRecap: best-effort after speakers.json (gated by
settings.recapEnabled); surfaces recapURL → menu 'Open recap'. Skips silently if
the gateway has no LLM. Settings toggle added.
Validated END-TO-END on the real Meet session against the live gateway: dual-channel
transcription → 3 topic sections + accurate TLDR + key quotes; 'Go Bitcoin'
correctly attributed to the remote speaker. 46/46 XCTest (10 new).
Grant Gilliam