zeta-techlead - Retrospective

What Went Well

Parallel generation throughput: Generating 31 video clips in batched waves (6-8 concurrent Veo calls) was highly effective. Total principal photography completed in ~90 minutes for 360s of raw footage.
Overhang principle (+4s pre/post-roll): Building in 2s pre-roll and 2s post-roll on every shot gave the editor flexibility to trim and crossfade without ever running short on material. This was a key decision from the playbook and paid off perfectly.
Content filter workarounds: After learning that Lyria and Veo reject named persons (“Bernard Herrmann”) and explicit violence (“building collapse”), I switched to component-based descriptions (“deep bass rumble, stone and wood creaking under strain”) which passed every time. This pattern was reusable across all subsequent generations.
Pure Python PNG resizer: When ffmpeg lacked PNG codec support and no image libraries were available, writing a raw PNG reader/writer with bilinear interpolation in pure Python solved the 1376x768 -> 1280x720 standardization problem for all 74 assets. Unconventional but effective.
Timeline xfade fix: Diagnosing that the genmedia-assemble timeline tool’s xfade filtergraph chain breaks on hard cuts, and fixing it by converting scene-boundary hard cuts to 0.5s crossfades using pre-roll source material, saved the assembly from a showstopper.

GCS upload race condition: Launching 14 gcloud storage cp background commands then immediately starting extend operations caused all extends to fail (“No such object”). Had to re-upload sequentially with verification. Wasted ~15 minutes.
File name collisions in parallel generation: When multiple Veo generations completed within the same second, the timestamp-based filenames collided and overwrote each other. Had to manually recover files from unique GCS operation paths in stdout logs.
Movement 2 score content filter: The prompt containing “Hitchcockian” and “Bernard Herrmann” was rejected. The error message was generic (“sensitive words”), requiring trial-and-error to identify which terms triggered it.
Context window exhaustion: The first session ran out of context mid-Step 6, requiring a continuation session. The 31-shot generation with all the callbacks, retries, and log inspection consumed enormous context.
21s audio silence gap: The escape/chase sequence (Scenes 4-5, 134-174s) had no music coverage. This gap was only caught during the editor’s QC of the assembled master, not during asset planning. Should have mapped audio coverage against the full timeline before declaring Step 6 complete.

Veo “no videos were generated” failures: Shot 6 and Shot 26 base generations silently failed, likely due to content filters on specific visual descriptions (e.g., “ebony cane tip hitting white marble tile”, “mansion crumbling”). Required prompt rewording and retry. No way to get specific rejection reasons from the API.
Extend dependency chain: Each extend requires uploading the previous clip to GCS first. With 14 single-extends and 2 double-extends, the sequential upload-then-extend pattern was the primary bottleneck. A pre-upload step before the extend wave would have been more efficient.
Tool bug — timeline xfade chain collapse: genmedia-assemble timeline cannot handle hard cuts (clips with no overlap/transition) interspersed with crossfades. The xfade offset accumulation breaks when it encounters a hard cut, truncating the output at that boundary. Workaround: make all transitions crossfades.
Lyria format mismatch: Lyria Pro returns MP3-encoded audio with a .wav extension. This required awareness and trim/convert steps before use in the timeline assembler.

Used veo-3.1-fast for base + veo-3.1-lite for extends: Fast model for quality base clips, lite model for continuation extends where visual consistency mattered more than generation quality. This saved time without visible quality loss.
Batched shots by scene rather than sequentially: Generating all shots within a scene simultaneously (sharing character reference context) improved visual consistency within scenes vs. generating in strict shot order.
Converted hard cuts to crossfades for assembly: Rather than building a manual FFmpeg pipeline (which the editor was attempting), I chose the simpler fix of converting 5 scene-boundary hard cuts to 0.5s crossfades in the timeline JSON. This preserved the tool-based pipeline and avoided custom scripting.
Trimmed score movements for specific sections: For Movement 3, took the tail 25.5s (peaceful resolution) rather than the front (which was still climactic), ensuring the right emotional tone for the ending.
Generated escape score post-QC: Rather than trying to extend an existing movement, generated a fresh purpose-built 45s stem for the 134-174s gap. Faster and better matched to the sequence’s energy.

Pre-flight audio coverage map: Before declaring any audio step complete, map every second of the timeline against available audio tracks. A simple script that identifies gaps >5s with no music/SFX coverage would have caught the 21s silence before assembly.
Fix genmedia-assemble timeline hard cut handling: The xfade chain collapse on hard cuts is a critical tool bug. Either the tool should handle hard cuts natively (by concatenating rather than xfading at those boundaries) or the documentation should warn about it.
Standardize Lyria output format: Document that Lyria Pro returns MP3 in .wav containers. Or have genmedia-music automatically convert to actual PCM WAV on output.
Add unique identifiers to generated filenames: Veo’s timestamp-based naming causes collisions in parallel generation. Adding the operation ID or a sequence number to filenames would prevent this.
Shorter context workflows: The 31-shot generation with per-shot callbacks consumed most of the context window. A batch-status approach (generate all, then check results) would be more context-efficient than individual callbacks.
Template shared pre-upload step: Add a production pipeline step between storyboarding and principal photography that uploads all reference assets to GCS upfront, avoiding the upload-during-generation bottleneck.