2026-04-20T01-56Z

4/20/2026, 1:56:50 AM · 1 flow · 315,790ms total

clean All settings match production defaults (app_defaults.yaml asOf 2026-04-19).

Build provenance

App

1.4·3

com.flashcopy.app.dev

Git

36652cef76

feature/processing-animation-variants · dirty

Sim

AAC26DF1…

com.flashcopy.app.dev

Built at

4/19/2026, 1:55:28 AM

video

ok pipeline →

Input media

No media file found for this input.

IMG_4558.mov37.21 MBsha256 96059437ee…

Input id

BBBB0003

Total

315.79s

Output

694 words

7360 chars

Cost est

$0.00184

gemini-2.5-flash · basis: chars

in 9,855 · out 3,680 tok

Stage timings

frame-extraction

0ms

s3-upload

4.39s

cloud-processing

311.40s

Stage details

frame-extraction	frames 28
s3-upload	bucket qr-video-ocr-frames
cloud-processing	lambda backgroundProcessor persisted true chars 7360

Extracted text

694 words · 7,360 chars

```json
{
  "stitched_response": "import * as vscode from \"vscode\";\nimport * as aws from \"aws-sdk\";\n\nexport function activate(context: vscode.ExtensionContext) {\n  const disposable = vscode.co…

Prompts used

all prompts →

frame_ocr gemini-2.5-flash 66326cc5be… · 81 chars

Perform OCR on this image and return plain text only. Do not describe the image.

qr_reader_v1/frame_ocr_lambda.py:31

video_polish gemini-2.5-flash structured JSON sha n/a · 898 chars

Please stitch together the OCRs that are taken from individual screenshots/frames from a video. There should be overlapping lines which can be used as a marker for when one frame ends and another begins. Please do not alter the content in any way besides stitching the content together and please add correct indentation. Do not alter or add any content.

Your final output must be a single, valid JSON object and nothing else. The JSON object must conform to the following structure:
{
  "stitched_response": "(string) This key must hold the final, fully reconstructed and cleaned document text.",
  "additional_notes": "(string) Use this field to briefly describe your process. Mention any significant noise you filtered out (e.g., 'Removed text from a Save As dialog box') or any ambiguities you encountered during the reconstruction."
}

Here is the raw OCR text from video frames:

{raw_text}

qr_reader_v1/video_ocr_polishing_lambda_rest.py:102

Output diff

no baseline

No prior run for this input (sha256 96059437ee…) and no ground-truth file at data/ground_truth/<sha>.md. After another ingest the diff will render here.

Video credit reconciliation

1 credit / frame

Frames

Charged

n/a

Expected (1/frame)

Current app bills by duration seconds; at fps=1.0 frames == seconds so this matches by accident. Any non-1.0 fps surfaces a mismatch.

Run settings

Show all 20 values

{
  "videoFramesPerSecond": 1,
  "videoStitchingMethod": "gemini_only",
  "videoPipelineMode": "s3_parallel",
  "useBackgroundVideoProcessing": true,
  "rekognitionThreshold": 80,
  "geminiModel": "gemini-2.5-flash",
  "photoOcrPromptSha": "48496a3017a2708a92d142281c5ab19f64f8132555514a00cbc35ca9d39daeba",
  "frameOcrPromptSha": "66326cc5be6bdd434dbbdd330b519e26bd8bbcab4a6037a64c2148b66cd2aceb",
  "imageRetentionHours": 24,
  "bypassImageSaveConfirmation": true,
  "bypassProcessingResultWindow": true,
  "enableAnalytics": true,
  "confirmCollectionReset": true,
  "enableNotifications": false,
  "autoProcessImages": true,
  "includeBrandingInSharedText": true,
  "autoSavePhotos": true,
  "multiPhotoSeparator": "double_line",
  "showDebugInfo": false,
  "headerFooterStyle": "equals"
}