MediaMind AI

AI-Powered Media Classification Library

Page 1 of 7 — Media Library
01 — Library Home
9:41
●●●

Your AI Library

1,247 items · 98.7% accuracy · 23ms avg

🧠
1,247
Total Items
98.7%
Accuracy
23ms
Avg Latency
14
Categories
Recent Captures
📸 Screenshot
📄 Document
🧾 Receipt
🔗 Article
💻 Code
🖼️ Photo

2:34

PM

Receipt – Whole Foods Market

AI: Grocery Receipt · 99.2% confidence · $47.23

1:18

PM

Screenshot – Figma Design System

AI: Design Asset · 97.8% confidence · UI Kit

11:05

AM

Article – TechCrunch AI Roundup

AI: Tech News · 98.5% confidence · 1,200 words

98.7%

Classification Accuracy

↑ +0.4% this month

02 — Smart Categories
9:41
●●●
Browse by Category
All
Screenshots
Documents
Receipts
Articles
Code
Links
342
Screenshots
198
Documents
87
Receipts
412
Articles
📸 Screenshots · 342
📄 Documents · 198
🧾 Receipts · 87
🔗 Articles · 412
💻 Code · 93
🖼️ Photos · 115

14

Active Categories

↑ +2 this week

72%

Storage Used

3.6 GB / 5.0 GB

03 — Flagged for Review
9:41
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🔍
All
High Confidence
Flagged
Today
This Week
5 Items Need Review
Low Confidence Captures

3:12

PM

Blurry Receipt – CVS Pharmacy

AI: 71.3% confidence · Needs manual review

10:44

AM

Mixed Content Screenshot – Notion

AI: Ambiguous · 2 possible categories · 68.9%

9:30

AM

Handwritten Note – Unknown

AI: OCR failed · Low contrast · 55.2%

96%

Auto-Classified

1,242 / 1,247 items

01 — Camera Capture
9:41
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Capture & Classify

Point at any doc, receipt, or screen — AI classifies instantly

📷
Live AI Detection
89%
Confidence
Receipt
Detected Type
12ms
Inference
OCR
Mode Active
89%

Scene Confidence

Receipt detected · Whole Foods

Auto
Document
Receipt
Screenshot
Link
Code

12ms

Real-time Hermes Inference

↑ New Architecture JIT

02 — Link Import
9:41
●●●
Import URL or Web Link
🔗
AI Content Preview
🧠

Claude 3.7 Sonnet – Analysis

Content Extraction + Classification

3

SETS

LLM passes

REPS

340

MIN

ms pipeline

CAL

100%

Content Extracted

Title, summary, tags ready

Now

TechCrunch – AI Agents in 2026

Category: Tech Article · Tags: AI, LLM, Agents · 1,340 words

01 — AI Results Detail
9:41
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Classification Complete

Receipt · 99.2% confidence · 18ms pipeline

99.2%
Confidence
Receipt
Category
18ms
Latency
Claude 3.7
Model
Extracted Data (OCR + LLM)

Merchant: Whole Foods Market

Google Vision OCR · 100% field accuracy

Total: $47.23 · Tax: $3.18

Line items: 12 · Payment: Visa ending 4821

Date: May 4, 2026

Location: 340 Bryant St, San Francisco, CA

Grocery
Food & Drink
Receipt
Expense
02 — Confidence Breakdown
9:41
●●●
Multi-Model Pipeline Analysis
👁️

Google Vision OCR

Text Extraction Layer

1

SETS

API call

REPS

8

MIN

ms

CAL

🧠

Claude 3.7 Sonnet

Semantic Classification

1

SETS

API call

REPS

10

MIN

ms

CAL

99%

OCR Accuracy

Vision API · 99.1%

99%

LLM Confidence

Claude 3.7 · 99.2%

Field-Level Confidence

Merchant
Total
Date
Items
Tax

18ms

End-to-End Pipeline

↓ P95: 34ms · P99: 48ms

03 — Manual Override
9:41
●●●
Override AI Classification
AI Flagged: Low Confidence 71.3%
71%

Original Confidence

Ambiguous content detected

Screenshot
Document
Receipt
Article
Code
Other
🏷️
📝

Re-run AI with Additional Context

Retry Claude API with override hints for better accuracy

01 — FlashList Media Feed
9:41
●●●
60fps
Scroll FPS
0
Dropped Frames
FlashList
Renderer
Hermes
JS Engine
Virtualized Media Grid — 1,247 Items
📸 Figma UI
🧾 Invoice Feb
📄 Q1 Report
🔗 Medium Post
💻 GitHub PR
🖼️ Wireframe
📸 Loom Recap
🧾 Stripe Payout
📄 Notion Doc

60fps

FlashList Scroll Performance

↑ 0 dropped frames

95%

Render Efficiency

Cell recycling active

Frame Budget Usage (ms per 16ms window)

Render
JS Thread
Layout
Commit
02 — Memoization Tracker
9:41
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React Optimization Dashboard

94%

useMemo Hit Rate

↑ +2% vs last build

Re-render Count by Component

MediaCard
CategoryBar
SearchBar
LibraryList
AIBadge

useMemo – MediaCard

Image Render Optimization

42

SETS

renders avoided

REPS

0

MIN

ms saved

CAL

🔁

useCallback – Event Handlers

Stable Reference Hooks

18

SETS

handlers stable

REPS

0

MIN

re-renders saved

CAL

94%

Memoized Components

47 / 50 components pure

01 — Classification Overview
app.example.com/analytics-dashboard
MENU
AI Classification Analytics – Last 30 Days
98.7%
Overall Accuracy
1,247
Items Classified
23ms
Avg Latency
5
Flagged Items

Daily Ingestion Volume (items)

Apr 28
Apr 29
Apr 30
May 1
May 2
May 3
May 4

Accuracy by Category (%)

Receipts
Articles
Code
Docs
Screenshots
Links

98.7%

Classification Accuracy

↑ +0.4% vs last month

99%

Auto-Classified

1,242 / 1,247 items

02 — Model Performance
app.example.com/analytics-dashboard
MENU
Multi-Model Pipeline Performance
8ms
Vision OCR P50
15ms
Claude API P50
23ms
E2E P50
$0.0023
Cost Per Item

Latency Percentile Distribution (ms)

P50
P75
P90
P95
P99

May 5

2026

Claude 3.7 Sonnet – Accuracy 99.2%

Tokens: 847 avg · Cost: $0.0015/item · Errors: 0 in 24h

May 5

2026

Google Vision OCR – Accuracy 99.1%

Requests: 1,247 · Avg: 8ms · Zero API errors

$2.87

AI Cost Today

↓ +$0.34 vs yesterday

88%

Daily Budget Used

$2.87 / $3.25 limit

01 — AI Pipeline Config
app.example.com/admin-config
MENU
AI Pipeline Configuration
Models
Prompts
Categories
Thresholds
Integrations
🧠

Claude 3.7 Sonnet

Primary Semantic Classifier

1

SETS

call / item

REPS

15

MIN

ms avg

CAL

👁️

Google Vision OCR

Text + Receipt Extraction

1

SETS

call / item

REPS

8

MIN

ms avg

CAL

Flag items below 80% confidence for manual review
Enable Anthropic prompt caching (beta header)
Auto-retry Claude API on 529 / timeout errors

23ms

Current Pipeline Latency

↑ Optimized · All systems nominal

02 — Prompt Engineering Lab
app.example.com/admin-config
MENU
Classification Prompt Management
Active Prompts
A/B Tests
Version History
Templates

v2.4

Active

Receipt Classifier Prompt

Accuracy: 99.2% · Tokens: 412 avg · Updated May 3, 2026

v1.9

Active

Article Extraction Prompt

Accuracy: 98.5% · Tokens: 287 avg · Updated Apr 28, 2026

v3.1

A/B Test

Screenshot Classifier – In Test

Variant A: 99.1% vs Variant B: 98.8% · 500 samples each

Accuracy by Prompt Version

v1.0
v2.0
v2.3
v2.4

99.2%

Best Performing Prompt

↑ v2.4 – Receipt Classifier

03 — GitHub CI/CD Pipeline
app.example.com/admin-config
MENU
GitHub Integration & EAS Deployments
main
Active Branch
v2.4.1
Build Version
Passing
CI Status
3m ago
Last Deploy

3:12

PM

feat: receipt prompt accuracy → 99.2%

SHA: a4f2e1c · Tests: 142 passed · Coverage: 94%

1:55

PM

fix: FlashList recycling on iOS 18.3

SHA: b8c9d3f · Scroll FPS improved +12%

11:20

AM

chore: upgrade to Claude 3.7 Sonnet

SHA: f1a3b2c · Model accuracy delta: +1.4%

94%

Test Coverage

142 / 151 tests passing

3m

Time Since Last EAS Deploy

↑ iOS + Android · OTA active

Feature Stack & Deliverables

Complete overview of confirmed features, deliverable items, and technical architecture for MediaMind AI.

🏗️

Tech Stack

React NativeExpo EASClaude APIGoogle VisionFlashListGitHub Actions

Core Technologies

⚛️
React Native — New Architecture + Hermes JIT for 60fps FlashList performance
📦
Expo EAS — Managed workflow with OTA updates, CI/CD, and EAS builds
🧠
Claude API — claude-3-7-sonnet for semantic classification and JSON extraction
👁️
Google Vision — OCR text extraction at 99.1% accuracy with 8ms average latency
FlashList — Shopify FlashList with cell recycling for high-volume media grids
🔄
GitHub Actions — Automated testing, coverage enforcement, and EAS deployment pipeline
📦

V1 Deliverables Checklist

  • Production React Native app with New Architecture + Hermes achieving 60fps FlashList scroll over 1,000+ media items
  • AI classification pipeline combining Google Vision OCR + Claude 3.7 Sonnet targeting 98.7%+ accuracy
  • Structured JSON extraction with domain-specific prompt engineering for receipts, articles, screenshots, and code
  • Confidence threshold system flagging items below 80% for manual review with full override UI
  • Multi-model confidence breakdown screen displaying field-level accuracy across OCR and LLM passes
  • Expo EAS CI/CD pipeline with GitHub Actions for automated iOS and Android builds and OTA delivery
  • Web admin dashboard with real-time analytics, prompt version management, and A/B testing for classifiers
  • Latency monitoring dashboard tracking P50/P95/P99 across each pipeline stage with cost-per-item tracking
  • 14-category auto-detection system with smart gallery view and FlashList-powered category browsing
  • Safe area and active-state styling compliance across iOS and Android with strict UI constraint enforcement
🔧

Architecture Layers

Capture Layer
Expo Camera + Clipboard + Share Extension
Camera frame capture, screenshot detection via clipboard listener, URL paste handler, iOS share extension, image preprocessing and compression before AI pipeline
AI Classification Pipeline
Google Vision OCR + Claude 3.7 Sonnet
Vision OCR text extraction at 8ms → Claude semantic classification at 15ms → JSON schema validation → confidence scoring → threshold flagging for review queue
Performance Layer
FlashList + Hermes + useMemo/useCallback
Virtualized media grid with cell recycling, 94% memoization hit rate, Hermes JIT compilation, New Architecture concurrent rendering, 60fps target on 1,247+ item lists
Data & Storage
SQLite + MMKV + AsyncStorage
Structured media metadata with category indexes, classification history and confidence logs, MMKV for high-frequency reads, AsyncStorage for user preferences and auth tokens
Admin & Observability
Web Dashboard + GitHub Actions + EAS
Real-time accuracy and latency metrics, prompt version management with A/B testing, GitHub Actions CI with 94% coverage gate, EAS builds with OTA delivery and cost budget alerts