| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Generates photorealistic composite images by detecting user facial features from uploaded photos and blending them into pre-rendered or dynamically generated scenes featuring a target celebrity (Sama Bankman-Fried). Uses computer vision for face detection and alignment, combined with generative image synthesis (likely diffusion models or GAN-based inpainting) to seamlessly composite the user's face into celebrity contexts while maintaining lighting, pose, and perspective consistency.
Unique: Specialized single-purpose implementation targeting a specific celebrity figure (Sama Bankman-Fried) rather than generic face-swapping; likely uses domain-specific training or curated scene datasets to optimize output quality for this particular use case, with pre-optimized lighting and pose contexts.
vs alternatives: More focused and potentially higher-quality output than generic face-swap tools because it optimizes for a single target identity and curated scene library, rather than attempting arbitrary celebrity matching across thousands of possible subjects.
Provides a web-based interface for users to upload photos, triggering an automated backend pipeline that handles image validation, preprocessing (resizing, normalization), face detection, and synthesis orchestration. The system manages file storage, temporary asset cleanup, and delivery of final composite images through a stateless HTTP API, likely using a serverless or containerized architecture for scalability.
Unique: Minimal-friction web interface designed for viral sharing — no authentication, no account creation, single-page flow from upload to download/share, likely optimized for mobile devices and social media integration (direct share buttons for Twitter, Instagram, etc.).
vs alternatives: Lower barrier to entry than desktop applications or API-first tools; optimized for rapid iteration and social sharing rather than batch processing or advanced customization.
Detects facial landmarks and bounding boxes in user-uploaded images using computer vision (likely OpenCV, dlib, or deep learning-based detectors like MTCNN or RetinaFace), then normalizes face pose and scale to match pre-defined target geometries in the celebrity scene templates. Handles rotation, translation, and scale correction to ensure consistent blending regardless of input photo orientation or framing.
Unique: Likely uses a specialized face detection model optimized for diverse lighting and pose conditions (e.g., RetinaFace or similar), combined with explicit pose normalization to handle the specific geometric requirements of the celebrity composite templates.
vs alternatives: More robust than simple template matching or Haar cascades; deep learning-based detection handles varied lighting and poses better than classical CV approaches, enabling higher success rates across diverse user photos.
Synthesizes photorealistic composite images by inpainting the user's face into pre-rendered celebrity scene templates using diffusion models (likely Stable Diffusion, DALL-E, or proprietary fine-tuned variants) or GAN-based inpainting. The system masks the target region in the scene, conditions generation on the user's face embeddings or aligned face crop, and applies post-processing (color correction, edge blending) to ensure seamless integration with background lighting and perspective.
Unique: Likely uses a fine-tuned or adapter-based generative model specifically optimized for face blending rather than generic image generation, with pre-computed scene embeddings and lighting-aware conditioning to ensure consistency across multiple generations.
vs alternatives: More photorealistic than simple face-swap or copy-paste approaches; diffusion-based inpainting naturally handles lighting, shadows, and perspective blending, producing results that appear as genuine photographs rather than obvious composites.
Generates shareable URLs for composite images and provides direct integration with social media platforms (Twitter, Instagram, Facebook, LinkedIn) for one-click sharing. The system stores generated images in a CDN or cloud storage backend, creates short URLs with tracking parameters, and embeds Open Graph metadata (og:image, og:title, og:description) to enable rich preview cards when links are shared on social platforms.
Unique: Likely implements a lightweight URL shortening and tracking layer with pre-generated Open Graph metadata, optimized for rapid sharing and viral distribution rather than deep analytics or user account management.
vs alternatives: Reduces friction for social sharing compared to manual download-and-upload workflows; pre-populated share intents and rich preview cards increase click-through rates and perceived legitimacy of shared links.
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Selfies with Sama at 18/100. Supabase also has a free tier, making it more accessible.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
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