| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Aggregates user-submitted comments into nested thread hierarchies with real-time upvote/downvote scoring that determines visibility ranking. Uses a tree-based comment structure where each reply maintains parent-child relationships, and implements a time-decay ranking algorithm that surfaces high-quality discussions while deprioritizing older low-scoring threads. The ranking system balances recency with community consensus through weighted scoring that accounts for vote count, submission timestamp, and comment depth.
Unique: Implements a simple but effective time-weighted ranking system that combines vote count with submission recency using a decay function, rather than pure chronological or pure popularity sorting. The tree-based comment structure with collapsible threads allows users to navigate deep discussion hierarchies without losing context of parent comments.
vs alternatives: Simpler and faster than algorithmic feeds (Reddit, Twitter) because it uses deterministic scoring rather than ML-based ranking, making it more predictable for power users while sacrificing personalization
Enables community members to flag, downvote, and report problematic content which triggers visibility reduction and potential removal by moderators. The system uses a combination of automated rules (spam detection, duplicate detection) and human moderator review to maintain discussion quality. Moderators can edit, delete, or flag comments as 'dead' (hidden by default), and the system maintains a moderation log visible to the community for transparency.
Unique: Uses a lightweight, transparent moderation model where community members can see moderator actions and reasoning through a public moderation log, rather than opaque algorithmic content removal. The 'dead' comment state allows content to be hidden by default while remaining accessible to users who explicitly choose to view it, preserving context without forcing visibility.
vs alternatives: More transparent than platform-moderated systems (Facebook, YouTube) because moderation decisions are logged and visible, but less scalable than AI-moderated systems because it relies on human judgment and community reports
Maintains a persistent reputation score (karma) for each user based on cumulative upvotes received on their submissions and comments. The karma system is used to gate access to certain features (flagging content, creating posts, voting) and to provide social proof of user credibility. Karma is calculated as a simple sum of upvotes minus downvotes, with no decay over time, and is displayed publicly on user profiles to establish trust and authority within the community.
Unique: Uses a simple, transparent karma calculation (sum of upvotes minus downvotes) with no algorithmic weighting or decay, making it predictable and auditable. Karma is used as a gating mechanism for moderation features, creating a self-reinforcing system where trusted community members gain more influence.
vs alternatives: More transparent than algorithmic trust systems (Twitter's Birdwatch, Facebook's Community Notes) because karma is directly tied to community voting, but less nuanced than systems that weight different contribution types differently
Delivers new comments to users in real-time as they are posted, with automatic page refreshing and lazy-loading of comment threads to handle high-volume discussions. The system uses server-side pagination to load comments in batches, reducing initial page load time and allowing users to navigate through hundreds or thousands of comments without loading the entire thread at once. New comments appear dynamically in the thread without requiring a full page reload, and users can choose to load older comments on-demand.
Unique: Combines server-side pagination with real-time comment streaming, allowing users to navigate large discussions without loading all comments upfront while still seeing new comments appear dynamically. Uses a simple polling or WebSocket mechanism to deliver new comments to connected clients without requiring users to manually refresh.
vs alternatives: More scalable than loading entire threads upfront (like traditional forums) because pagination reduces initial load time, but less smooth than infinite scroll (Reddit) because pagination creates artificial boundaries
Allows users to link to specific comments, discussions, and external URLs within the comment text, creating a web of interconnected discussions. The system automatically detects URLs in comments and renders them as clickable links, and users can reference other HackerNews discussions by their item ID (e.g., 'item?id=12345'). Comments can be linked directly via a unique URL that includes the comment ID, allowing users to share specific discussion points with others.
Unique: Provides direct linking to individual comments via unique URLs, allowing users to share specific discussion points without requiring recipients to search through the entire thread. Automatically renders URLs in comments as clickable links without requiring markdown or special syntax.
vs alternatives: Simpler than citation systems (academic databases) because it requires no special formatting, but less structured than systems with automatic metadata extraction (Slack, Discord)
Maintains a persistent user profile that displays karma score, submission history, comment history, and user metadata (join date, location). Users can view their own profile to track their contributions and see how their content has been received by the community. Other users can view public profiles to assess credibility and see a user's historical contributions, creating accountability and enabling reputation-based trust.
Unique: Provides a simple, public user profile that displays all contributions and karma, creating transparency and accountability. Profiles are indexed and searchable, allowing users to find and evaluate contributors based on their historical participation.
vs alternatives: More transparent than closed reputation systems (LinkedIn endorsements) because all contributions are visible, but less detailed than systems with contribution analytics (GitHub profiles)
Ranks user-submitted stories and links on the homepage using a time-weighted algorithm that balances vote count with submission recency. The ranking formula (often referred to as the 'Hacker News algorithm') uses a logarithmic decay function that heavily weights recent submissions while gradually deprioritizing older content. The homepage displays the top-ranked submissions in a paginated list, with each submission showing title, domain, score, comment count, and submission time.
Unique: Uses a publicly-known, deterministic ranking algorithm (the 'Hacker News algorithm') based on logarithmic time decay and vote count, making it predictable and auditable. The algorithm is simple enough to be understood and replicated by users, creating transparency around what content surfaces.
vs alternatives: More transparent and predictable than ML-based ranking (Google News, Twitter) because the algorithm is deterministic and publicly documented, but less effective at surfacing diverse or niche content because it lacks personalization
Allows users to submit links and stories to the platform with automatic metadata extraction (title, domain, favicon) from the submitted URL. The system fetches the webpage, parses the HTML to extract the page title and Open Graph metadata, and displays this information in the submission form for user review and editing. Users can override extracted metadata and add custom titles or descriptions before submitting.
Unique: Automatically extracts metadata from submitted URLs using HTML parsing and Open Graph tags, reducing friction for users submitting external content. Allows users to preview and edit extracted metadata before submission, balancing automation with user control.
vs alternatives: More user-friendly than manual metadata entry (traditional forums) because it automates extraction, but less robust than systems with rich link previews (Slack, Discord) because it doesn't fetch or display page content
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 HackerNews Discussion at 19/100. 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
+1 more capabilities