WaspGPT

Ingests and normalizes cryptocurrency news from fragmented sources (Twitter, CoinTelegraph, traditional finance feeds, on-chain data providers) into a unified feed with consistent metadata (timestamp, source credibility score, asset tags). Uses content deduplication and source-weighting algorithms to surface unique stories and filter noise, presenting aggregated results through a single interface rather than requiring manual cross-platform monitoring.

Applies large language model inference over aggregated news, price data, and on-chain metrics to generate interpretive analysis, market context, and trading implications. The system likely uses prompt engineering or fine-tuning to synthesize multi-modal crypto data (news sentiment, transaction volume, whale movements) into human-readable narratives explaining market drivers and potential outcomes, rather than serving raw data alone.

Implements a tagging and filtering system that maps news, analyses, and market data to specific cryptocurrencies, blockchain addresses, or DeFi protocols. Uses entity recognition (likely NER or regex-based pattern matching) to identify asset mentions in unstructured text, then allows users to subscribe to intelligence feeds filtered by asset, sector (DeFi, Layer-2, staking), or risk category. Enables personalized dashboards showing only relevant information for a user's portfolio.

Collects sentiment signals from multiple sources (social media mentions, news tone, on-chain transaction patterns, exchange funding rates) and synthesizes them into composite sentiment scores (bullish/bearish/neutral) for specific assets or the broader market. Likely uses sentiment analysis models (fine-tuned transformers or rule-based scoring) applied to news headlines, Twitter/X posts, and community discussions, then aggregates scores with time-decay weighting to reflect current market psychology.

Implements a freemium business model where basic news aggregation and sentiment feeds are available to free users, while advanced features (detailed on-chain analysis, historical backtesting, premium analyst reports, API access) are gated behind paid subscription tiers. The architecture likely uses role-based access control (RBAC) to enforce feature limits, rate-limiting on API endpoints, and feature flags to toggle premium capabilities per user tier.

WaspGPT aggregates cryptocurrency intelligence from multiple sources, but the specific data providers, update frequencies, and freshness guarantees are not documented. The system likely integrates with news APIs (CoinTelegraph, Crypto News, etc.), social media streams (Twitter/X, Discord), and possibly on-chain data providers (Glassnode, Nansen), but the architecture for source prioritization, conflict resolution, and update scheduling is opaque.