WALPHA connects signals, memory and market context in one research system. Start with emerging attention, then follow the people, assets and stories behind it.
The system map
| System area | Where to explore it |
|---|---|
| Emerging attention | Hot Motion for exact tokens and Social Radar for wider subjects |
| Current signals and changes | Overview and Feed |
| Coin stories and published research | Alpha Threads and Brain Journal |
| Sources and their track records | Leaderboard and account pages |
| Assets and market context | Token and wallet dossiers and Token Board |
| Connected subjects | Intelligence Graph and the sources behind available relationships |
| Contributions and rewards | Community Rewards and Your rewards |
| Personal updates | Notifications for your posts, rewards and payouts |
Read about the planned WALPHA Launchpad and its use of the Rewards engine. Sources, networks and research views can have different depths of coverage.
How the research builds context
- 01Social and app signals
- 02People and assets
- 03Current attention
- 04Memory and relationships
- 05Cognitive interpretation
- 06Market context
- 07Research views
- 08Outcomes and new context
A new observation enters a growing record of people, projects, ideas and market activity. Short-term context keeps recent changes nearby. Long-term memory preserves earlier episodes, narrative states and relationships. Retrieval connects relevant history to the current question.
This accumulated context helps investigate a returning theme, a shift in community mood or a source whose role has changed. Measured outcomes and corrections give later research more to draw on.
Technology behind the research
| Research layer | Technology and role |
|---|---|
| Source processing and analysis | Python and typed analysis tools organize source observations and measurements |
| Durable knowledge and semantic retrieval | PostgreSQL stores evidence, research state and memory; pgvector supports semantic retrieval where enabled |
| Associative graph memory | Graphiti and FalkorDB support a derived layer for exploring relationships alongside the source-backed record |
| Cognitive interpretation | Language-model tools combine relevant evidence and memory to investigate meaning, mood and possible explanations |
| Market and on-chain intelligence | Metadata, volume, wallet-intelligence integrations and GMGN supply available token, market, holder and wallet context |
| Product views | Next.js and TypeScript present connected research across the product and documentation |
The memory and interpretation layers continue to develop. Their role is to bring more useful context to early signals as the evidence record grows.
Open WALPHA