Allocated to: 0xa9BE414c38F1612DeADf39e4666fd741F5199D6C (Julian Heller)
Transaction: Deeplink tx84524
Allocation: 0.58%, 5,800,000 Deeplink tokens
Allocation basis: agreed to cover expenses up to $500,000 in return for tokens

This allocation records expense coverage for Deeplink's core DeFi intelligence stack, spanning execution routing, data exploration, and graph based DeFi comprehension.

Your Eta X documentation frames the product correctly: a DEX and DeFi “search engine” that exposes a single access point through APIs for efficient route discovery, price discovery, pair matching, and slippage estimation without binding users to any specific pool or token. Medium

On top of routing, this allocation funded the intelligence layer, including Pythia as a natural language to SQL query execution and visualization framework, and DeFiGraph as a DeFi Knowledge Graph foundation with LLM based natural language interaction and API delivery.

Key Areas of Contribution

1. Eta X aggregator and smart order routing foundation

Funding supported the core Eta X system design and delivery scope, including:

DEX aggregation logic and API layer
Price discovery engine behavior across liquidity sources
Route discovery primitives and trade pair matching
Slippage and price impact estimation logic, positioned as universal and unbiased in routing outcomes Medium+1

Eta X (DEX Aggregation Engine):

• Reverse-engineering of AMM Conservation Functions (Uniswap v2/v3, Curve) to build a universal pricing model.
• Development of Pathfinding Algorithms (Smart Order Router) to split trades across multiple liquidity sources for zero-slippage execution.

The earlier Deeplink Beta v1 material also frames the deeper vision: smart order routing agents, autonomous rebalancing agents, and ML capabilities that can be integrated into execution logic and agent infrastructure. Medium

2. Pathfinding algorithms for routing

Funding supported development of pathfinding algorithms for order routing, aligned with your broader published work that treats pathfinding as a first class system, not a detail. This work underpins multi hop routing, route scoring, and optimization decisions under constraints like liquidity depth, fees, and execution cost. Medium+1

3. Pythia data exploration and query execution framework

Pythia (Web3 Business Intelligence):

• Development of the "Generation 1" SQL-to-Chart engine.
• Engineering the Postgres data warehouse tailored for indexing 200GB+ of raw CEX/DEX trade data.
• Implementation of an SQL IDE that allows non-technical users to query on-chain data and visualize results via Recharts.

Funding supported Pythia as a chatbot style data exploration system where users express questions in natural language, which is translated into SQL and executed against the database, with results returned as text and visual outputs.

Your Pythia post is unusually specific on the interaction model: charts and tables in responses, user controls like viewing SQL, editing properties, and running in a SQL lab. It also defines key components like real time streaming, chart rendering, and database integration. Medium

Deliverables include: NL query to SQL translation flow, query execution pipeline, result streaming and visualization interface, and the framing of Pythia as a data product creation substrate.

4. DeFiGraph Knowledge Graph foundation and natural language interface

Funding supported DeFiGraph as an initiative to build a Knowledge Graph mapping relationships across DeFi entities (protocols, tokens, liquidity pools, aggregators, blockchains) and exposing that graph through a user facing interface and APIs.

Your DeFiGraph post is explicit about scope: data collection, KG design, semantic mapping, graph database implementation, interactive frontend, and APIs. It also clearly positions the natural language interface as the bridge for non technical users. Medium+1

This allocation also reflects the stated collaboration set: L3A, Openmesh, Deeplink, MIT researchers, Neo4j, plus ecosystem distribution considerations such as marketplace integration. Medium+1

5. Developer facing APIs and productization

Funding supported API delivery so developers can run DeFi intelligence queries programmatically, not only via a UI. Your post emphasizes this as a core product capability, including examples of queries around blocks, transactions, and market events, and the product positioning of Knowledge Graph as a service. Medium

Actions

5,800,000 Deeplink tokens to
0xa9BE414c38F1612DeADf39e4666fd741F5199D6C