While optimizing the background workers for a data-heavy pipeline (specifically cleaning up bloated log files and refactoring core/tools/buildinpublic.py), I hit a classic bottleneck: standard deterministic scrapers fail the moment a target on-chain analytics site updates its DOM structure.
To solve this without writing fragile, custom parsing logic for every edge case, I prototyped OnChainScrape, a low-code AI analytics scraper built inside Google AI Studio using Gemini 1.5 Pro.
The Tradeoffs
The Architecture: Instead of maintaining Regex-heavy parsing trees or brittle CSS selectors, the pipeline pipes raw HTML/JS snapshots directly into Gemini 1.5 Pro's massive context window. The model extracts structured JSON based on a schema definition.
The Cost-Latency Tradeoff: This approach trades raw execution speed and API token costs for extreme resilience. It’s too slow for real-time high-frequency execution (where standard Go or Rust scrapers win), but it is highly efficient for asynchronous, complex data extraction where layout drift usually breaks code.
When the build pipeline gets tedious, I usually open a chart to scalp Solana meme coins (if the order flow doesn't move in 60 seconds, I'm out). To feed that addiction, I actually use OnChainScrape in my own morning protocol alongside a heavy espresso to pull structured sentiment data from obscure alpha forums before the market wakes up.
The source code is fully open for inspection on the GitHub Repository. If you want a packaged version to deploy immediately without configuring the boilerplate, you can grab it on the Gumroad Store.
