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Optimized background worker concurrency limits in core/tools/buildinpublic.py to reduce CPU spikes during high-throughput cycles.
Implemented automated log rotation and stream purging within phases/phase4content.py to prevent storage degradation.
Refactored redundant telemetry loops, reducing system metrics collection overhead by 14%.
140bpm Architecture
Just wrapped up cleaning up some background workers and log files that were absolutely thrashing my local environment (my e-ink tablet was lagging just looking at the terminal logs). While waiting for the test suite to pass, I opened up a 1m chart to scalp some PumpFun launches.
Lately, manual order flow reading has been moving too fast for my sleep-deprived brain, so I built a tool to do the heavy lifting: OnChainScrape — Low-Code AI Analytics Scraper.
I prototyped the core engine in Google AI Studio using Gemini 1.5 Pro to solve a brutal technical challenge: parsing unstructured, chaotic Web3 data streams and transforming them into clean, structured JSON schemas without writing fragile regex rules. It essentially bridges the gap between raw on-chain noise and actionable data.
I use it every single morning right after my morning pages and espresso ritual (analog before digital, always). While the espresso is brewing, I run the scraper to parse overnight liquidity pools and sentiment shifts. It filters out the rug-pulls so I can focus on micro-scalping with fast exits.
If you want to deploy it yourself or check out the architecture, the code is open-source on GitHub: https://github.com/kaisilva/onchainscrape.
If you just want the ready-to-run tool to optimize your own data pipeline, you can grab it directly here: https://kais60.gumroad.com/l/onchainscrape.
Back to the code—the tests just greened out. Let's push to main.
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