Julian Faulkner (J. Faulkner)
Rooted in Historical Training, A Mirror for the Digital World
My background is rooted in academic historical training—accustomed to interrogating primary sources, evaluating evidence, and tracing concepts to their roots. This humanist analytical framework ultimately guided me into the digital realm.
As a self-taught developer and quantitative builder, I believe technology should never be a mere stack of complexities, but a mirror designed to clear away noise and restore the essence of things.
Modern attention is frequently consumed by fragmented tools and information. Utilizing modern web technologies such as Python, TypeScript, React, and Supabase, I build lean, high-signal-to-noise personal utilities and experimental projects.
Core Tools & Experimental Projects
YieldSpot
A quantitative analysis tool that transforms complex dividend data and historical percentiles into intuitive decision-making criteria. Originally built out of an early passion for dividend theory, it evolved from Excel to the web. While capabilities expanded significantly, it highlighted a steep learning curve—where users often wonder how to begin despite tutorials. Rather than writing a heavy manual that deters users, I am exploring guided interfaces and AI assistance. If you have questions on how to operate it, feel free to drop me an email.
Reserve Pulse
A macro liquidity tool tracking Federal Reserve bank reserves (TOTRESNS), VIX, and broad market liquidity metrics. Frankly, this is a highly experimental project—given that the Fed has never operated this way before, and recent rate cycles frequently run counter to traditional analysis. Rather than a mature model, it is an exploratory attempt: observing whether liquidity can yield meaningful answers to market shifts. As for backtesting, the reality is that sufficient historical data simply does not exist; we must observe and adapt amidst unprecedented conditions.
Paceshaper
A marathon pacing and load management web application combining AI with scientific training data. The logic is straightforward: using running power to derive training intensity. Is the intensity appropriate? Let the data speak. If your data shows improved running power, intensity follows; if baseline capacity is lacking, stay the course. I firmly believe injury-free progression is the most reliable path forward. We are not professional athletes with dedicated teams; there is no need to fight physical limits. Future plans include detailed training log ingestion, but my current focus remains raising my own running power.
The Quiet of 5 AM, and the Sub-3 Running Journey
My daily routine begins in the quiet of 5:00 AM. This window is dedicated to deep reading, writing, and technical building.
This is typically followed by outdoor morning runs. As I train toward a Sub-3 marathon goal (4:15/km pace), I integrate data science principles into running form, VDOT metrics, and lactate threshold analysis, striking a balance between rational metrics and somatic awareness.
If you have thoughts to exchange regarding writing, tool development, or long-distance training, feel free to reach out via email.