Jeremy Knox. The Signal Behind the Signal.

I'm a software engineering leader with 14 years building products at scale — spanning digital marketing technology, blockchain infrastructure, and AI-driven systems. I've led engineering teams, shipped enterprise software used by Fortune 500 companies, and mentored the next generation of developers.

The crypto side started as a personal obsession. It became a professional one when I spent two years as Chief Blockchain Analyst at Hive Mind Crypto, educating 300+ investors through the 2021 bull run and subsequent bear market. That experience taught me one thing above everything else: emotional analysis kills accounts. Systematic analysis survives.

InDecision is the framework that came out of that lesson — built from 7 years of personal trading data and refined through live market conditions. It's not a black box. It's a model you can understand, audit, and trust.

Most crypto “analysis” is post-hoc rationalization. A trader with a gut feeling finds the chart that confirms it. Twitter sentiment overrides the technicals. A celebrity tweet nukes the setup. The information exists to make good calls — the failure is in how humans aggregate it.

I built InDecision because I wanted a model that couldn't be swayed by a trending hashtag. Six independent factors. Each measuring a distinct dimension of market structure. Synthesized into a single conviction score before any directional bias is formed. The model abstains when signals conflict — and that's a feature, not a bug.

Publishing it publicly wasn't a business decision at first — it was an accountability decision. If the signals are good, they should hold up under scrutiny. So far, they do.

The InDecision Framework is the foundation. On top of it, I'm building an ecosystem: an automated signal pipeline with live Polymarket trade execution, a premium intelligence layer for serious traders, and open educational content to make systematic trading accessible.

The goal isn't to sell picks. It's to teach the model — and then let the model do its job. If you understand why a signal fired, you can trust it when it fires again.