Crypto Scaling is a Legal Problem
Tags: Lawyers, Blockchain, Cryptolaw, Law

What Does CleanApp Know About Law?

CleanApp got its start as a global jurisdiction mapping exercise. We saw litter on the ground, and we asked a simple question: who is responsible for cleaning it up?

The first processes we invented were devoted to crowdsourcing jurisdictional data from incident reporting and responding parties in various overlapping jurisdictional domains. Our core crew was, and remains, a crew of strong globally-minded legal coders.

Over the past five years, CleanApp’s understanding of the socio-legal matrix that structures ordinary waste hazard reporting-analytics-response processes has become much deeper. We want to share every key lesson we learned along the way so that fellow developers, code-coders, and law-coders don’t get stuck.

We have no agenda beyond collaboration; if our legal postures and arguments save you the hassle of reinventing the wheel and expending scarce resources on needless legal work, then so much the better.

The CleanApp Foundation has unique and valuable perspectives on ongoing DLT debates. We don’t just bring out critiques. We also outline solutions for gaining maximum legal freedom of action in the contemporary global regulatory landscape.

One of our solutions outlines massive system-wide efficiency gains from coalescing around CleanApp as a test platform for the law of the New Internet. Here’s a peak into what that means.

For Less Viscous Law, Launch CleanApps

You, our fellow tech enthusiasts, entrepreneurs, BigTech theorists, and crypto-developers, stand to benefit the most from a successful CleanApp deployment. You gain the most from globally scaled CleanApp data structures and data markets around mundane reporting of ordinary sidewalk litter, gooey spills in store parking lots, messy kitchens at home, and so on.

How, exactly, do you benefit? To start:

CleanApp represents one of the largest untapped data I/O fields & markets in the material world.

As we outline in greater detail in our appeals to the DLT community and in our Whitepaper, it’s ultimately in your long-term interests to partner with us to design legal DLTs that facilitate these globally-decentralized, infinitely-scalable resource markets. Here’s why:

When you help us develop a fluid and decentralized, yet truly global, crypto-security paradigm that can withstand operational stresses in the trash/hazard-mapping context, you will be prototyping the key BlockTech innovation most needed to overhaul, um, the Internet: flexibly constituted, predictable, secure, semi-permeable jurisdictional bounds.

CleanApp = Jurisdiction Mapping

“Flexibly constituted, predictable, secure, semi-permeable jurisdictional bounds” is a mouthful at first.

But what we mean are rigorously probabilistic answers to the questions: (a) who is responsible for cleaning up a particular object or condition in a particular location; (b) what should be the best allocation of cleanup costs in cases where a condition straddles a property line, or where the objects and conditions are in a blurred public-private space; (c) what is the best way for assuring seamless data-sharing transitions from one jurisdiction (such as one’s home) to another jurisdiction (such as one’s apartment building) or another jurisdiction (such as a municipal sidewalk), and so on and so forth; (d) etc.

All of these questions yield actionable data, which by itself is very valuable. However, the jurisdictional maps that are created in the process of building global incident report markets are useful far beyond incident reporting markets.

For instance, dynamic CleanAppMaps add tremendous value to all navigation systems, which is crucial in our continuing transition to autonomous transit. Dynamic jurisdiction maps are key to more effective security frameworks, and so on.

The yields from these data structures go far beyond litter, waste, or hazard reporting. These jurisdictional maps and data structures will underpin the 21st century’s CivicTech, LegalTech, and CleanTech fields, among others.