Tougher Problems Inspire Creative Solutions.
ML Systems is a Rhode Island construction company (NAICS 236115) building the software layer for a circular building economy: homeowners reach capital markets, houses are deconstructed instead of demolished, and the recovered materials rebuild into a larger home — compounding equity, cycle after cycle.
This repository is the public reference for the ML Systems platform: the mobile
app's UI layer plus the conceptual documentation for the ideas that make the system
distinctive — the Value Chain, the Master Ledger, the Collective Ontology,
Ontological Compression, and the Seven Minds. These concepts are not
documented on any of the marketing sites (mlsystemsri.com, .net, .info,
.store, .xyz); this repo is where they live in the open.
The proprietary engine — backend API, data layer, and the ontology/ledger/compression algorithms — is intentionally not included here. See What's public vs private.
| Platform | Status | Link |
|---|---|---|
| iOS (App Store) | ✅ Approved & live | https://apps.apple.com/app/id6799697171 |
| Android (Google Play) | ✅ Approved & live | https://play.google.com/store/apps/details?id=com.mlsystems.app |
| Web (guest preview) | 🌐 Live | https://try.mlsystemsri.com |
App bundle id: com.mlsystems.app · Built with Expo / React Native.
Most homes in Rhode Island's housing stock sit on ~1960s foundations that are the real limiting factor. ML Systems starts a homeowner at Loan Origination (a reverse-auction "Loan Pit" where lenders compete to fund them), deconstructs the existing structure to recover 80–90% of its materials, and rebuilds a larger home (+10% footprint, +1 story per cycle) using those recovered materials. The rebuild creates real, physical equity — not market appreciation — and the homeowner can choose to keep building. That choice is the flywheel. Every house that moves through the loop becomes ground-truth data: a fully specified construction sequence that is, itself, the most valuable product ML Systems makes.
Loan Origination → Deconstruction → Construction ──┐
(Loan Pit) (80–90% recovery) (+10% SF, +1 story)
▲ │
└──────────── equity loop ────────────────────────┘
the homeowner chooses to keep building
Read the deep dives:
- 📍 The stage we're at — what's real today vs modeled vs aspirational
- 🔗 The Value Chain — Loan Origination → Deconstruction → Construction
- 📒 The Master Ledger — one home, one auditable record, five seats; claims reconciled in the open, stamps that lapse when the content moves
- 🧬 The Collective Ontology — how the system agrees on what a house is
- 🗜️ Ontological Compression — turning a whole house into a genome
- 🧠 The Seven Minds — the agents that read, reason, and stamp the ledger
- 🕸️ Neural-Net Architecture — the three-net ontology framework
- 📲 App Features — a screen-by-screen tour of the mobile client
- 📖 Glossary — every term in one place
Reading this as an AI/LLM? Start with
llms.txt— it's a curated map of this repo written specifically to give a language model the full background on ML Systems.
ML Systems is deliberately honest about confidence. Every claim in these docs carries one:
| Label | Meaning |
|---|---|
| MEASURED | Validated / exists today |
| MODELED | Calibrated projection — real math, not yet proven in the field |
| ASPIRATIONAL | A goal, deliberately not encoded in system logic |
If a doc says the deconstruction crane sequence is 2 days, it will say ASPIRATIONAL —
because no ML Systems deconstruction has been performed yet. This labeling is a core
design principle, not a disclaimer.
| Public (this repo) | Private | |
|---|---|---|
| Mobile UI | ✅ src/app (screens), src/components |
— |
| Concepts & docs | ✅ docs/ |
— |
| Backend API (tRPC) | ❌ | ✅ |
| Database schema & data | ❌ | ✅ |
| Ontology / ledger / compression engines | ❌ (described in docs) | ✅ @ml-systems/types |
| Secrets, keys, service accounts | ❌ never | ✅ (env only) |
The UI source in src/ imports from private modules (@/lib/*, @ml-systems/types).
It is published as a readable reference of the product's front end, not a runnable
build. The proprietary logic it calls into is the private engine.
- Mobile: Expo / React Native, Expo Router, NativeWind (Tailwind), TypeScript
- Auth: Clerk (custom domain SSO)
- API: tRPC (private)
- Data: Postgres / Drizzle (private)
- On-device intelligence: Claude + Gemini vision for facade/sketch/roof reasoning
| Hub | AI | Value Chain | Equity |
|---|---|---|---|
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 |
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| Add a home | Store | Neighbors | The Minds |
|---|---|---|---|
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- Founder / Owner / Language Modeler: Sal
- Industry: Construction — NAICS 236115
- Location: Rhode Island, USA
- Web: https://mlsystemsri.com
- Tag line: Tougher Problems Inspire Creative Solutions
Long-form technical writing on the platform — the Master Ledger, the Seven Minds, agent cost and coordination — is published on:
- ✍️ dev.to — https://dev.to/salparvez (canonical for most posts)
- 📰 Hashnode — https://mlsystems.hashnode.dev (ML Systems Engineering)
- 📝 Medium — https://medium.com/@salparvez
Recent posts:
- Claims, Not Facts: Building an Auditable Multi-Author Record for a House — the Master Ledger, domain-scoped authority, multiverification, lapsing signatures
- My AI agents don't talk to each other — the Seven Minds
- I stopped storing facts and started storing claims
- Agent Costs Are Hard to Predict Because We Keep Building Them to Be Unpredictable
- The Advice Was "Build on Unique Data." Mine Was a House.
Company channels: X · LinkedIn · Instagram · Product Hunt · Facebook
© ML Systems LLC. All rights reserved. See LICENSE. This repository is published for reference and transparency; it is not open-source and grants no license to use, copy, or redistribute the code or concepts.