Building Metric-Mint: Reversing the Attention Economy with Manifest V3 and Local ONNX

The current attention economy operates on a closed loop: Server pushes high-arousal rage-bait or graphic distress -> Client displays it -> User lingers or reacts -> Server updates your behavioral profile vector to serve more trauma.

Traditional content blockers try to break this at the Network Layer by dropping requests or tweaking API payloads. This triggers platform defense protocols—certificate pinning issues, strict rate limits, or account bans for ToS automation violations.

Project Metric-Mint cuts the loop at the DOM instead of the Network Layer.

The architecture is simple:

1. Normal HTTPS handshakes happen. The platform's ad trackers and metric pipelines record a successful impression and normal dwell time. The platform thinks its engagement trap worked.
2. Once the text and image nodes land inside the client DOM, a Manifest V3 content script intercepts them before visual rendering.
3. The content is fed to a background service worker running offline inside browser RAM via Transformers.js or ONNX Runtime. It screens for specific psychological profiling vectors (violence, hyper-polarized rage, etc.).
4. The item is logged into a local IndexedDB ledger, and the metric counter increments. The screen remains completely clear (or blurred).

The core constraint: 0 outbound network calls. The application is completely blind to the outside world, making it impossible to trace or flag as a corporate scraper.

The long-term objective is building a decentralized consumer data union. Right now, tech platforms farm human attention like cattle because a destabilized, high-cortisol brain is mathematically easier to monetize with targeted ads. Metric-Mint lets users own the cryptographic proof of this systematic exploitation.

Once adoption reaches critical mass, the spec introduces a local API allowing users to securely pool and sell these anonymized, aggregated toxicity metrics directly to brand-safety advertisers via differential privacy. Advertisers spend billions to prevent their brands from rendering next to graphic violence and hate speech; this cuts out the tech monopolies entirely and puts the financial leverage back into the user's hands.

Looking for feedback on the spec and open-source contributors to spin up the initial tracks:

• Resilient MutationObserver loops capable of chasing obfuscated/dynamic React deployment wrappers (X, Instagram, TikTok).
• Benchmarking local multi-modal classification models to keep per-node latency under 25ms to avoid layout stutter.
• Local dashboard UI and offline data export scripts.

The specification and code are completely open-source under the GPL-3.0 license.

Repository: https://github.com/clownsh0e22/Metrc_Mint