Sep 2022
↓ Present
Founder & Technical Lead
Hackerware LLC
Security-focused applied AI lab. Builder-operator across architecture, implementation, deployment, and iteration — bare metal to paid Pro tier on the VS Code Marketplace.
// Project — R-01.01
Continuity — Persistent memory and runtime governance for AI-assisted development.
- Identified the core failure mode: AI assistants produce inconsistent output because architectural decisions, constraints, and rationale are lost between sessions. Treated context as infrastructure, not convenience.
- Designed a bounded-retrieval middleware that decouples per-turn token consumption from total stored knowledge. Hard-ceilinged top-K (default 3, max 10) with per-record character caps. At n = 2,189 decisions, worst-case per-turn injection is ≈17,500 tokens against a static-embedding baseline of ~485,000 — a ~96.4% reduction that holds as the corpus grows.
- Shipped a five-layer defense-in-depth credential-scrubbing architecture for the Memory Amplifier threat — credentials that enter AI context once and get re-injected into every future session through persisted memory. Five enforcement boundaries (input, AI output, persistence write, persistence read, tool result) consuming a shared idempotent scrub primitive. 27 provider-specific patterns plus a Shannon-entropy fallback calibrated to 4.5 bits/char (measured 100% recall, 0% high-confidence FP).
- Built the MCP security interception layer that validates and gates tool execution before LLM-initiated actions reach the file system or external services — enforcement boundary, not policy document. Intercept → Pause → Evaluate → Enforce pipeline operating under a Compromised Context threat model.
- Designed human-in-the-loop correction model: corrections stored as scoped annotations with timestamps, not global overrides. Prevents contamination of unrelated future outputs and resolves conflicts deterministically (narrower scope wins within applicability window).
- Drafted the full patent portfolio — Patent 1 (bounded retrieval + Governance Lock), Patent 1 CIP (multi-signal relationship enforcement with Markov decision-chain prediction), Patent 2 provisional (defense-in-depth credential scrubbing) — including §101 Alice arguments, §112 enablement, and OWASP LLM Top 10 (2025) controls mapping across nine categories.
- Security-first defaults: 100% local storage, zero telemetry, on-device vector index. Develops against a self-hosted inference stack (Ollama on TrueNAS SCALE) using the same MCP protocol it ships to users on Anthropic, Gemini, or OpenAI — backend is swappable, application code is identical. Shipped to VS Code Marketplace October 2025; active paid users in production.
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// Project — R-01.02
RedArchives — Tamper-evident digital evidence platform.
- Designed for environments where evidence must survive adversarial challenge, insider threats, and legal scrutiny over decades. Mission: preserve documentation of war crimes and human rights violations with cryptographic integrity that doesn't depend on trusting the platform operator.
- Built layered integrity architecture (Artifact Ingestion → Provenance Ledger → Metadata Store → Verification Layer → Presentation Layer) with explicit separation of duties between storage, verification, and presentation.
- Implemented blockchain-anchored cryptographic fingerprinting at ingestion; fingerprints cover normalized metadata, not just content, preventing semantic rewriting without detection. Designed for algorithm agility and re-verification paths to address long-term cryptographic decay.
- Modeled explicit adversarial threats: post-hoc tampering, chain-of-custody disputes, insider risk, temporal attacks, selective disclosure, platform trust collapse. Same integrity discipline informs how I design trust-critical AI systems (training data provenance, evaluation artifacts, feedback integrity).
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