What are this project's goals? How will you achieve them?

The Goal: To finalize and deploy Project Eclipse—a fully offline, air-gapped AI vulnerability auditing oracle running on local 70B parameter models. The objective is to eliminate the catastrophic systemic risks of centralizing enterprise DevSecOps and code auditing through cloud-based APIs (which introduces risks of zero-day data leakage, model poisoning, and reliance on black-box corporate infrastructure).

How I will achieve it: I have already engineered the core architecture. I successfully built a localized Polyglot RAG pipeline and a LanceDB vector vault that actively processes thousands of DevSecOps documents and CVEs using smaller, quantized models (like 7B Qwen Coder). I am currently hitting a hardware limit. By acquiring a unified-memory Mac Studio M4 Max, I will break my consumer-grade VRAM bottleneck, ingest 70B models into the existing vault, optimize the offline reasoning loops, and open-source the finalized defense architecture.

How will this funding be used?

I am requesting a total of $16,000 to bridge this project from prototype to deployment over the next 4 months.

• $3,700 for Hardware: Acquisition of an Apple Mac Studio M4 Max (16-core CPU, 40-core GPU, 128GB Unified Memory). This specific machine is required to bypass standard multi-GPU VRAM bottlenecks and run highly quantized 70B models completely offline.

• $12,300 for Runway: A living stipend for 4 months ($3,075/month) in the Washington D.C. area. This replaces my commercial contracting income and allows me to dedicate 100% of my time to finalizing this open-source safety infrastructure and establishing my sovereign AI defense LLC.

Who is on your team? What's your track record on similar projects?

I am a solo independent operator and self-taught ML engineer transitioning from a 15-year career in high-stakes, "zero-fail" operational risk management and logistics. I know how complex systems fail in the real world.

Technically, my track record is built on extreme self-reliance. I recently engineered "Project Diamond," a complex, predictive ML engine that parses 11 years of NCAA data utilizing temporal knowledge graphs and Cartesian merge techniques to eliminate look-ahead bias (currently yielding a 52.7% accuracy rate and 12% ROI in backtesting). Prior to that, I built "Project Sentinel," a locally hosted digital perimeter and compliance factory. I have proven I can build the complex ETL pipelines and vector databases; I just need the compute to scale the reasoning.

What are the most likely causes and outcomes if this project fails?

Cause of Failure: The most likely technical failure mode is that running highly quantized 70B models on a single Mac Studio still yields inference latency that is too slow for practical, real-time enterprise DevSecOps pipelines. Alternatively, the context window required to parse massive code repositories alongside the RAG retrieval data could degrade the model's reasoning capabilities.

Outcome of Failure: If the 70B monolithic approach proves too slow or degraded, the fallback is an ensemble approach. I will pivot the architecture to orchestrate multiple highly-specialized, smaller models (8B-14B parameters) managed by a local routing layer. The project will still succeed in its primary goal—remaining fully air-gapped and offline—but it will trade the deep reasoning capabilities of a singular frontier-level model for the speed and efficiency of a localized swarm.

How much money have you raised in the last 12 months, and from where?

$0. I have completely self-funded my research, training, and infrastructure up to this point through personal capital and commercial contracting. I currently have pending proposals deployed to the Long-Term Future Fund (EA Funds), BlueDot Impact (Rapid Grants), and Coefficient Giving (Open Philanthropy), but no funds have been awarded yet.