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James Bower

Quantitative AI Security

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James Bower

Quantitative AI Security

Newsletter

Quantitative AI Security
A weekly build log.

I’m building Detect-Forge in the open: an AI security company with zero employees, one person and an AI agent workforce, in a category where funded startups have already raised $10M to $85M. Six months of runway. Can it build a real competitor, and prove it in public?

The company is the story. The newsletter is the build log. The repo is the receipt.

What you’ll watch, every week

The product. Does AI-validated detection engineering actually work? detect-forge, the closed loop, and the research underneath it, shipped as open-source tools you can run.

The company. Can agents replace headcount? Every system shipped is an episode. The agent org chart is the org chart.

The customer. Who pays first, and why? The audit chase, the readouts, the retainers. Win or miss, it gets reported.

The numbers. Readers, stars, income, hours. Published every week, including the ones that hurt.

The standard

Every claim gets four questions, the field’s and mine. Can I validate it? Can it even be validated? Where’s the code proof? Is it feasible at scale, in a real security environment? Papers, vendor claims, and benchmarks get reproduced and graded, and everything Detect-Forge ships is held to the same rubric. Every post ends with a verdict: holds, doesn’t hold, unvalidatable as published, no code, lab-only, or deployable.

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    Issues

    #1, September 13, 2026: Announcing Quantitative AI Security.

    The baseline

    September 13, 2026: 40 readers, 0 stars, $0 revenue. Every issue updates these.

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    • Detection Rule Staleness: How AI Can Solve This Now
    • How to Think About Low-Latency Development with Python
    • Managing Noise In AI Security Machine Learning
    • Dimensionality Reduction: Find the Best Method for Your Data
    • Improved Anomaly Detection in Cybersecurity using Model Cascading

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