Welcome to Tokens & Traces. This blog is a technical notebook focused on machine learning mechanics, ML-driven information security solutions, autograd engines, and high-performance systems engineering.

Why “Tokens & Traces”?

Modern technical work increasingly sits at the intersection of statistical models and deterministic systems:

• Tokens: Representing the discrete units of natural language processing, transformer context windows, and generative model weights.
• Traces: Representing stack traces, execution flows, memory alignments, network telemetry, and system log telemetry.

Here, I publish deep dives, empirical benchmarks, and code walkthroughs.

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What to Expect

Articles on this site fall into three core categories:

1. Machine Learning Mechanics: Deconstructing attention routing, transformer interpretability, and autograd backpropagation engines.
2. Infosec ML Solutions: Building ML-driven threat detection models, intelligent log analysis pipelines, and security analytics tooling.
3. Systems & Performance: Building lightweight runtimes in C++ and Python, memory alignment optimization, and high-throughput daemons.

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Code & Mathematical Formulations

Technical posts may include code snippets and explicit mathematical derivations:

def scalar_autograd_example():
    """Example autograd forward pass."""
    x = 2.0
    w = 3.0
    b = 1.0
    out = x * w + b
    return out

$$\mathcal{L}(\theta) = \frac{1}{N} \sum_{i=1}^N \ell(f(x_i; \theta), y_i)$$

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Stay Connected

You can follow along via the RSS Feed or check out my open-source code on GitHub.