05 module

Clustered evidence stack.

Systems and platform engineering lead. Accelerated compute groups CUDA with C++, Rust, matrices, tensors, and GPU scheduling. Web delivery remains the final supporting layer.

01 / systems

Systems core

Assembly

Instruction-level reasoning, registers, memory, and machine behavior

practiced

C

Memory-oriented software, firmware perspective, and systems exercises

maintained

Rust

Ownership-driven systems design, safety, concurrency, and future WASM kernels

practiced

Linux

Primary operating environment for development, infrastructure, and self-hosting

maintained

Operating systems

Processes, memory, scheduling, filesystems, isolation, and failure behavior

practiced

Computer architecture

Hardware–software boundaries, execution, memory hierarchy, and performance

practiced

02 / platform

Platform and virtualization

Virtual machines

Workload isolation, lab environments, snapshots, and reproducible systems

maintained

Docker

Portable service packaging and repeatable development environments

maintained

Containers

Process isolation, images, networks, volumes, and deployment boundaries

maintained

Networking

DNS, routing, service boundaries, reverse proxies, and operational debugging

practiced

Self-hosted lab

Directly observable infrastructure for controlled experimentation

maintained

Reliability

Failure-aware design, recovery paths, monitoring, and operational clarity

maintained

03 / compute

Accelerated computing

CUDA

GPU execution model, memory hierarchy, scheduling, and tiled kernels

exploring

C++

RAII, performance-aware abstractions, native kernels, and tensor structures

practiced

Rust for compute

Safe host orchestration and portable compute implementations

exploring

Matrices

Shape, stride, layout, traversal, multiplication, and numerical checks

practiced

Tensors

Broadcasting, views, accelerator-aware layouts, and execution graphs

exploring

GPU scheduling

Launch geometry, occupancy, resource constraints, and workload placement

exploring

04 / backend

Backend and cloud engineering

Backend engineering

APIs, service boundaries, data flows, security, and operational behavior

maintained

SQL

Evidence records, learning history, relational modeling, and auditable queries

practiced

JSON

Portable contracts, configuration, schemas, and benchmark artifacts

practiced

Cloudflare

Primary edge deployment target behind portable application contracts

built

AWS

Planned compute, GPU, and object-storage adapter path

exploring

Google Cloud

ML platform experience and future GPU/backend adapter path

practiced

Firebase

Planned OAuth implementation behind an application-owned auth contract

practiced

Git and CI/CD

Versioned systems, automated validation, reproducibility, and delivery

maintained

05 / ai

AI infrastructure

AI systems

Observable, grounded, governed model infrastructure

practiced

ML pipelines

Reproducibility, deployment, monitoring, lineage, and feedback loops

practiced

LLM infrastructure

Retrieval, evaluation, tool use, isolation, and lifecycle controls

practiced

AI governance

Auditability, human decision loops, adversarial constraints, and policy

exploring

06 / web

Web delivery

HTML

Semantic delivery surface for systems content

built

CSS

Responsive presentation without a heavy client framework

built

JavaScript

Progressive interaction and future lab coordination

practiced

Astro

Portable content build with minimal browser runtime

built