Engineering teams & technical leads
Inspect architecture, boundaries, concurrency, integration, search behavior, and other engineering decisions behind a working implementation.
Technical work & execution evidence
Projects: from difficult problems to inspectable implementations
A curated record of engineering, algorithmic, and technical learning work—showing the problem, the reasoning, the implementation, and the evidence behind each result.
Product and system owners
See what kind of problem each project addresses, what was in scope, and what the resulting implementation actually provides.
Research & computational teams
Explore algorithmic, optimization, numerical, and AI projects where modeling and implementation need to remain connected.
What the projects demonstrate
Technical depth is visible in the path, not just the final screen
The case studies focus on the reasoning that turns a difficult requirement into an implementation that can be inspected and tested.
Architecture & reliability
System boundaries, modularity, distributed state, concurrency, failure semantics, and integration choices.
Algorithms & numerical methods
Mathematical formulation, solver design, optimization methods, numerical behavior, and algorithmic trade-offs.
AI & learning systems
Transformer mechanics, inference paths, generation, and implementation choices made explicit rather than hidden behind a high-level API.
Validation & evidence
Tests, benchmarks, diagnostics, documentation, and other project-specific evidence where they genuinely exist.
How to read a project
Every case study follows the same evidence path
The structure is consistent so a technical reader can move from the original problem to the implementation and its supporting evidence.
01
Problem & context
What needed to be solved, why it was difficult, and which constraints shaped the work.
02
Approach & decisions
How the problem was decomposed and which architectural, mathematical, or algorithmic choices were made.
03
Implementation
What was actually built, how the main parts fit together, and what remains intentionally outside the scope.
04
Result & evidence
What the project produces and which public sources, tests, benchmarks, or documentation allow the work to be inspected.
Technical directions
Browse projects by technical direction
Selected case studies
A few projects for deeper inspection
Case study
RateLimitEngine: rate limiting for .NET systems
A .NET 8 rate-limiting library with four algorithms, in-memory or Redis state, and explicit concurrency and failure semantics.
Case study
Elasticsearch Search Platform: engineering search and relevance
A search platform built with Elasticsearch for relevance, filtering, facets, pagination, and explainability.
Case study
Convex QP Solver: numerical engineering for constrained optimization
A Python solver for convex QP with interior-point, Mehrotra, numerical validation, and reference-solver comparison.
Project record
The complete published project record
5 projects
Find content
Search the project record
Search across project titles, descriptions, outcomes, technical directions, and related services.
5 projects
Software architecture & reliability
RateLimitEngine: rate limiting for .NET systems
A .NET 8 rate-limiting library with four algorithms, in-memory or Redis state, and explicit concurrency and failure semantics.
Software architecture & reliabilitySearch & information retrieval
Elasticsearch Search Platform: engineering search and relevance
A search platform built with Elasticsearch for relevance, filtering, facets, pagination, and explainability.
Optimization & numerical computing
Convex QP Solver: numerical engineering for constrained optimization
A Python solver for convex QP with interior-point, Mehrotra, numerical validation, and reference-solver comparison.
Optimization & numerical computingSoftware architecture & reliability
Event-Driven Metaheuristic Optimization: dynamic resource allocation & scheduling
A Python project for dynamic resource allocation and scheduling with GA and Differential Evolution, warm-start adaptation, constraint handling, events, and reproducible benchmarks.
AI & learning systems
HowLLMsWork: building Transformer/LLM mechanics from scratch
A Python/NumPy educational implementation that traces tokenization, next-token prediction, training, sampling, and KV-cache inference without hiding the mechanics behind a high-level framework.
Public evidence
Inspect the implementation in the source repositories
The case study is the interpretation layer; the public GitHub repositories are where the implementation and project documentation can be inspected.
From project evidence back to the problem
Each project starts from a concrete problem and constraints. Use the related links on a case study to move from technical evidence back to comparable problems and services.