Software Architect | AI Systems Researcher | Distributed Systems | Technical Writer | Open Source Builder
I work at the intersection of AI systems, distributed software, cloud-native platforms, and practical software architecture.
Tools that give coding agents better context with fewer tokens.
| Project | What it does |
|---|---|
| catalogify | Builds a portable OKF knowledge base from code and git history, with checkable evidence, and keeps it current as the code evolves. pip install catalogify |
| logreduce | A single Rust binary that cuts huge logs down to an LLM token budget while keeping every error: 99% critical-signal recall on 35 real CI incidents. Hooks into Claude Code, Codex and Copilot. |
| harness-distill | Distills coding-agent sessions from Claude Code, Codex, Cursor, Copilot and others into AGENTS.md + HANDOFF.md, so no decision stays trapped in one tool's memory. |
- Eval Driven Agentic AI Development: production-grade agentic AI with evaluation-driven development
- LLM-Trust-audit: black-box audits of LLM endpoints (change one detail that shouldn't matter, measure what changes), with paper, harness and run data
- SDD/Speckit_Limitations: why "the specification is the source of truth" conflicts with iteratively developed code
- Tiny Transformer: a small language model built to explain transformers as simply as possible
- Data LakeHouse OS: Trino / Presto on Kubernetes with a standalone Hive metastore and Postgres
My work spans:
- AI systems, neural networks, and transformers
- Distributed systems and Software architecture
- Radio Network Management Systems,SON, PathLoss, Wifi6/7
- Data platforms and Developer infrastructure
- Cloud-native engineering (Kubernetes,gRPC,ArgoCD etc)
- Technical writing, papers, and Open source projects
- Transformer-Based Surrogate Model for Accelerated Irregular Terrain Model Path Loss Prediction
- High-Throughput Terrain Retrieval for Radio Propagation Modeling Using Zarr-Based Chunked Storage
- The Mathematical Intuition Behind Deep Learning: From the Dot Product to Multivariate Calculus and the Jacobian, with Python Amazon In Kindle Amazon Intl
- Explaining Transformers as Simply as Possible through a Small Language Model
- CNN from Scratch
- Vibe Coding — Prompts Are All You Need?
- MCP — The Golden Key for AI Automation
- REST is not the Best for Micro-Services — GRPC and Docker makes a compelling case
- Exploring Large Language Models — Language Understanding, Task Generalization & Fine-tuning
- Why Specification-Driven Development (SDD) is Not a Silver Bullet for AI-Assisted SDLC
- Training / Fine-tuning a Masked Language Model (MLM)
- Leveraging Smaller LLMs for Enhanced Retrieval-Augmented Generation (RAG)
- Backpropagation Deep Dive — Back Propagation with Softmax Output
- Micro Services & Git — Mono/Multi Repo, Balancing Feature Development
- End to End Testing in MicroServices
- From Data Warehouse to Data Lake to Data Lakehouse
- Using Apache Cassandra — A Few Things Before You Start
Context engineering for coding agents, evaluation-driven AI development, practical transformer systems, distributed architecture, cloud-native platforms, and production-grade engineering workflows.
Views expressed here are my own and do not reflect those of my employer.
Open source work on this profile is independent and does not include any proprietary or confidential employer material.