Engineering thought leadership, AI architecture, and leadership articles — plus finance notes on markets and discipline. Published on LinkedIn and expanded here.
Four articles that best represent what I build and how I think.
AI Engineering Series · Part 1 of 10
A practical guide for .NET engineers moving from chat prompts to RAG, MCP servers, agents, and agentic workflows — with security patterns, architecture diagrams, and platform mental models.
AI Engineering Series · Part 2 of 10
Embeddings convert meaning into vectors; vector databases make those vectors searchable. Together they form the retrieval foundation behind RAG, semantic search, and enterprise AI assistants.
AI Engineering Series · Part 4 of 10
Tutorial: expose product catalog search to AI hosts with the ModelContextProtocol NuGet package — compare manual HTTP integration vs MCP tools using enterprise domain models and Cursor setup.
Kubernetes Series · Part 1 of 17
A practical, no-fluff explanation of Kubernetes for software engineers — what problem it solves, how Pods, Nodes, and the control plane work, with finance, C#, and AWS examples.
Learn the 3-5-7 rule in trading as a simple risk management framework for position sizing, portfolio exposure, and avoiding concentrated losses.
Pick a track and explore it end to end.
Hand-picked highlights across AI, cloud, and finance.
AI Engineering Series · Part 3 of 10
Standard RAG, Graph RAG, and Agentic RAG all connect LLMs to private data — but they solve different problems. Match the architecture to the question: retrieval, relationships, or reasoning.
Kubernetes Series · Part 16 of 17
An MCP server is just an HTTP service — run it on Kubernetes as a Deployment and Service with probes, autoscaling, and Secrets Manager. The MCP-specific decisions explained.
AI Engineering Series · Part 6 of 10
Understand MCP tools, resources, and prompts with practical examples for AI agents, enterprise workflows, and developer productivity.
Kubernetes Series · Part 15 of 17
Beyond YAML: the reconciliation model, resource requests, failure design, security, scaling, and cost trade-offs that staff engineers are expected to reason about.
Finance-specific MCP patterns — equity search, portfolio resources, order tools with approval gates, and audit-friendly error handling for regulated workloads.
Kubernetes Series · Part 11 of 17
Managed Kubernetes on AWS for .NET engineers — how EKS, ECR, IRSA, load balancers, and Secrets Manager map to the Kubernetes concepts you already know.
AI Engineering Series · Part 7 of 10
Learn what MCP resources are, how they differ from tools, and how to use them for logs, portfolios, documents, APIs, and enterprise AI workflows.
The traditional model repeats the same roles across siloed domain teams. The AI-era model connects specialized squads through an Efficiency Team building shared MCPs, RAG pipelines, agents, and agentic workflows.
AI Engineering Series · Part 2 of 10
Embeddings convert meaning into vectors; vector databases make those vectors searchable. Together they form the retrieval foundation behind RAG, semantic search, and enterprise AI assistants.
AI Engineering Series · Part 3 of 10
Standard RAG, Graph RAG, and Agentic RAG all connect LLMs to private data — but they solve different problems. Match the architecture to the question: retrieval, relationships, or reasoning.
AI Engineering Series · Part 6 of 10
Understand MCP tools, resources, and prompts with practical examples for AI agents, enterprise workflows, and developer productivity.
AI Engineering Series · Part 9 of 10
A practical explanation of what MCP connectors expose to AI clients: tools, resources, prompts, and how to think about capability support.
AI Engineering Series · Part 7 of 10
Learn what MCP resources are, how they differ from tools, and how to use them for logs, portfolios, documents, APIs, and enterprise AI workflows.
AI Engineering Series · Part 8 of 10
Understand MCP prompts, reusable AI workflow templates, and how they differ from tools and resources in Model Context Protocol servers.
Kubernetes Series · Part 1 of 17
A practical, no-fluff explanation of Kubernetes for software engineers — what problem it solves, how Pods, Nodes, and the control plane work, with finance, C#, and AWS examples.
Kubernetes Series · Part 2 of 17
Docker packages one container; Kubernetes runs many across machines. A clear, practical explanation of how they fit together, with finance, C#, and AWS examples.
Kubernetes Series · Part 3 of 17
The three Kubernetes objects you touch every day — Pods, Deployments, and Services — explained clearly with finance, C#, and AWS examples.
Kubernetes Series · Part 4 of 17
Trace one kubectl apply command through the API server, etcd, controllers, scheduler, and kubelet — so debugging becomes a step-by-step checklist.
Kubernetes Series · Part 5 of 17
A memorable mental model for Kubernetes architecture: the control plane is the tower, Nodes are runways. Understand every component and how they fail.
Kubernetes Series · Part 6 of 17
How Kubernetes self-heals: the reconciliation loop plus liveness and readiness probes keep your services alive without paging anyone. With C# and AWS examples.
Kubernetes Series · Part 7 of 17
How traffic reaches your Pods: Services, ClusterIP, Ingress, and cluster DNS explained without the iptables deep-dive. With finance, C#, and AWS examples.
Kubernetes Series · Part 8 of 17
Pods are disposable, so how does data survive? PersistentVolumes, PersistentVolumeClaims, StorageClasses, and StatefulSets explained, with EBS/EFS examples.
Kubernetes Series · Part 9 of 17
Helm is the package manager for Kubernetes. Learn what charts, values, and releases are, and why they tame YAML sprawl across environments.
Kubernetes Series · Part 10 of 17
The anatomy of a Helm chart — Chart.yaml, values.yaml, and templates — with a practical .NET service example you can actually ship to EKS.
Kubernetes Series · Part 11 of 17
Managed Kubernetes on AWS for .NET engineers — how EKS, ECR, IRSA, load balancers, and Secrets Manager map to the Kubernetes concepts you already know.
Kubernetes Series · Part 12 of 17
A clear decision framework for ECS vs EKS — simplicity versus the Kubernetes ecosystem — so you pick the right AWS container service instead of defaulting to one.
Kubernetes Series · Part 13 of 17
What CrashLoopBackOff means and a fast, repeatable routine to debug it — logs, events, exit codes, config, and probes — with C# and AWS examples.
Kubernetes Series · Part 14 of 17
A Pending Pod is a scheduling problem, not an app crash. Learn the causes — capacity, taints, unbound PVCs, affinity — and how to fix each on EKS.
Kubernetes Series · Part 15 of 17
Beyond YAML: the reconciliation model, resource requests, failure design, security, scaling, and cost trade-offs that staff engineers are expected to reason about.
Kubernetes Series · Part 16 of 17
An MCP server is just an HTTP service — run it on Kubernetes as a Deployment and Service with probes, autoscaling, and Secrets Manager. The MCP-specific decisions explained.
Kubernetes Series · Part 17 of 17
AI agents run on Kubernetes as ordinary workloads — Deployments, Services, HPAs — plus queues for long jobs and GPU nodes for self-hosted models. With finance examples.
AI Engineering Series · Part 10 of 10
Platform engineering guide to MCP transport choice — stdio for Cursor, HTTP for production containers — plus human-in-the-loop approval before state-changing tools.
AI Engineering Series · Part 5 of 10
Hands-on C# patterns for enterprise MCP servers — support ticket tools, customer profile resource URIs, documentation prompts, and contract testing with platform-domain examples.
AI Engineering Series · Part 4 of 10
Tutorial: expose product catalog search to AI hosts with the ModelContextProtocol NuGet package — compare manual HTTP integration vs MCP tools using enterprise domain models and Cursor setup.
AI Engineering Series · Part 1 of 10
A practical guide for .NET engineers moving from chat prompts to RAG, MCP servers, agents, and agentic workflows — with security patterns, architecture diagrams, and platform mental models.
Learn the 3-5-7 rule in trading as a simple risk management framework for position sizing, portfolio exposure, and avoiding concentrated losses.
Finance-specific MCP patterns — equity search, portfolio resources, order tools with approval gates, and audit-friendly error handling for regulated workloads.
How to move past "that's not how we do it here" — reinvent processes, share business problems openly, make failure acceptable, balance accountability with experimentation, and keep an open mind for ideas from anywhere.
When stakeholders fall in love with a legacy product, sunsetting becomes emotional — but the best product leaders know when to pull the plug using cost, customer satisfaction, technology age, and market fit.
Following Scrum ceremonies without an agile mindset is a car without an engine. Focus on sprint goals, fail-fast culture, and retrospectives that actually change behavior.
Mid-sized teams can build secure, self-hosted AI assistants with open-source models, vector stores, and RAG — without routing proprietary data through third-party APIs.
Before you dial a customer care number, Google Phone already analyzed the business, estimated wait time, and surfaced busy-hour patterns — practical AI embedded in everyday workflows.
Literacy in software engineering is not just writing code — it is reading others' code, debugging, and explaining logic in natural language.
Twitter's engineering team cut code from 700k to 70k lines, moved infrastructure on-prem, and reduced bills by 60% — proof that a small motivated team with clear direction can solve complex problems.
90% of traders focus on picking stocks; 10% focus on protecting capital. A simple framework: 3% risk per trade, 5% portfolio drawdown cap, 7:1 profit-to-loss ratio.
A timeless budgeting framework: 50% needs, 30% wants, 20% savings and investments. Consistency beats complexity.
The most expensive words in investing: "I should at least get my money back." When a stock drops 50%, it must double just to break even.
Everyone quotes Apple, Tesla, and Infosys — but for every multi-bagger, dozens quietly disappeared. Over 70% of U.S. listed companies from 1980 no longer exist.
A 12-hour AWS outage created a new data point for traders — revealing single-provider dependency and which companies engineer redundancy.
Most investors plan stop-losses but not profit-taking. A sell-limit order captures gains automatically — decide your exit before the market decides for you.
NVIDIA crossed $5 trillion — roughly 8% of the S&P 500. Index funds are becoming more concentrated in a few names, whether investors intend it or not.
Savvy investors invest to reach goals — buying a home, funding education, retirement. Purpose brings patience; every rupee or dollar links to a destination.
Flight disruptions rearrange demand across industries — car rentals, cruise lines, energy, and regional tourism benefit while aviation slows.
Geopolitical uncertainty widens short-term outcomes. Edge comes from patience, disciplined risk management, and waiting for clean setups — not every headline deserves a reaction.
Periods of geopolitical tension create the largest price dislocations. The edge is not predicting every move — it is preparation and risk management.
Q1 2026 was brutal for indices — S&P -6.53%, Nasdaq -9.26%. A disciplined trading process delivered +8.13% through shorter holds, fast profit booking, and sitting in cash when there was no edge.
Allbirds pivoted to AI and its market cap jumped 7x in 24 hours — with no product launch. We've seen this pattern before: dot-com, blockchain, now AI.
Poker teaches that real edge is knowing when not to buy. Fewer trades, better entries, patience over FOMO — markets reward the same discipline.
Dot-com, housing boom, AI excitement, SpaceX IPO — technology can reshape decades, but great technology does not automatically equal a great investment at any price.
