Product systems
Full-stack
AI workflows
Applied
Delivery style
Builder-led
Software that feels fast, useful, and alive.
I build polished web products, backend systems, and practical AI workflows for teams that need momentum without duct-taping the future together. Clean interfaces, solid architecture, real outcomes. Wild concept, apparently.
Live Greeting from Nathan's AI
Agent demo
LiveSee a plain model become an agent with controlled tools.
A traditional LLM works from the information available to it: training data, instructions, and the context you provide. It can reason, write, summarize, and explain, but it cannot search the web, check live systems, or interact with the outside world by itself.
The useful part is the software around the model. Builders like me connect LLMs to real systems: APIs, databases, files, search, calendars, dashboards, and workflows, while keeping control over what the model can request and what actually runs.
This demo shows that idea with two simple tools: live web search and weather lookup. Turn on Agent Mode, ask something current, and watch the runtime validate the request, run the tool, trace the flow, and feed fresh context back into the final answer.
Capabilities
Build the product, wire the systems, give the AI a real job.
The sweet spot is practical: interfaces people like using, services that do not fall over, and automation that removes actual drag instead of becoming another toy dashboard.
Product engineering
Next.js, React, TypeScript, mobile surfaces, dashboards, admin tools, and practical UI systems that can survive real users.
Backend and cloud
APIs, data models, auth, S3, CloudFront, PostgreSQL, Prisma, workers, integrations, and the boring reliability work that actually matters.
AI implementation
Private AI adoption, prompt systems, agent tooling, evaluation loops, OpenAI/Anthropic APIs, and internal automation that earns its keep.
Stack
Familiar tools, sharp edges included.
This is the working set I reach for when the job calls for fast iteration, clean interfaces, and systems that have to keep behaving after launch day.
About
Builder first, developer second.
class Nathan {
role: string;
vibe: string;
caffeineLevel: string;
constructor() {
this.role = "Full-Stack Engineer";
this.vibe = "builder-first";
this.caffeineLevel = "dangerously optimized";
}
frontend(): string[] {
return ["React", "Next.js", "TypeScript", "Tailwind", "Three.js"];
}
backend(): string[] {
return [
"Node.js",
"Python",
"FastAPI",
"Flask",
"Django",
"Ruby",
"Rails",
"Postgres",
"Prisma",
"Supabase",
];
}
aiStack(): string[] {
return [
"OpenAI APIs",
"Anthropic APIs",
"MCP",
"RAG",
"Vector Search",
"LangChain",
"Hugging Face",
"Ollama",
"llama.cpp",
"Prompt Systems",
"OpenClaw",
"Cursor",
"Claude Code",
"OpenCode",
"GitHub Copilot",
"Codex",
];
}
ship(feature: string): string {
return `${feature} shipped with tests, telemetry, and minimal drama.`;
}
debug(issue: string): string {
return `Fixing ${issue}... probably with logs, coffee, and spite.`;
}
}I like making things that feel clean, fast, and just a little bit cursed in the best way.
Sometimes that means shipping polished product work. Sometimes it means disappearing into an idea at 1 a.m. and coming back with something surprisingly real.
This site is home base for all of it: projects, experiments, Twitch, side quests, and whatever weird thing I'm building next.
Featured blog
Fresh notes from the build log.
ERP, AI, and the Microsoft Dynamics 365 advantage
A practical look at what ERP systems do, which platforms matter, and how Dynamics 365, Microsoft Graph, Copilot, and workflow agents can connect business data to real action.
RIP Oliver Tree
Oliver Tree's sudden death feels unreal. A quick, human note on the shock, the weirdness, and why his music hit people so hard.
AI-First Search Is Here. The Web Will Not Be the Same.
Google’s AI-first search shift means the web is moving from pages and links to generated interfaces and agent actions. Here are my predictions for what breaks, what survives, and what smart builders should do next.
Support
If the work helps, you can support future builds.
Contributions help fund tooling, experiments, open resources, and deeper AI/dev systems content. Totally optional, always appreciated.
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