Why I Built My Own AI Assistant (And Why You Might Too)

Six kids. Four school drop-offs by 7:20 AM. A family business. Freelance clients. Multiple calendars, task systems, and email accounts fighting for my attention.

I thrive in chaos. What I'm terrible at? Follow-ups. Remembering deadlines until they're breathing down my neck. The details that slip through cracks I didn't know existed.

For years, I threw tools at the problem: Todoist, ClickUp, multiple calendars, reminder apps, push notifications, handwritten notes. Each one promised to help me stay on top of everything.

Instead, they multiplied the places where things could hide. More tools meant more information overload, not less.

The pattern was always the same: focus on whatever was screaming loudest—the tyranny of the urgent—while everything else piled up until it started screaming too.

What I needed wasn't another productivity app. I needed a personal assistant. Something that could proactively surface what mattered, remind me of commitments before they became emergencies, and help me shift from reactive firefighting to intentional planning.

I don't have the budget for a human assistant. But I'm a developer with AI know-how. And in late 2025, the tools to build something like this finally existed.

This is the story of how I built ARIA, my personal AI assistant, and what I learned about building versus buying, integrating AI into your workflow, and why sometimes the best tool is the one you make yourself.

A Day in the Life

6:00 AM: Wake up. Get four kids ready for school. Pack lunches. Check homework. Sign permission slips. Out the door by 7:20, back by 8:00.

Clean up breakfast. Get my oldest set up for homeschool. Then: work.

I'm Director of Technology at Newton Institute, our family business. I manage platforms, oversee a junior developer, and juggle strategic decisions alongside hands-on coding. My work happens in fragmented blocks: 9 AM to noon, 1 PM to 2:45 PM, then 3:20 PM to 4:30 PM after school pickup.

Between those blocks? Soccer practice. Volleyball. Church activities. Dinner. Homework help. Bedtime routines.

After the house quiets down, I switch contexts again: freelance AI consulting for entrepreneurs building MVPs. Client calls. Code reviews. Prototypes.

Somewhere in there, I'm supposed to work out and sleep.

The challenge isn't volume. I can handle a lot. The challenge is context switching and the sheer surface area for dropped balls.

Every role has its own communication channels:

Work email (Outlook)
Personal email (iCloud)
Todoist for personal tasks
ClickUp for work development
Zoom for meetings
Multiple calendars
Apps from teachers, coaches, church coordinators

Each one is a place where something important might be hiding. Each one requires me to remember to check it.

And I did miss things. Not catastrophically, but enough to bother me. Follow-up emails that sat too long. Deadlines that snuck up. Commitments I forgot until someone reminded me.

The tools weren't the problem. The number of tools was the problem.

The AI Chatbot Promise (and Disappointment)

When ChatGPT launched, I was an early adopter. Same with Claude. I immediately saw the potential: AI that could help me work faster, think through problems, generate code, draft emails.

For those things, they were great. I used them constantly.

But they never helped with planning. They couldn't tell me what I should be working on. They didn't know about my calendar, my tasks, my commitments. They couldn't proactively remind me of anything.

They were assistants in name only.

Then Claude Code came out, and everything changed.

Suddenly I could execute arbitrary commands on my computer. Write scripts. Run CLI tools. Interact with my file system. This was the unlock—AI that could actually do things, not just talk about them.

But even Claude Code had limitations. Session-based. No persistent memory. Couldn't run in the background. Powerful tool, but not a personal assistant.

What I needed was something that combined conversational intelligence with deep integration into my actual workflow. Something that could remember context across sessions, proactively surface information, and actually manage my day-to-day life.

I needed to build it myself.

The Catalyst

The decision wasn't spontaneous. It was deliberate.

At Newton Institute, I'd been creating AI tools for our platform for years. But most AI integrations I saw felt lazy: jam ChatGPT into a product and call it "AI-powered." That wasn't interesting.

Then we built our first chat interface with real tool calling. Not a chatbot that pretended to be helpful, but one that could actually execute actions. Query databases. Update records. Trigger workflows.

That's when it clicked.

A chat interface with deep tool integration wasn't a novelty—it was genuinely useful. It could bridge conversational AI and real system access. It could be proactive, contextual, and actually get things done.

If I could build that for work, why couldn't I build it for myself?

Just before Christmas 2025, I started building ARIA. No grand architecture. No project plan. Just the things I needed most:

Todoist integration — Manage personal tasks
Email access — Read and search across accounts
Basic memory — Remember things across sessions
Media tracking — Connect to scripts I already had
ClickUp access — Pull in work tasks

Scrappy. Incremental. But it worked.

Then I Discovered OpenClaw

A couple weeks in, I started seeing buzz on X and YouTube. People were sending me links: "Have you seen this? OpenClaw just launched."

I pulled up the repo. Started reading the code.

Two immediate feelings: validation and frustration.

Validation because someone else had the same vision, a deeply integrated AI assistant that could actually manage your life. OpenClaw blowing up meant I wasn't crazy for thinking this was valuable.

Frustration because ARIA was struggling with tool calling reliability. Sometimes it worked perfectly. Other times it just... didn't. Wrong tools. Malformed parameters. I was debugging in isolation, trying different prompt strategies.

OpenClaw had already solved this.

So I did what any pragmatic developer would do: I started poaching.

First: OpenClaw's memory system. Clean pattern for persistent memory, daily logs plus curated long-term memory. I ripped out my rudimentary implementation and rebuilt it.

Next: their heartbeat and cron configuration. I'd been defining background jobs in code, hardcoded functions on timers. OpenClaw used JSON and markdown files, making it easy to modify without touching code. I migrated.

Then: config-as-files more broadly. Settings, skills, automation rules, all moved from database tables into editable files.

OpenClaw became my teacher. Read their code, understand the pattern, implement my own version.

But I didn't just copy. ARIA was solving different problems.

Where ARIA Diverged

While borrowing OpenClaw's backend architecture, I was building something they weren't focused on: a mobile app.

I wanted push notifications. A settings UI. The ability to interact with ARIA from my phone while driving to soccer practice or waiting in the pickup line.

So I built a React Native app with Expo. Push notifications. Full settings interface. Voice input and output.

The other major divergence: tool consolidation.

As I kept building, I hit a wall. 389 individual tools. Every calendar action was separate. Every email operation. Every task function. The LLM was drowning in options.

I shifted to a unified tool pattern. All calendar operations, Outlook and personal, went under a single calendar tool with an action parameter. Email became one tool with actions like get_inbox, search, send. Tasks, health data, media tracking, all consolidated.

389 tools became 40.

Then I added a skills system to filter even those 40 based on context. Core skills always load. Specialized skills (fitness tracking, gift planning, package tracking) only load when relevant.

The result: ARIA stays focused without being overwhelmed by irrelevant options.

Should You Build Your Own?

Honest answer: it depends.

Build your own if:

You're a developer who wants to understand how these systems work
You need deep integration with non-standard workflows
You want patterns you can apply to other software
You're willing to maintain infrastructure

Use OpenClaw (or similar) if:

You just want a working assistant
Standard workflows work for you
You don't want to maintain code
You value stability over customization

My recommendation? Do both. Run them side-by-side. Learn from OpenClaw's architecture while experimenting with your own ideas. That's what I did, and it accelerated everything.

There's no wrong answer. The best tool is the one that actually helps you get things done.

What's Working (and What's Next)

Today, ARIA handles most of my day-to-day reliably. Calendar management. Surfacing important emails. Tracking habits. Reminding me of commitments before they become emergencies.

What's next? I want to funnel all my Claude Code usage through ARIA. Instead of manually spinning up sessions for development work, ARIA would orchestrate them, managing multiple sessions, tracking context, coordinating between them.

At some point it stops being a personal assistant and starts being a development workflow tool. That's where I'm headed.

The Takeaway

I built ARIA because existing tools couldn't give me what I needed: proactive assistance deeply integrated with my actual workflow.

I didn't reinvent everything. OpenClaw taught me architecture. My own needs drove the features. The combination gave me something I couldn't buy off the shelf.

If you're drowning in tools and tired of playing catch-up, maybe it's time to build your own. Or at least crack open one of the open source options and see how they work.

That's what ARIA does for me. And that's why I built it.