One month ago, Browsing.AI didn't exist.

Today, we have a live landing page, a growing waitlist, and a clear vision for what we're building. This post is the first in a series where we share everything—the wins, the mistakes, and the decisions that shape this product.

Building in public isn't comfortable. But we believe transparency builds trust, and trust is everything when you're asking people to let software observe their browsing habits.

Here's how Month 1 went.

The Problem That Started Everything

It began with a spreadsheet.

I was tracking my own productivity manually—logging when I started deep work, when I got distracted, which sites pulled me off task. After two weeks, the data was eye-opening: I was losing nearly 2.5 hours every day to unintentional browsing.

Not breaks. Not intentional research. Just... drift.

The pattern was always the same:

Open a new tab to look something up
Notice an interesting link
Follow it
Emerge 40 minutes later wondering what I was doing

Sound familiar?

I looked for tools to help. RescueTime tracks time but feels like surveillance. Browser blockers are too rigid—they don't understand that YouTube might be work (tutorials) or distraction (random videos). Screen time apps are built for phone addiction, not knowledge work.

The gap was clear: No tool existed that could intelligently understand context, respect privacy, and help knowledge workers without making them feel watched.

That's when the idea for Browsing.AI clicked.

Key Decisions in Month 1

Every early-stage product is shaped by a few pivotal choices. Here are the ones that will define Browsing.AI:

Decision 1: Privacy-First, No Exceptions

Most productivity trackers send your data to servers. Your browsing history, your work patterns, your habits—all sitting in someone else's database.

We rejected that model completely.

Browsing.AI processes everything locally. Your data never leaves your machine. The AI runs on-device. We literally can't see what you're doing, even if we wanted to.

This complicates development. Local-first is harder to build. But it's the only approach that feels right for a tool that sees everything you browse.

Privacy isn't a feature. It's the foundation.

Decision 2: AI That Understands Context

Here's what makes existing tools frustrating: they can't tell the difference between productive YouTube and procrastination YouTube.

Watching a React tutorial? That's work. Watching comedy clips? That's a break. Same site, completely different context.

We're building AI that understands intent, not just URLs. The model analyzes page content, your current task context, and your personal patterns to make intelligent categorizations.

Early tests are promising. Our prototype correctly identified research vs. distraction 87% of the time—without any manual setup from the user.

Decision 3: Insights Over Surveillance

We're not building a tool that watches you and reports to your boss. We're building a tool that helps you understand yourself.

The difference matters.

Browsing.AI will show you patterns you didn't know existed. When you tend to lose focus. What triggers your distraction spirals. How your energy fluctuates throughout the day.

The goal isn't to make you feel guilty. It's to give you data so you can make better choices.

Think of it like a fitness tracker for your attention. It doesn't judge you for skipping a workout—it just shows you the pattern so you can decide what to do about it.

What We Built This Month

Month 1 was about foundations:

Week 1-2: Research and Validation

Interviewed 23 knowledge workers about their productivity pain points
Analyzed competitor products (RescueTime, Toggl, Freedom, Cold Turkey)
Validated that privacy concerns were a real barrier to adoption
Defined our core value proposition

Week 3: Landing Page and Waitlist

Built the marketing site with Next.js
Set up email capture with n8n automation
Wrote initial blog content to establish SEO foundation
Launched on a few small communities for early feedback

Week 4: Technical Foundation

Started browser extension architecture
Prototyped local AI categorization
Set up development infrastructure
Began designing the dashboard UI

Current status: Waitlist is live, extension MVP is in active development.

Mistakes and Lessons

We're not going to pretend everything went smoothly.

Mistake 1: Overcomplicating the MVP

Our first technical spec included real-time coaching, team dashboards, and integration with 15 productivity tools. Way too much.

We cut it down to the essentials: track, categorize, and show insights. Everything else can come later.

Lesson: Ship the smallest thing that delivers value. Then iterate.

Mistake 2: Underestimating the Privacy Messaging

Early landing page copy focused on features. But when we asked visitors what they remembered, almost no one mentioned privacy.

We rewrote everything to lead with privacy-first positioning. Conversion improved immediately.

Lesson: Your differentiator should be impossible to miss.

Mistake 3: Not Building Email List Earlier

We spent two weeks perfecting the landing page before adding email capture. That's two weeks of visitors we couldn't follow up with.

Lesson: Capture interest from day one, even if the page isn't perfect.

The Road Ahead

Month 2 is all about the extension MVP.

What we're building:

Chrome extension with basic tracking
Local categorization engine (v1)
Simple daily/weekly insights dashboard
Privacy controls and data export

What we're not building yet:

AI coaching features
Team/enterprise features
Integrations with other tools
Mobile apps

We're targeting a closed beta in Q2 2026 with our waitlist members getting first access.

Why We're Sharing This

Building in public is a bet.

We're betting that transparency builds trust faster than polished marketing. We're betting that showing our work—including the messy parts—creates a connection that corporate-speak never could.

If you're building something similar, steal whatever's useful from our approach. If you're considering joining the waitlist, hopefully this gives you confidence that real humans are behind this thing, making thoughtful decisions.

And if you think we're making mistakes? Tell us. Seriously. We're building this for people like you, and your feedback shapes every decision.

Month 2 preview: We'll share our technical architecture decisions, early beta testing results, and the surprisingly tricky challenge of categorizing browser activity with AI.

Want early access? Join the waitlist. Beta members will shape the product through direct feedback—and get lifetime discounts when we launch.

See you next month.

This is part of our Building in Public series, where we share the unfiltered journey of creating Browsing.AI. Subscribe to get monthly updates delivered to your inbox.