DEV Community: Glen Modiba

Stop building AI that acts like an oracle. Build a scientific partner instead.

Glen Modiba — Fri, 17 Jul 2026 15:45:23 +0000

I got a piece of feedback today that completely changed how I am architecting the AI features in my current project.

Like a lot of us building right now, I'm working on a tool that feeds application data to an LLM to generate plain-English insights. In my case, it’s a lightweight behavioral analytics tool that watches user drop-offs and tries to explain why they happened.

Initially, I thought the goal was to make the AI sound confident.
Data goes in ➔ AI spots a 10-second idle time on a checkout button ➔ AI tells the founder, "Your button contrast is too low."

But a developer in the community pointed out a massive flaw in this UX: It masks a hypothesis as a measured fact.

When we give an AI a "confidence score" and a text box, we create an illusion of precision. The drop-off is an objective fact. The reason is just a synthesized guess. If the AI is wrong (and it will be), the founder loses trust in the entire platform.

So, I’m ripping out the standard "AI Output" text box and replacing it with a structured Scientific Method UI.

Going forward, every AI-generated insight in the dashboard will be physically separated into three distinct blocks:

The Evidence (Immutable)
What the DOM/database actually saw.
Example: "84 sessions idled on #password-input for >8 seconds before exiting."
The Inference (Mutable)
What the AI suspects is the friction.
Example: "Hypothesis: Password requirements are hidden until validation fails."
The Falsification Experiment (Actionable)
How to prove the AI wrong.
Example: "Display password rules in plain text below the input field. If idle time does not drop below 3s, this hypothesis is invalid."

We also changed how "dismissing" an alert works. Instead of just hiding a notification, dismissing an AI hypothesis now acts as a deployment state. The system logs it as: "Archived. I will silence this hypothesis, but I will continue watching this element. I will only wake this alert back up if I observe materially new evidence (e.g., a >15% shift in baseline drop-off)."

We need to stop building AI wrappers that pretend to have all the answers. The real value isn't in generating a perfect guess; it’s in helping developers separate the signal from the noise so they can run better experiments.

How are you all handling AI confidence in your own apps? Are you letting the LLM speak authoritatively, or are you fencing it in?

Why Your Users are Leaving in Silence (and How to Fix the "Leaky Bucket" with AI)

Glen Modiba — Wed, 15 Jul 2026 16:34:41 +0000

Every founder has experienced this exact gut-punch:

You build an app. You launch it. You spend weeks hunting down your first 100 sign-ups.

Then, one by one, they quietly disappear.

There are no errors in your server logs. No angry support tickets. No refund requests. Just... silence.

This silent churn is the single most expensive problem in modern software, and it is incredibly difficult to diagnose.

The "More Data" Trap
When developers and founders try to solve this, they usually turn to the industry giants—tools like Mixpanel, Amplitude, or Hotjar.

But those tools present a massive paradox: They require you to already know what questions to ask.

To get any value out of them, you have to spend hours setting up custom tracking events, building complex conversion funnels, and configuring massive dashboards. It quickly turns into a full-time data analytics job.

But as solo developers and small teams, we don't need more data. We don't have time to stare at graphs trying to play detective.

We don't want a tool that gives us numbers to interpret. We want a tool that simply tells us what is broken.
**
Moving From "Dashboards" to "Actionable Insights"**
I wanted a system that worked like an active, AI-powered product consultant living inside my app. That is why I built AppScore.

The philosophy behind it is simple: you install a lightweight SDK, and instead of giving you a blank canvas of charts, the platform automatically monitors user behavior and delivers plain-English, actionable insights.

Instead of staring at a drop-off graph, AppScore tells you:

"Step 2 of your onboarding is your biggest leak, losing 40% of your users. This is double the drop-off of any other step. You are asking for credit card details too early—defer this step until after they experience the core value of your app."

What it tracks automatically:
Onboarding Friction: Pinpoints the exact input field or step where users lose patience and close the tab.

Dead Features: Highlights code you spent weeks writing that fewer than 5% of your active users ever click on.

Rage-Clicks & Frustration: Detects where users are clicking repeatedly in frustration, signaling a broken button or a confusing UI layout.

The Ultimate Goal: Contextual Benchmarking
The vision for AppScore goes beyond just looking at your own isolated numbers.

Most founders have no idea if a 30% weekly retention rate is amazing or disastrous for their specific niche. As the platform grows, AppScore is designed to progressively learn from anonymized aggregate data to give you real-world context:

"Your app's activation rate is currently performing 5% better than similar B2B SaaS tools in your category."

We are also working on a natural language QueryBox—allowing you to click on any friction alert and literally text the AI: "Why did this happen on the billing page today?" to get an instant, simplified explanation.

Join the Free Beta (No Credit Card Required)
I’ve just opened up the beta for AppScore, and I am looking for a handful of early developers, founders, and product minds to test it out.

It takes less than 5 minutes to set up:

Grab your unique API key.

Install our lightweight SDK, paste the key, and let the AI start analyzing.

<script src="https://shiny-malasada-5a8b3a.netlify.app/appscope.js"></script><script>

AppScope.init({

apiKey: 'your-api-key',

userId: currentUser.id, // optional

});</script>

If you are tired of guessing why your users are slipping away, you can jump into the free beta here:

👉https://shiny-malasada-5a8b3a.netlify.app/register.html

I'd love to hear from anyone who has battled silent churn. What is the one metric or user behavior you wish your analytics tool would just tell you outright without making you build a dashboard for it?

I built a behavioral analytics tool for app builders — here's why and how

Glen Modiba — Tue, 14 Jul 2026 10:53:30 +0000

Every solo developer hits the same wall eventually.

You ship something. You get users. Then one by one, they quietly disappear. No error in your logs. No angry email. Just silence.

That silence is the most expensive problem in SaaS and nobody talks about it enough.

The problem I kept running into

I'd build a feature, ship it, and have no idea if anyone actually used it. I'd watch my user count slowly decline and have zero visibility into why. Was it the onboarding? A confusing page? A feature nobody wanted?

The tools that exist to solve this (Mixpanel, Amplitude, FullStory) are powerful but they require you to already know what questions to ask. You have to build funnels, define events, set up dashboards. That's a full-time job.

I didn't want a tool that gives me more data to interpret. I wanted something that just tells me what's wrong.

What I built

AppScope is a behavioral analytics platform with an AI insight layer on top. The idea is simple: drop in one script tag, and within 48 hours the platform tells you what's broken in plain English.

No funnels to configure. No dashboards to build. It just watches and reports.

Here's what it detects automatically:

Churn risk — users who never completed onboarding and have gone silent
Friction points — pages where users leave within 60 seconds of arriving
Onboarding drop-off — exactly which step loses the most users
Dead features — features that less than 5% of users ever touch

Then it passes those detected patterns to an AI layer that narrates them like a product analyst would: "Step 2 is your biggest drop-off point, losing 4 users — double the drop at any other step. Review what's being asked there and either simplify it or defer it until users experience core value first."

The tech stack

Backend: FastAPI + PostgreSQL (hosted on Render)
SDK: Vanilla JavaScript — no dependencies, ~5KB
AI layer: Claude API for insight narration
Frontend: Plain HTML/CSS dashboard (hosted on Netlify)

I deliberately avoided heavy frameworks. The SDK needs to be something any developer can drop into any app without worrying about bundle size or conflicts.

Installation

<script src="https://your-appscope-url.netlify.app/appscope.js"></script>
<script>
  AppScope.init({
    apiKey: 'your-api-key',
    userId: currentUser.id, // optional
  });
</script>

That's it. Drop it before </body> and you're live.

For tracked elements, just add data attributes:

<button data-as-track="upgrade_button" data-as-feature="billing">
  Upgrade
</button>

What I learned building this

The hardest part wasn't the tracking or the AI — it was the low data problem. A new client with 50 users doesn't have enough signal for meaningful churn prediction. So the system has two modes: descriptive insights for low-volume apps (drop-off rates, dead features) and predictive insights as data accumulates.

The second thing I learned: founders don't want more data. They want someone to read the data for them. That insight shaped everything about how AppScope presents information.

Try it

I just launched the beta. It's free, no credit card required.

👉https://shiny-malasada-5a8b3a.netlify.app/register.html

Looking for feedback from developers and founders who've dealt with silent churn. What would you want a tool like this to tell you?