Forem: Merse Sarvari

I built a monitoring tool because I’m tired of "YAML Hell" and $500 Datadog bills.

Merse Sarvari — Fri, 03 Apr 2026 11:33:21 +0000

Hey everyone!

I’m a solo developer, and like many of you, I’ve spent way too much time in "Monitoring Purgatory." You know the place: you’re either flying blind with console.log, or you’re drowning in a sea of complex YAML configurations for enterprise tools that cost more than your rent.

I wanted a "Middle Ground." Something built specifically for the JavaScript ecosystem that just... works.

So, I built Statvisor.

The Problem with Modern Monitoring

The "Vercel Gap": Vercel Analytics is great for the frontend, but it leaves your backend as a total black box especially if you aren't fully 100% serverless.
The "Datadog Tax": Massive tools like Datadog are powerful, but the "event taxes," complex agents, and steep learning curves are overkill for 90% of the apps we actually build.
The "Context Switch": Checking Sentry for errors, Vercel for Web Vitals, and another tool for API latency is exhausting.

The Solution: One SDK for the Full Stack

Statvisor is designed to be the "Vital Signs" for your entire app. You drop in one middleware, and you get your backend health and frontend experience in a single view.

Backend: P50/P95/P99 latency and error rates per route.
Frontend: Core Web Vitals (LCP, INP, CLS) tracked alongside your API.
Setup: Under 2 minutes. No infrastructure to manage.

// npm install @statvisor/sdk
import * as statvisor from "@statvisor/sdk";

// Backend - any framework (Express, Fastify, Hono, Next.js)
app.use(statvisor.express({ apiKey: "your_key" }));

// Frontend - Drop in your React layout or Vanilla JS
<StatvisorAnalytics frontendKey="pk_your_key" />

Built for Builders

I’m launching this from zero today. No VC funding, no massive marketing team-just a tool I needed for my own projects.

JavaScript Only: We focus 100% on the JS/TS ecosystem (Node, Bun, Deno, React).
Flat Pricing: $12/month for Pro. No "surprise" bills or hidden event quotas.
Free Tier: 30-day retention for your side projects, no credit card required.

I'd love your feedback!

I'm looking for the first few people to try it out and tell me what sucks, what’s missing, and what’s actually helpful. I’m building this in public and want to make it the best possible tool for the JS community.

Check it out here: Demo

What’s the biggest "monitoring headache" you’ve had recently? Let’s talk in the comments!

Why Your LCP Score is Perfect in Lab but Failing in Production

Merse Sarvari — Thu, 02 Apr 2026 15:06:18 +0000

We’ve all been there: Lighthouse gives you a green 100 on desktop, but your Google Search Console is screaming "Poor" for Largest Contentful Paint (LCP) on mobile.

The reality is that 75% of your real-world users must hit that 2.5s threshold for Google to count it as a "Good" experience. If you have one slow API region or a heavy hero image that only affects mobile sessions, your score is going to tank.

After digging into the data across billions of sessions, here are the two biggest (and often invisible) culprits:

1. The "Hidden" API Latency

If your LCP element depends on a data fetch—like a personalized hero image or a product URL returned from your backend—your API latency is your performance floor.

On pages with poor LCP, client-side loading delays average around 1,290ms. That is more than half of your entire 2.5s budget gone before a single pixel even renders.

2. The Preload Gap

Images represent 72% of LCP elements on mobile. Yet, less than 1% of mobile pages use the single most effective fix: Image Preloading. Adding one line of markup

<link rel='preload' as='image'>

allows the browser to start the fetch before it even finishes parsing your HTML.

How to find the real bottleneck?

Lighthouse gives you a score, but it doesn't give you a cause. To fix LCP for good, you need to see exactly which backend routes are adding latency to your P95 users in real-time.

I’ve put together a full deep-dive on how to diagnose these production-only failures, including a 5-step LCP fix checklist.

Check out the full breakdown here:
LCP: Why Your Score Keeps Failing in Production

From Frustration to Insight: Full-Stack Analytics Without the Headaches

Merse Sarvari — Sat, 28 Mar 2026 21:55:48 +0000

As a JavaScript developer, I've spent countless hours wrestling with performance issues and elusive bugs that plague full-stack web applications. You know the drill: the frontend feels sluggish, users complain of errors, and pinpointing the root cause feels like searching for a needle in a haystack.

The traditional approach to debugging these issues often involves a fragmented ecosystem of tools. Backend monitoring requires one set of libraries and dashboards, while frontend analytics demands another. Configuring these tools can be tedious, requiring you to meticulously instrument your code and potentially impact application performance. Then, correlating backend performance with frontend behavior becomes a manual, time-consuming process.

This fragmented approach often leads to:

Blind Spots: Missing crucial connections between backend API performance and frontend user experience. A slow API response might be the culprit behind a frustrated user, but uncovering that connection requires significant effort.
Configuration Overload: Spending valuable development time setting up and maintaining complex analytics pipelines instead of focusing on building features.
Performance Impact: Adding heavy analytics libraries to your frontend can negatively impact Web Vitals, ironically worsening the user experience you're trying to improve.
Delayed Issue Resolution: The time it takes to identify and diagnose problems increases, leading to prolonged periods of degraded performance and unhappy users.

This frustration is what led me to explore alternatives and eventually contribute to building Statvisor. The core idea behind Statvisor is simple: unify backend API monitoring and frontend analytics into a single, streamlined SDK.

What problems does this solve in practice? Let's consider a few common scenarios:

Scenario 1: API Latency Bottleneck

Imagine users reporting slow page loads. Traditional frontend analytics might show poor First Contentful Paint (FCP) or Largest Contentful Paint (LCP) scores, but without deeper insight, you're left guessing. With Statvisor, you can immediately correlate these frontend metrics with backend API latency. If a specific API endpoint is consistently slow, it becomes a clear target for optimization.

Scenario 2: Error Cascade

A JavaScript error on the frontend might be a symptom of a deeper issue in your backend API. By tracking errors across both tiers, Statvisor can help you identify the root cause. For instance, a 500 error from an API endpoint might trigger an error in your React component responsible for displaying that data. Seeing these errors in a unified view accelerates debugging.

Scenario 3: Geographical Performance Variations

Are users in certain geographic regions experiencing consistently slower response times? This could indicate issues with your CDN configuration or database latency in specific data centers. Statvisor automatically tracks visitor geography, allowing you to identify and address location-specific performance bottlenecks.

Practical Tips for Monitoring Your JavaScript Applications (Even Without Statvisor):

Whether you're using Statvisor or not, here are some general best practices for monitoring your JavaScript applications:

Implement Structured Logging: Use structured logging (e.g., JSON format) on your backend to make it easier to query and analyze your logs. Include relevant context such as request IDs, user IDs, and timestamps.
Track Web Vitals: Web Vitals are essential for understanding the user experience. Use tools like the web-vitals library and integrate them with your existing analytics platform. Focus on metrics like LCP, FID, and CLS.
Monitor Error Rates: Implement robust error tracking on both the frontend and backend. Capture unhandled exceptions and log them with sufficient context to facilitate debugging. Consider using Sentry, Bugsnag, or similar error tracking services.
Set Performance Budgets: Define acceptable performance thresholds for key metrics (e.g., API response time, page load time). Monitor these metrics continuously and trigger alerts when they exceed the defined budgets.
Use Browser Developer Tools: Leverage the browser's developer tools to profile your application's performance. Identify bottlenecks in your JavaScript code, CSS rendering, and network requests. The Performance tab is particularly useful.

The beauty of tools like Statvisor is that they automate many of these tasks, providing a unified view of your application's health and performance. By combining backend API monitoring and frontend analytics, you can gain a deeper understanding of the user experience and quickly identify and resolve performance issues. This ultimately leads to happier users and a more reliable application.