I Debugged a 3GB Memory Leak in a Web App. Here's What I Found.
A deep dive into browser performance, React state management, and why I ended up building my own solution.
I've been using AI chat interfaces constantly for work: coding, writing, research, you name it. But I kept running into the same frustrating pattern: after 20-30 minutes of use, the browser tab would become unusable. Keystrokes lagging by seconds. Scroll freezing. Memory climbing past 3GB.
At first I assumed it was my machine. Then I noticed something weird: the mobile app on my phone was buttery smooth. Same account. Same conversations. Completely different experience.
That inconsistency bugged me. So I opened DevTools and started digging.
The Investigation
I started with a fresh browser tab and took some baseline measurements:
- DOM nodes: 779
- JS heap: ~150MB
- FPS: Steady 60
Then I did what I normally do: scrolled through my conversation history, opened a few old chats, typed some messages. Normal usage.
Twenty minutes later:
- DOM nodes: 89,424
- JS heap: 3.17GB
- FPS: 1-5 (not a typo)
- Active timers: 23,584
That's a 112x increase in DOM nodes just from scrolling. And the memory never came back down.
What's Happening Under the Hood
The web app uses React with virtualized scrolling, a common optimization pattern. The idea is simple: only render what's visible on screen, and recycle DOM nodes as the user scrolls. This keeps the page fast regardless of how much content exists.
But here's the catch: virtual scrolling handles the DOM, not the JavaScript heap.
When you scroll through conversations, the app loads message data into React state. Old messages get removed from the visible DOM, but their data stays in memory. React holds references to everything you've scrolled past.
In JavaScript, memory only gets garbage collected when there are no remaining references to an object. If React state still points to that data, it sticks around. Forever.
The result: DOM stays manageable (sort of), but the heap grows unbounded. Classic memory leak pattern.
Why Mobile Doesn't Have This Problem
Native iOS apps have a completely different memory model. The operating system actively manages memory and will terminate apps that misbehave. Developers are forced to be disciplined about cleanup.
Web browsers are more forgiving. A tab can consume gigabytes of RAM before the browser intervenes. That forgiveness becomes a liability when applications don't clean up after themselves.
Same product, different runtime constraints, completely different user experience.
What I Tried
Attempt 1: DOM Trimming
I wrote a script to automatically remove old DOM nodes as I scrolled. Aggressive pruning: kept only the visible viewport plus a small buffer.
Result: DOM node count dropped significantly. Memory stayed at ~1GB. Lag continued.
The DOM wasn't the bottleneck. The JavaScript heap was.
Attempt 2: Forced Garbage Collection
Chrome's DevTools lets you manually trigger garbage collection. I tried forcing it periodically.
Result: No change. The references in React state prevented anything from being collected.
Attempt 3: Page Refresh
The nuclear option. Just refresh the page when things get slow.
Result: Works, but you lose your scroll position and it's annoying. Band-aid, not a fix.
The Realization
At some point I stopped trying to fix the existing app and asked a different question: what if I just didn't use it?
The underlying API is solid. The AI models are excellent. The performance issues exist purely in the web client layer: the React app sitting between me and the API.
So I built a minimal alternative. My requirements were simple:
- Talk to the same models
- Stream responses in real-time
- Store conversations locally
- Don't leak memory
The result was about 200 lines of vanilla JavaScript. No React. No framework. Just fetch calls, DOM manipulation, and localStorage.
Memory usage: ~20MB. Consistent. Doesn't grow over time.
Technical Takeaways
A few things I learned from this investigation:
1. Virtual scrolling isn't enough.
If you're only virtualizing the DOM but not the underlying data, you've solved half the problem. For large datasets, you need pagination or a sliding window for state, not just for rendering.
2. Memory profiling is underrated.
Most frontend developers focus on bundle size and initial load time. Runtime memory behavior gets less attention, but it matters a lot for long-running sessions.
3. Framework overhead is real.
React is great for complex UIs with lots of interactivity. But for a fundamentally simple interface (input box, message list, send button) the abstraction layer adds more weight than value.
4. Native apps have different constraints.
The same codebase won't behave the same across platforms. Mobile operating systems enforce discipline that browsers don't. If your web app works but your mobile app doesn't (or vice versa), that's a signal worth investigating.
What I Built
I ended up turning my debugging project into a proper tool. It's a lightweight web client for AI chat that focuses on performance:
- Same underlying models
- Import your existing conversation history
- Search that actually works
- ~20MB memory footprint
I called it GPTRapid. It's available at gptrapid.com if you're experiencing similar frustrations.
Two tiers: $4.99/month if you bring your own API key, $14.99/month if you want everything included. Both cheaper than the standard $20/month subscription and, more importantly, no lag.
Closing Thoughts
This wasn't meant to be a product. It started as genuine curiosity about why my browser kept freezing. The solution just happened to be useful enough to share.
If you're a developer experiencing similar issues with any web app, I'd encourage you to crack open DevTools and investigate. The Memory tab and Performance tab are incredibly powerful. Most frontend performance problems are diagnosable with patience and the right tools.
And if you find yourself building a replacement for something that frustrates you, that's often a sign you're onto something worth pursuing.
Built by HarwoodLabs. We build lightweight tools that solve real problems.