The rise of AI-powered chat applications has revolutionized how users interact with technology, offering assistance, creativity, and research tools through natural language discussions. Among these applications, the Poe app, developed by Quora, stands out as a versatile interface for engaging with multiple AI models from OpenAI, Anthropic, and others. However, users recently experienced a frustrating issue: the app would frequently crash or become unresponsive when switching between models. After weeks of user reports and investigations, developers identified the culprit—improper handling of local cache data—and implemented a targeted solution that dramatically improved stability.
TL;DR
Poe app users encountered frequent crashes and UI lockups when switching between AI models. The problem was traced to a bloated and conflicting local cache system. By purging corrupted and legacy cache entries, developers restored fluid model switching and eliminated app lockups. The incident highlights the importance of managing client-side resources effectively in AI-driven applications.
Understanding the Crash: What Went Wrong
The Poe app’s appeal lies in its seamless integration of multiple AI models such as GPT-4, Claude, and others from varying providers. However, each model has distinct response times, data footprints, and interaction styles, which the Poe client handles through local caching to ensure smooth user experiences. Unfortunately, that cache system became a vulnerability.
Users began noticing that after prolonged use or switching between models like Claude 2 and GPT-4, the app would freeze or abruptly crash. Crash logs pointed to memory overflows and unhandled exceptions in the UI rendering thread. These red flags revealed a deeper systemic issue: the local cache had grown too large and incoherent, with artifacts from different models clashing during render.
Primary Symptoms Reported by Users Included:
- App unresponsiveness after switching AI models multiple times
- Freezing UI while accessing saved conversations
- Crashes triggered by switching models mid-conversation
- Disappearing or malformed messages in the chat window
It became clear that the Poe app’s local cache was not adequately segregated or purged across sessions or model context changes. Instead of clearing irrelevant data or refreshing dynamically, the cache accumulated outdated information and mismatched tokens, often exceeding device memory limits or triggering render errors.
The Cache Mechanism: Helpful Until It Isn’t
Local caching is a common optimization strategy in mobile and web apps. It reduces backend calls, speeds up load times, and retains session context. For an app like Poe, which interacts heavily with APIs returning model-generated data, caching conversations, tokens, and IDs locally helps maintain consistency and performance. However, the implementation must account for model-specific metadata and volatility.
Essentially, Poe was caching too much too broadly—conversations, prompt completions, token references, and model plugins were stored locally without expiration timestamps or model scoping. When a user switched from GPT-4, which might return a structured JSON payload for tools, to Claude, which formats replies differently, the app attempted to render both within the same UI structure. This caused malformed DOM elements or rendered loops, leading eventually to thread crashes or stack overflows.
Diagnosing the Problem: A Developer’s Detective Story
The development team initiated a crash review after a spike in support tickets and app store complaints. They analyzed crash analytics from services like Sentry and Firebase Crashlytics and noticed a pattern: most crashes occurred within seconds of model switching. The majority happened in functions responsible for parsing cached responses and rendering UI panels.
Among the critical discoveries was a thread deadlock caused by overlapping state refreshes between the old and new model’s caches. Essentially, one thread attempted to invalidate a cache while another was still reading from it—leading to inconsistent pointers and corrupted memory. On devices with slower storage or fewer resources, this issue became more pronounced, which explained why Android users saw even more frequent crashes.
The Log Diagnostics Revealed:
- CacheHandler.js:121 — Unexpected null token ID
- RenderManager.kt:94 — UI component failed to parse source payload
- StoreProvider.swift:303 — Mutex timeout while writing context state
These technical breadcrumbs ultimately led the team to the realization: a complete redesign of caching hierarchy wasn’t immediately necessary—a selective purge and better model separation would resolve the issue short-term.
The Fix: Purging the Local Cache
The solution applied was both elegant and targeted. Instead of overhauling the entire caching mechanism, developers first pushed a hotfix update that detects when users switch models and selectively purges the corresponding incompatible cache entries. Additionally, they introduced segmentation in the cache—model-specific namespaces that prevent cross-model data bleed.
The updated cache strategy included the following changes:
- Model-specific cache buckets to isolate data per AI provider
- Automatic purge triggers on model switch events
- Reduced cache footprint by setting expiration rules for ephemeral data
- Fall-back rendering paths to handle UI gracefully in edge cases
Once implemented, user reports of persistent crashes dropped by over 80% within the first week. On Reddit and community forums, users expressed relief and renewed confidence in Poe’s reliability. Developers also added a manual “clear cache” option in the settings menu as an extra safety net for power users and testers.
Why It Matters: Cache Management in AI UX
This incident with Poe is not just about one bug—it symbolizes a critical design principle for AI app developers. As these apps scale in complexity, incorporating multiple backend models and integrations, client-side data becomes harder to sync and manage. Factors such as latency compensation, session persistence, and user history all rest on fragile local infrastructure.
Lessons learned from Poe’s crash diagnostics underscore the importance of:
- Maintaining cache hygiene through expiration and isolation
- Designing fault-tolerant rendering logic that accounts for null or malformed data
- Monitoring user session flows to detect pattern-based breakpoints
Moreover, as users increasingly rely on these tools for high-stakes work—from writing papers to coding—stability is not merely a feature, but a requirement. Developers must treat performance and resilience as primary product goals, not afterthoughts.
Looking Ahead: Towards a More Robust Poe
Following the hotfix, the Poe team has reportedly started work on a more thorough overhaul of its state management and cache lifecycle approach. Long-term improvements will likely involve smarter predictive caching, user-specific configs, and adaptable render frameworks that adjust dynamically to the model being queried.
The crisis, while disruptive, acted as a catalyst for modernizing a piece of foundational infrastructure that had long gone unnoticed. The trust regained since the fix stands as a testament to responsive product development and user-first thinking.
As the AI app space becomes more competitive and crowded, issues like Poe’s crash scenario serve as important case studies in how scalability and UX go hand-in-hand. It’s not just about what the AI says—it’s about ensuring it can say it without crashing halfway through.