EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·28d ago·5 min read
I stress-tested Meta Muse until its agent control plane started timing out
TL;DR
Meta Muse, an AI agent, faced significant spawn failures under load, revealing potential database contention issues. Testing showed a 72.5% failure rate at peak attempts, indicating scalability challenges.
✦ Why It Matters
Evaluate your database's performance under load to prevent similar contention issues in your applications.
Key Takeaways
- Monitor database performance during high-load scenarios.
- Investigate and resolve lock contention issues.
- Consider staggering requests to improve success rates.
- Utilize logging to analyze agent performance under stress.
How It Works
Meta Muse operates by spawning subagents to perform tasks, with each agent's progress tracked in a PostgreSQL database. The stress tests involved issuing multiple spawn requests simultaneously, allowing for the observation of failure rates and performance bottlenecks.