Engineering brief
Silent failures at scale: why your training code probably has undetected bugs
At a glance
- Relevance
- Practical value
- Warnings
- None
Poolside's pretraining team caught a race condition corrupting 0.5% of gradients. Most teams lack the hash checks to detect this.
Silent training failures can destroy weeks of compute. Observability is a budget line item.
Summary
Poolside's talk reveals that scaling pretraining surfaces silent failures like broken GPUs
causing data corruption and numerical precision issues that halt convergence. These problems
are invisible without custom observability like weight hash checks across replicas.
Watch the video
This video is blocked due to your privacy settings. To watch this video, please accept YouTube marketing cookies.
Related breakdowns
How Two Sigma Tames Cloud Agents by Running Them as You
Shu Fang explains how Two Sigma lets agents run as the user's identity, using attribution headers and a cached web index to reduce risk. A practical approach…
Compression as strategy: why quantized giants beat native dwarfs
Quantization isn't just about shrinking models—it's a strategic lever. Compressed giants outperform native dwarfs of equal size, but new architectures are…
Turbopuffer: Why vector search doesn't need GPUs or DRAM
Vector search doesn't need expensive GPUs or DRAM. Turbopuffer uses CPUs and S3 to cut costs 95%—and Cursor proved it works in production.
Get TL;DW
Too Long; Didn't Watch.
A concise breakdowns of the AI and devtools videos that actually matter for engineering leaders.
Free. Weekly. No hype.
Video and thumbnails remain the property of their respective creators. tldw.news provides editorial analysis, commentary, and discovery links to original content.