Sources
- Airbnb Engineering
- Amazon AWS AI Blog
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- BrettTerpstra.com
- ByteByteGo
- CloudFlare
- Dropbox Tech Blog
- Facebook Code
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- O'Reilly Radar
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- Stripe Blog
- The Batch | DeepLearning.AI | AI News & Insights
- The Dropbox Blog
- The GitHub Blog
- The Netflix Tech Blog
- The Official Microsoft Blog
- Vercel Blog
- Yelp Engineering and Product Blog
Engineering @ Scale — 2026-06-25#
Signal of the Day#
The “lost in the middle” context window problem is not just a training artifact to be smoothed out with more compute, but a fundamental geometric property of transformer architecture where causal mask primacy biases and position encoding recency biases cancel out in the middle. To build reliable agentic systems, engineering teams must stop relying on massive context windows as stateful databases, and instead treat the LLM as an ephemeral pipe by externalizing state to disk and forcing fresh reads at the point of action.