The Complete Guide to Inference Caching in LLMs
Calling a large language model API at scale is expensive and slow.
International Conference on Learning Representations (ICLR) 2026
The Human Infrastructure: How Netflix Built the Operations Layer Behind Live at Scale
By: Brett Axler, Casper Choffat, and Alo Lowry In the three years since our first Live show, Chris Rock: Selective Outrage, we have witnessed an incredible expansion of our live content slate and the live operations that support it. From modest beginnings of streaming just one show per month, we are now capable of streaming over …
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Introducing granular cost attribution for Amazon Bedrock
As AI inference grows into a significant share of cloud spend, understanding who and what are driving costs is essential for chargebacks, cost optimization, and financial planning. Today, we’re announcing granular cost attribution for Amazon Bedrock inference. Amazon Bedrock now automatically attributes inference costs to the IAM principal that made the call. An IAM principal …
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OpenAI Executive Kevin Weil Is Leaving the Company
The former Instagram VP is departing the ChatGPT-maker, which is folding the AI science application he led into Codex.
This AI mines the numbers buried in scientific papers and turns them into usable data fast
Numbers are the language of science—yet in research articles, they are often buried within the text and difficult to analyze. Researchers at Jülich have developed an AI system that automatically identifies these numbers, categorizes them, and converts them into structured data. The Quinex framework thus eliminates the need for time-consuming manual work.
Flux2klein little info
So in the past few weeks I have been dedicating long hours into finding optimal approaches to preserve as much of the ref latent inside and basically force the model to do two things; preserve the features and be flexible and it has been such pain but I think I stumbled accidentally many interesting features …
Python Decorators for Production Machine Learning Engineering
You’ve probably written a decorator or two in your Python career.
MixAtlas: Uncertainty-aware Data Mixture Optimization for Multimodal LLM Midtraining
This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models (NADPFM) at ICLR 2026. Principled domain reweighting can substantially improve sample efficiency and downstream generalization; however, data-mixture optimization for multimodal pretraining remains underexplored. Current multimodal training recipes tune mixtures from only a single perspective such as data format or …
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Cost-efficient custom text-to-SQL using Amazon Nova Micro and Amazon Bedrock on-demand inference
Text-to-SQL generation remains a persistent challenge in enterprise AI applications, particularly when working with custom SQL dialects or domain-specific database schemas. While foundation models (FMs) demonstrate strong performance on standard SQL, achieving production-grade accuracy for specialized dialects requires fine-tuning. However, fine-tuning introduces an operational trade-off: hosting custom models on persistent infrastructure incurs continuous costs, even during …