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The Three Cs: Concatenate, Compress, Cache

CSS Wizardry

What is the availability, configurability, and efficacy of each? ?️ Caching them at the other end: How long should we cache files on a user’s device? Given that 66% of all websites (and 77% of all requests ) are running HTTP/2, I will not discuss concatenation strategies for HTTP/1.1 Cache This is the easy one.

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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

Both categories share common requirements, such as high throughput and high availability. Best Effort Regional Counter This type of counter is powered by EVCache , Netflix’s distributed caching solution built on the widely popular Memcached. Let’s see if we can iterate on our solution to overcome these drawbacks.

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Cache Grab: How Much Are You Leaving on the Table?

CSS Wizardry

For the longest time now, I have been obsessed with caching. I think every developer of any discipline would agree that caching is important, but I do tend to find that, particularly with web developers, gaps in knowledge leave a lot of opportunities for optimisation on the table. Want to know everything (and more) about HTTP cache?

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Consistent caching mechanism in Titus Gateway

The Netflix TechBlog

We introduce a caching mechanism in the API gateway layer, allowing us to offload processing from singleton leader elected controllers without giving up strict data consistency and guarantees clients observe. The cache is kept in sync with the current leader process. How do I know that my cache is up to date? of the data.

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Migrating Netflix to GraphQL Safely

The Netflix TechBlog

The three strategies we will discuss today are AB Testing , Replay Testing, and Sticky Canaries. And we definitely couldn’t replay test non-functional requirements like caching and logging user interaction. Let’s discuss the three testing strategies in further detail. To launch Phase 1 safely, we used AB Testing.

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Foundation Model for Personalized Recommendation

The Netflix TechBlog

Key insights from this shiftinclude: A Data-Centric Approach : Shifting focus from model-centric strategies, which heavily rely on feature engineering, to a data-centric one. At inference time, when multi-step decoding is needed, we can deploy KV caching to efficiently reuse past computations and maintain lowlatency.

Tuning 165
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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

The RAG process begins by summarizing and converting user prompts into queries that are sent to a search platform that uses semantic similarities to find relevant data in vector databases, semantic caches, or other online data sources. Development and demand for AI tools come with a growing concern about their environmental cost.

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