1d ago · 12 min read · Every time a viewer opens a streaming app, the recommendation service has a few hundred milliseconds to decide what to show them. It pulls a viewer engagement profile from a low-latency store, passes it to a ranking model, and returns a personalized ...
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1d ago · 10 min read · By Terry Building rolling window metrics over raw event streams looks straightforward until you run the numbers. A 90-day API call history for ten thousand customers, where each customer fires thousands of calls per day, means hundreds of millions o...
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2d ago · 14 min read · AI agents forget everything between requests. Without persistent memory, an agent cannot recall what a user asked five minutes ago, learn that a particular customer prefers technical documentation over marketing content, or notice that the same quest...
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2d ago · 14 min read · Every modern application generates a constant stream of data: user clicks, transactions, sensor readings, log events. Traditional databases were designed for a world where data arrives in batches and queries run on demand. But when you need answers a...
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2d ago · 15 min read · Most recommendation engines run on batch pipelines. A nightly job crunches user activity logs, updates a model, and pushes new recommendations to a cache. By the time a user sees "Recommended for you," the data behind it is already stale. This matter...
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2d ago · 14 min read · Most machine learning models are only as good as the features they consume. You can have the most sophisticated model architecture in the world, but if your features are stale, incomplete, or slow to compute, your predictions suffer. This is the feat...
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2d ago · 14 min read · You have a dashboard that shows total revenue by product category. Every time a new order comes in, the underlying aggregation query runs against millions of rows. Response times creep up. Users complain. Someone suggests caching the results in a mat...
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2d ago · 13 min read · Event-driven microservices promise loose coupling and real-time responsiveness. In practice, they deliver something else: hundreds of lines of consumer boilerplate, hand-rolled state management, and retry logic that nobody wants to maintain. If you h...
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2d ago · 13 min read · Why Batch Inventory Systems Fail at Scale Every e-commerce operations team has experienced this: a customer places an order, the system confirms it, and ten minutes later a support ticket arrives because the item was already out of stock. The batch j...
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2d ago · 15 min read · Every production ML system eventually hits the same wall: getting fresh features to models fast enough to matter. You trained a fraud detection model on features computed from the last 30 minutes of user behavior, but in production, your feature stor...
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