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How Pandas chunking, Dask, and Polars help process millions of records when adding more compute isn't an option. The post What Can We Do When Memory Becomes the New Bottleneck in Data Engineering? appeared first on Towards Data Science.
I’ve been thinking about how teams measure latency for LLM API calls in production. A lot of dashboards seem to start with one number: total response time. That is useful, but I’m finding it too blunt. Two requests can both take 20 seconds and feel completely different: one starts streaming tokens a
Next.js 16.3: Instant Navigations Next.js 16.3 preview introduces "Instant Navigations", a new way to make app navigations feel much faster. There are also new dev tools like Instant Insights, a Navigation Inspector, and a Playwright helper for testing instant page changes ► What RSCs Can Do in Next
A week ago I shipped Prism's edge layer and wrote a blog post explaining what was good about it and what was wrong with it. The good: bad keys now get rejected from your nearest Cloudflare PoP, no Mumbai round-trip. The wrong: cache hits were still landing in 300–500 milliseconds because the cache i
NumPy vs List: SIMD Vector Processing with Concrete Mathematical Example Hussein Mahdi Feb 9 #webdev #ai #programming #tutorial Add Comment 4 min read
Dynamic memory allocation in C is flexible, but it becomes a bottleneck in systems where objects are created and destroyed frequently. Repeated malloc/free calls can introduce overhead, fragmentation, and unpredictable latency. To avoid that, I built a small object pool in C89. The idea is simple: i