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Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.
Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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Carlos Arguelles spoke about Amazon’s inflection points in engineering productivity at QCon San Francisco, where he explained that shift testing left can help catch issues early. He suggested using guardrails such as code reviews and coverage checks. Your repo strategy, monorepo or multirepo, will impact the guardrails that need to be in place.
By Ben LindersBibek Bhattarai details Intel's AMX, highlighting its role in accelerating deep learning on CPUs. He explains how AMX optimizes GEMM operations using low-precision data (INT8, BF16) and tile-based memory management, boosting performance and efficiency. He shows how to leverage AMX through frameworks like TensorFlow/PyTorch or Intel tools for substantial gains in CPU-deployed AI models.
By Bibek BhattaraiRecently, Pinterest disclosed its internal orchestration framework, called Hadoop Control Center (HCC), to automate the scaling and migration of its large-scale Hadoop clusters. This move addresses the operational complexity and limitations Pinterest previously faced when managing thousands of nodes across dozens of YARN clusters on AWS.
By Claudio MasoloAWS's new Fair Queues for Amazon SQS revolutionize message handling in multi-tenant systems by mitigating the "noisy neighbor" issue. This feature ensures low message dwell times for quieter tenants without requiring code changes, enhancing both performance and fairness. Developers can effortlessly implement this capability and maintain consistent service quality across applications.
By Steef-Jan WiggersTraditional data lakes aregreat for storing massive amounts of stuff, but they're terrible at the transactional guarantees and versioning that ML workloads desperately need. Apache Iceberg and SparkSQL bring database-like reliability to your data lake. Time travel, schema evolution, and ACID transactions help support reproducible machine learning experiments.
By Anant Kumar
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