Large codebase search benchmarks for Cursor, Windsurf, Claude Code, Copilot, Codex, and Jolt

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Presentation: Inflection Points in Engineering Productivity as Amazon Grew 30x

Transcript Arguelles: My name is Carlos Arguelles. I'm a Senior Principal Engineer in Amazon Worldwide Stores. I'm here to talk about inflection points that I got to see throughout my time at Amazon. It was an interesting time because the company grew by about 3000% while I was there. Lots of interesting little turns and so forth. First, I'm going to...
WebAssembly: Yes, but for What?

June 30, 2025Volume 23, issue 3 PDF WebAssembly: Yes, but for What? The keys to a successful Wasm deployment Andy Wingo WebAssembly (Wasm) turns 10 this year, which, in software terms, just about brings it to the age of majority. It has been polished, prepared, explored, and deployed, but in the language of American speculative fiction author William Gibson, we are...
Context Engineering: Going Beyond Prompt Engineering and RAG

The initial wave of large language model (LLM) development centered on prompt engineering – carefully crafting the right question or instruction to coax a desired answer. LLM developers and enthusiasts prided themselves on clever one-shot prompts like “You are an expert X. Do Y like Z”; and the term “prompt engineer” gained traction accordingly. However, as LLM-powered applications have grown...
How to evaluate throughput utilization for Amazon DynamoDB tables in provisioned mode

Amazon DynamoDB is a serverless, NoSQL, fully managed database service that delivers single-digit millisecond latency at any scale. In provisioned mode of DynamoDB, you pay for the capacity you provision, regardless of actual usage. The percentage of consumed capacity relative to provisioned capacity is known as throughput utilization. In this post, we demonstrate how to evaluate throughput utilization for DynamoDB...
Google's BigTable Paper Explained

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Category: Big Data

pandas on spark apply_batch/transform_batch broken? (tl;dr; No – but it isn’t well documented)

pandas on spark apply_batch/transform_batch broken? (tl;dr; No – but it isn’t well documented)

Published by Arnon Rotem-Gal-Oz on October 16, 2022

Using pypark’s pandas integration via apply_batch and transform_batch is very powerful but lacking documentation can cause hard to trace bugs – hopefully my experience (below)…

Intro to Apache Spark (slides)

Published by Arnon Rotem-Gal-Oz on December 16, 2020

I gave a general overview of Apache Spark to our R&D teams. You can find the slides below

Where is Apache Spark heading?

Where is Apache Spark heading?

Published by Arnon Rotem-Gal-Oz on December 4, 2020

I watched (COVID19-era version of “attended”) the latest spark Summit and in one of the keynotes Reynold Xin from Databricks, presented the following two images…

Big data isn’t – well, almost

Big data isn’t – well, almost

Published by Arnon Rotem-Gal-Oz on March 23, 2019

Back in ancient history (2004) Google’s Jeff Dean & Sanjay Ghemawat presented their innovative idea for dealing with huge data sets – a novel idea…

Big data in the cloud – welcome to cost oriented design

Big data in the cloud – welcome to cost oriented design

Published by Arnon Rotem-Gal-Oz on April 1, 2016

A couple of weeks ago I presented @ BDX2016 The slides are available on Slideshare Big data in the cloud – welcome to cost oriented…