xAI
xAI
xAI
Machine Learning
WebAssembly
CSS
Memory and Hardware
In this video, we look at how a process’s virtual address space is laid out: code, data, heap, stack, shared libraries, mmap regions, and more.
Python 3.14.0 introduced a new incremental garbage collector. But reports of higher memory usage caused the Python team to revert the garbage collector changes in 3.14.5.We investigate how memory management works in Python and workloads that perform best and worst for the incremental garbage collector.
Agentic Commerce and Payments
Meta
Defense Tech
LLM Evaluation
Mathematics
Developer Tools
WebSockets
Concurrency
C++
Containers
Time-Series Databases
Stripe
Cloud Providers
Startups and Venture
C++
Network Security
I've spent months building a RAG system over a large, dense regulatory/compliance corpus and I genuinely can't tell anymore whether the architecture is "appropriately complex for the problem" or whether I've talked myself into a monster. Looking for a gut check from people who've shipped this stuff. The corpus, to give you a feel without naming it: - Long legal-technical documents, heavily cross-referenced (one section will point to 5-15 others, plus external standards). - The *answers people...
PostgreSQL
The Postgres community values feedback from testing of Beta releases, and with Docker it’s been easier to get pre-release versions up and running. With the recent announcement of PostgreSQL 19 Beta 1, let’s get that running and test some of the new capabilities. Pre-Release Versions of Postgres with Docker First, you’ll need to install Docker for your OS! Grab the version needed for your OS and processor architecture, for example ARM or AMD/Intel/x86. On MacOS run uname -m or sw_vers...
Vector Databases
AI Psychosis
CDN and Caching
NVIDIA
PostgreSQL
PostgreSQL solves a bootstrapping puzzle with `data_directory`: how to find the config file before knowing where the data lives.
Microsoft
xAI
MySQL
Etsy has maintained a sharded MySQL architecture since around 2010. This database cluster contains most of Etsy’s online data and is made up of ~1,000 tables distributed across ~1,000 shards. Over the last 16 years, it has grown significantly: combined, these tables have over 425 TB of data and receive roughly 1.7 million requests per second. Etsy engineers access our MySQL data through a proprietary object-relational mapping (ORM). The ORM has a corresponding model for each MySQL table. W...
Artificial Intelligence
When buyers search on Etsy, they need to quickly and easily find the perfect item. At the same time, sellers need to be confident their unique products are being seen by the right customers. Our Ads Search ranking model, which is built on a multitask learning foundation, is the critical link in this connection. Recently, we identified an opportunity to drive more meaningful buyer engagement by enhancing our model’s ability to predict purchase intent. We achieved this via a dual-pronged impr...
RAG
Etsy’s marketplace is defined by the creativity and craftsmanship of our sellers and the hundreds of millions of highly diverse products they offer. You can find silversmiths who cold-forge recycled sterling silver, weavers who dye raw fleece with indigo and black walnut, and ceramicists who throw stoneware on a kick wheel. These details define each product and often determine whether it matches a buyer’s taste, style, and interests. Sometimes buyers know exactly what they want, searching...
Apple
Startups and Venture
MySQL
Etsy has maintained a sharded MySQL architecture since around 2010. This database cluster contains most of Etsy’s online data and is made up of ~1,000 tables distributed across ~1,000 shards. Over the last 16 years, it has grown significantly: combined, these tables have over 425 TB of data and receive roughly 1.7 million requests per second. Etsy engineers access our MySQL data through a proprietary object-relational mapping (ORM). The ORM has a corresponding model for each MySQL table. W...
Artificial Intelligence
When buyers search on Etsy, they need to quickly and easily find the perfect item. At the same time, sellers need to be confident their unique products are being seen by the right customers. Our Ads Search ranking model, which is built on a multitask learning foundation, is the critical link in this connection. Recently, we identified an opportunity to drive more meaningful buyer engagement by enhancing our model’s ability to predict purchase intent. We achieved this via a dual-pronged impr...
RAG
Etsy’s marketplace is defined by the creativity and craftsmanship of our sellers and the hundreds of millions of highly diverse products they offer. You can find silversmiths who cold-forge recycled sterling silver, weavers who dye raw fleece with indigo and black walnut, and ceramicists who throw stoneware on a kick wheel. These details define each product and often determine whether it matches a buyer’s taste, style, and interests. Sometimes buyers know exactly what they want, searching...
Open Source
MySQL
Etsy has maintained a sharded MySQL architecture since around 2010. This database cluster contains most of Etsy’s online data and is made up of ~1,000 tables distributed across ~1,000 shards. Over the last 16 years, it has grown significantly: combined, these tables have over 425 TB of data and receive roughly 1.7 million requests per second. Etsy engineers access our MySQL data through a proprietary object-relational mapping (ORM). The ORM has a corresponding model for each MySQL table. W...
Artificial Intelligence
When buyers search on Etsy, they need to quickly and easily find the perfect item. At the same time, sellers need to be confident their unique products are being seen by the right customers. Our Ads Search ranking model, which is built on a multitask learning foundation, is the critical link in this connection. Recently, we identified an opportunity to drive more meaningful buyer engagement by enhancing our model’s ability to predict purchase intent. We achieved this via a dual-pronged impr...
RAG
Etsy’s marketplace is defined by the creativity and craftsmanship of our sellers and the hundreds of millions of highly diverse products they offer. You can find silversmiths who cold-forge recycled sterling silver, weavers who dye raw fleece with indigo and black walnut, and ceramicists who throw stoneware on a kick wheel. These details define each product and often determine whether it matches a buyer’s taste, style, and interests. Sometimes buyers know exactly what they want, searching...
NVIDIA
Google
The first rung of the vaunted career ladder now seems frustratingly out of reach for thousands of would-be software engineers.
Startups and Venture
Anthropic
You can use the new console experience to browse and compare the latest AI models on Amazon Bedrock side by side, organize work into projects with streamlined evaluation workflows, and access project-aware documentation with auto-prefilled code snippets ready to copy and run.
NVIDIA
Expansion and condensation reflect two fundamental cognitive directions in knowledge processing and knowledge construction.
Startups and Venture
Linear Algebra
Build Systems
Testing
Search Engines