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Development

Featured in Development

Designing for Concurrency: the Hilbert’s Hotel Problem in Go

In this article, we want to show how achieving concurrency is the result of an appropriate design. A concurrent solution may turn out to be more elegant and easier to reason about than an equivalent sequential algorithm. To illustrate these concepts we use, as an example, the Hilbert’s Hotel mathematical problem.

All in development

Architecture & Design

Featured in Architecture & Design

Expanding GraphQL Federation at Netflix with Tejas Shikhare

GraphQL can be a great choice for client to server communication, but it requires investment to maximize its potential. Netflix operates a very large, Federated GraphQL platform. Like any distributed system, this has some benefits, but also creates additional challenges. In this episode, Tejas Shikhare explains the pros and cons of scaling GraphQL adoption.

All in architecture-design

AI, ML & Data Engineering

Featured in AI, ML & Data Engineering

Resilient Real-Time Data Streaming across the Edge and Hybrid Cloud

Kai Waehner explores different architectures and their trade-offs for transactional and analytical workloads. Real-world examples include financial services, retail, and the automotive industry.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

Sustaining Fast Flow with Socio-Technical Thinking

Nick Tune shares principles and practices from the fields of DDD and Team Topologies that leaders can apply to create high-performing teams and sustainable flow throughout their organization.

All in culture-methods

DevOps

Featured in DevOps

If Twitter Doesn’t Have a Staging Environment, Should Anyone?

With Twitter revealing they do not have a staging environment, how important is staging to delivering quality? When so many tasks are shifting to the developer these days and no one best developer approach fits all, how do dev teams determine the best developer methodology for them and their company?

All in devops

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Apple Adds Core ML Support for Stable Diffusion on Apple Silicon

Apple released a set of optimizations to Core ML to enable running the Stable Diffusion text-to-image model on Apple Silicon-powered devices running the latest iOS or macOS versions, respectively iOS 16.2 and macOS 13.1.

Sergio De Simone
on Dec 03, 2022

Topics

Designing for Concurrency: the Hilbert’s Hotel Problem in Go

Expanding GraphQL Federation at Netflix with Tejas Shikhare

Resilient Real-Time Data Streaming across the Edge and Hybrid Cloud

Sustaining Fast Flow with Socio-Technical Thinking

If Twitter Doesn’t Have a Staging Environment, Should Anyone?

Helpful links

Choose your language

News

Apple Adds Core ML Support for Stable Diffusion on Apple Silicon

Designing for Concurrency: the Hilbert’s Hotel Problem in Go

Article Series: PHP 8.x

The Six Ways of Optimizing WebAssembly

Avoid Being an "Ivory Tower" Architect: the Relationship between Architects and Their Organisation

Expanding GraphQL Federation at Netflix with Tejas Shikhare

No Next Next: Fighting Entropy in Your Microservices Architecture

Sustaining Fast Flow with Socio-Technical Thinking

Test Automation Requires a Strategy and Clean Code

Your Tech Stack Doesn’t Do What Everyone Needs It To. What Next?

Stanford Researchers Present AI Framework to Implement and Validate Complex Algorithms

Google Unveils MusicLM, an AI That Can Generate Music from Text Prompts

DeepMind Announces Minecraft-Playing AI DreamerV3

Permit Elements Enables Low-Code User-Managed Access Control

Infrastructure from Code: The New Wave of Cloud Infrastructure Management

AWS Patches Undocumented APIs Bypassing CloudTrail Event Logging

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