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Resque v Sidekiq for Ruby Background Jobs Processing

Background job processing is integral to modern software architecture. Background jobs allow resource-intensive tasks to be handled asynchronously, improving your application’s responsiveness and efficiency. You can use background processing for tasks such as sending emails, data processing, and batch jobs. If you were to run these synchronously, they could significantly degrade the user experience and system performance. Thus, most frameworks have libraries for running background jobs.

Revolutionizing Financial Services with AI: Harnessing Speed and Real-Time Data in the Cloud

In today's financial services landscape, the need for speed is paramount. Traditional financial processes are no longer sufficient to meet the demands of modern consumers and businesses. The synergy of three emerging technologies promises to expedite financial services processes: By implementing a data fabric, financial institutions can break down silos, enabling data to flow freely across the organization.

S1.E7: What is quality engineering? | QA Therapy Podcast

Today, we're joined by our expert QA Therapist, Antoine Craske, who's here to diagnose and prescribe solutions for symptoms related to Quality Engineering. Hailing from France and working as a Software Engineer at La Redoute, Antoine brings a wealth of experience to the table. He emphasizes that quality isn't the responsibility of just one person or team; it's a collective company goal. To achieve this, leveraging tools is essential.

What is a Headless Data Architecture?

The headless data architecture. Is it a fad? Some marketecture? Or something real? In this video, Adam Bellemare takes you through the basics of the headless data architecture and why it’s beginning to emerge as its own respective pattern. Driven by the decoupling of data computation from storage, the headless data architecture provides the basis for a modular data ecosystem. Stream your data for near real-time low latency use cases, or convert it to an Iceberg table for analytical use cases.

GPUs Public Preview: Run AI workloads on H100, A100, L40S, and more

Welcome to day two of Koyeb launch week. Today we're announcing not one, but two major pieces of news: Our lineup ranges from 20GB to 80GB of vRAM with A100 and H100 cards. You can now run high-precision calculations with FP64 instructions support and a gigantic 2TB/s of bandwidth on the H100. With prices ranging from $0.50/hr to $3.30/hr and always billed by the second, you'll be able to run training, fine-tuning, and inference workloads with a card adapted to your needs.

Empowering Enterprise Generative AI with Flexibility: Navigating the Model Landscape

The world of Generative AI (GenAI) is rapidly evolving, with a wide array of models available for businesses to leverage. These models can be broadly categorized into two types: closed-source (proprietary) and open-source models. Closed-source models, such as OpenAI’s GPT-4o, Anthropic’s Claude 3, or Google’s Gemini 1.5 Pro, are developed and maintained by private and public companies.