#WorkLoad

The thin portable MacBook Air has been refreshed and given the M5 chip, giving it speed, power, and AI capabilities for zooming through your workload. ...

After returning from AWS Summit London 2026 I was doing some research on running AI/ML workload in...

From Dev.to - ai: Skuphysics | Azure VM Performance Engineering from SKU Physics to Cloud-Scale Mastery | R.A.H.S.I. Framework™ Analysis

"What's the best model?" used to be a meaningful question. Today it has the wrong shape. The useful...

RFC 7523 jwt-bearer grant for the Claude API. SPIFFE, EKS IRSA, GitHub Actions OIDC, Kubernetes projected SAs — all federate to short-lived tokens.…

From Dev.to - postgresql: Internals: How PostgreSQL 17's New B-Tree Index vs. MongoDB 8.0's WiredTiger Engine Improves Query Performance in 2026

From Dev.to - mongodb: Benchmark: MongoDB 8 vs. FerretDB 1.8 for MongoDB-Compatible Workloads on Azure Cobalt 100

From Dev.to - machinelearning: How I finally stopped rewriting deployment configs every time I switched GPU providers

A practical walkthrough of real AI workloads developers can run on Jungle Grid, from inference to batch jobs and agent-triggered execution.

In the last few weeks the Model Context Protocol surface for crypto trading has filled out in a...

Serverless and containers solve overlapping problems. Both abstract away the work of managing...

Note: This article is adapted from the official Phala Network post: “dstack: One Confidential Compute...

Six months after migration, your cloud bill runs 40% higher than projected. On top of that, three...

From Dev.to - machinelearning: How I used Launch Templates to deploy AI workloads elastically across GPU providers and finally avoided vendor lock-in

​ Hi everyone, I recently started a food service job in Japan (meal preparation), and I’d appreciate some perspective from people with experience working here. I came to Japan with a few others, but we were assigned to different locations.…

This post introduces Dynamic Context Parallelism (Dynamic-CP), a scheduling approach in NVIDIA Megatron Core used for LLM post-training or DiT pre-training.

Optimizing the cost-effectiveness of cloud infrastructure requires the careful placement of workloads in the environments best suited to their execution.

Understanding which cloud platforms are the best fit for which workloads can maximize your return on investment and your customers’ output.