An Open Data Infrastructure can be secure, too
How open standards give security teams more control, not less.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Inside NERSC at Berkeley Lab: How a DOE Office of Science User Facility Is Exploring ClearML for Scientific AI Workflows
NERSC, the mission high-performance computing center for the U.S. Department of Energy Office of Science, is using ClearML as part of the AI infrastructure stack for Perlmutter, the upcoming Doudna supercomputer, and the broader American Science Cloud. Here is a look at what they are exploring and why it matters for AI for science at scale.
Durable Execution meets Durable Sessions: Resilient AI Agents with Temporal and Ably
Most teams building agents with Temporal have solved the backend problem: crashed workflows restart, LLM call failures retry automatically, and long-running tasks complete reliably. What they haven't solved is the client side -- what happens to the stream when the user's connection drops, when they switch devices, or when two sub-agents are working concurrently and the client needs a single coherent view.
The best tools don't force teams to change how they work
They fit into the workflows, processes, and environments teams already have. As Chris Armstrong, Manager of Developer Relations at SmartBear, explains, every organization is on a different stage of its journey. Some are exploring AI. Others are scaling it. Many are managing a mix of legacy systems, modern platforms, and everything in between. What teams need isn't another platform that demands a complete overhaul. They need solutions that respect their context while helping them move forward with confidence.
Iceberg and Data Sharing
Hey, did you know? The fastest way to AI might not be adding more tools. It might be getting more value from the data you already have. See how Cloudera helps reduce data costs, unify governance, and make enterprise data AI-ready without disrupting operations.
How is Agentic AI rewriting Retail Banking?
Your customers are no longer comparing you to the bank down the street. They are comparing you to Amazon, Netflix, and every hyper-personalized digital experience they interact with daily. And most banks are losing that comparison. Quite literally! Somewhere between the legacy core systems, the compliance overhead, and the quarterly earnings pressure, a tectonic shift has started. Agentic AI is no longer a concept in a research paper.
How We Designed a Node.js Production Debugging Experience with AI
Earlier this year, our team launched the N|Solid Extension, a Node.js production debugging and observability tool designed for modern development environments. The goal was simple: help developers investigate production issues without constantly switching between dashboards, monitoring platforms, and their editor. Instead, runtime telemetry, diagnostics, security insights, and AI-assisted workflows could live directly where developers already spend most of their time.
Neobank vs. Challenger Bank vs. Digital Bank: What You're Actually Building
The global financial landscape has shifted from digital-first to digital-only at a relentless pace. As we navigate 2026, the stakes for fintech founders and engineering leaders have never been higher. According to recent data from Fortune Business Insights, the global neobanking market is currently valued at approximately $310.15 billion, with a projected surge to a staggering $7.6 trillion by 2034.
CDSS EHR Integration Best Practices: A Technical Guide for Engineering Teams
Clinical AI projects usually fail during integration, not development. They work well in controlled environments, but production workflows expose problems. CDS Hooks and FHIR payloads can be inconsistent and incomplete. Engineering teams face a challenge: embedding clinical decision support into existing EHR workflows without disrupting care. The problem is not just about APIs. Teams must manage many things, including CDS Hooks, authentication, and latency constraints.
How a Fractional CMO Turns Marketing Strategy Into Revenue Growth
Most growing businesses reach a point where their marketing stops working as well as it used to. The tactics that got them to a certain size aren't scaling. The team is busy but the results are inconsistent. There's no clear owner of the strategic picture, just a collection of activities running in parallel without a unifying direction.