IBM and Red Hat are bringing together what they’ve learned from frontier AI models and 20,000 engineers to launch Project Lightwell, a $5 billion initiative aimed at helping enterprises better secure their open source software, work that has become more challenging in the age of such models as Anthropic’s Claude Mythos Preview.Mythos and similarly powerful frontier models are quickly collapsing the exploit window for organizations, reducing from weeks to days or hours the time between vulnerability detection and patching. IT and security vendors are scrambling to develop AI-powered protections and processes to match the machine speed at which bad actors…
Author: drweb
Our latest State of Software Delivery report analyzed more than 28 million CI workflows and found a pattern that should give engineering leaders pause. Average throughput grew 59% year over year. Main branch activity for the median team declined 7%. Teams are generating more code than ever before. Less of it is reaching production.The cost of poor validation used to show up mostly in developer hours: debugging, blocked deployments, context switching. That cost hasn’t gone away. But there is a second bill now. Every failed build means agent retries. Every slow pipeline is compute burning while an agent waits. Main…
Agentic SRE is the evolution of site reliability engineering where AI agents help observe systems, reason over telemetry and take bounded operational actions under human-defined guardrails. The goal is not to replace SREs, but to reduce toil, speed up diagnosis and make incident response more consistent and scalable. Why This Matters Modern systems are too distributed, noisy and fast-moving for purely manual operations to keep up. Engineers spend significant time correlating dashboards, reading logs, checking recent deploys and hunting for context before they can even start fixing the problem. Agentic SRE addresses this by turning telemetry into actionable context and automating safe parts of the response loop. This shift is especially important because reliability work is full of repetitive, high-pressure…
One thing many people miss after switching from Windows to Linux is a good download manager. Tools like Internet Download Manager (IDM) and Download Accelerator Plus are popular on Windows, but they aren’t available natively on Linux. The good news is that Linux has several excellent alternatives that offer the same core features, including multi-threaded downloads, browser integration, download scheduling, and the ability to pause and resume downloads. To help you find the right tool, we’ve put together a list of 10 download managers that are actively maintained and work well on modern Linux distributions in 2026. If you’re setting…
You built the agent. It works in testing. Then it hits production and starts giving wrong answers, timing out or burning through your token budget, and you have no idea why. This is when developers discover that print statements and log files weren’t designed for this. LLM applications fail in ways that traditional tooling can’t see. A hallucination doesn’t throw an exception. A slow retrieval step doesn’t show up in CPU metrics. A prompt that worked yesterday silently degrades today. The fix is observability, and the standard for doing it right is OpenTelemetry (OTel). What OpenTelemetry Actually Is OTel isn’t a monitoring product; it’s a vendor-neutral specification under the CNCF that defines a standard way to collect…
Most enterprise AI projects start with retrieval.You connect Jira, Confluence, SharePoint, and Slack. Maybe a few internal databases nobody has touched in five years. You tune embeddings, optimize chunking, wire up a vector database, and convince yourself you’ve built an AI-powered knowledge system.Then the model server crashes. And suddenly, you discover the uncomfortable truth about enterprise AI: The hard part was never retrieval. It was infrastructure.For the past two years, the industry has treated LLM deployment like a feature integration problem. In reality, it is rapidly becoming a platform engineering problem, one involving GPU orchestration, scaling economics, governance boundaries, workload…
Over the past year, AI has fundamentally changed how software is written. Infrastructure code is no exception. Tasks that once required deep familiarity with tools, syntax, and workflows can now be handled through natural language. Engineers are no longer starting from a blank file. In many cases, reviewing and modifying code generated for them has become the norm.At a high level, this looks like progress, and in many ways, it is. Teams can move faster, the barrier to entry is lower, and experimentation is easier. But there is a growing gap that many organizations are only beginning to recognize: AI…
Several years ago, the observability community reached what felt like a consensus: The three pillars — logs, metrics and traces. Instrument everything, ship it all to a central platform and you will finally understand what your system is doing. It’s a tidy framework. Yet it turns out to be incomplete in ways that only become obvious once you’re actually trying to debug a production incident with it. This article isn’t an argument against logs, metrics and traces; you need all three. However, there’s a growing set of failure modes in modern distributed systems that the three-pillar model struggles to explain — and understanding why is the first step toward building observability that…
Installing and configuring cloud-init on Ubuntu 26.04 makes it much easier to automate server setup, especially when working with cloud VPS systems, virtual machines, and home lab deployments. If you’ve ever installed a fresh Ubuntu server and spent the next 20 minutes creating users, installing packages, configuring SSH keys, and adjusting networking manually, cloud-init can save yourself a lot of repetitive work. Cloud-init is the initialization service used by most cloud platforms and virtual machine images. It reads configuration data during the first boot of a Linux system and automatically applies settings like hostname changes, user creation, SSH configuration, package…
The first time an enterprise AI system gives a wrong answer, people usually blame the model. Let us talk Enterprise AI’s Hidden Problem Is Organizational Amnesia in detail.AI does not fail only when it forgets. It also fails when the company remembers badly.Why the smartest model in the room still fails when the company cannot remember what it knowsThe first time an enterprise AI system gives a wrong answer, people usually blame the model.They say the model hallucinated. They say the prompt was weak. They say the vendor overpromised. They say the technology is not mature enough yet. Sometimes that…