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AWS targets software release bottlenecks with DevOps Agent update17:37 The problem with software development today may no longer be writing code. With AI coding assistants generating code faster than ever, the bigger challenge is reviewing, testing, and safely releasing it. AWS is betting that software teams need help with that part of the process, adding release management features to its DevOps Agent. The new features, currently in preview, automatically assess co… From RAG to ontology: Databricks bets on context as the key to trusted AI agents13:36 First came vector databases, then RAG. Now, the next frontier in enterprise AI is taking shape: context layers that give autonomous agents a shared understanding of the business, a vision Databricks is advancing with Genie Ontology. Currently in preview, Genie Ontology automatically extracts business context from enterprise data, dashboards, queries, pipelines, documents, and applications and org… Z.ai pitches GLM-5.2 for long-running software engineering tasks12:48 Z.ai has released GLM-5.2, an MIT-licensed open-source AI model designed for long-running software engineering tasks, as the Chinese company seeks to challenge proprietary coding models on cost and performance. The company said GLM-5.2 ranked just behind Anthropic’s Claude Opus 4.8 on FrontierSWE, a long-horizon coding benchmark, trailing it by 1%. Z.ai said the model also edged out OpenAI’s GPT-… 10 tips for getting better R code from your AI coding agent11:05 Most generative AI tools know less about R than languages like JavaScript and Python , thanks to how much training data is available for each. However, with a little extra setup, you can give a large language model (LLM) the knowledge it needs to improve its R skills. Here are 10 ways to help generative AI write R code like a pro. Use a coding agent AI coding agents have more power, flexibility, … Designing frontend systems for cloud latency, not just cloud failure11:05 Frontend reliability is often discussed in terms of outages. Teams prepare for failed API calls, downtime and visible crashes because those failures are easy to recognize and measure. However, in many modern applications, the bigger challenge is not complete failure but latency. Systems rarely go fully offline. Instead, they become slow enough that users lose confidence in the interface long befo… Code like Hemingway11:05 I was blessed with a terrific high school English teacher. Ms. Jewel was funny, kind, interesting, and tough on us. I can still spell “ecstasy” on the first try because of her. One of the more memorable lessons she taught was a “Hemingway and Fitzgerald” module. I loved the short stories of both, but Hemingway’s work always stuck out for me. It’s cliché to say that Hemingway was terse, but we onl… SpaceX’s planned $60 billion deal for Cursor raises questions for CIOs22:49 When SpaceX on Tuesday officially announced its plan to purchase AI coding startup Cursor for $60 billion in stock, as it had predicted it would do in April , it presented CIOs and developers with a little good news, a little bad news and a massive pile of uncertainty. The details of the proposed acquisition were virtually identical to the terms announced in April, even retaining the $10 billion … Databricks pitches LTAP as a new foundation for agentic applications16.června As enterprises rush to build AI agents that can reason over business data and take action, Databricks argues that the long-standing practice of separating operational and analytical data systems is turning into a liability. That separation, the cloud-based data warehouse provider says, is becoming increasingly strained as AI agents require simultaneous access to live operational data and historic… Shipping enterprise-quality code with AI agents16.června Developers are caught between the joy — or pressure — of using agents to ship 10x faster today and the dread of how they will maintain that code tomorrow. The gap between “vibe” code and code that can be deployed to millions of users is vast and easy to underestimate. Closing the gap requires care, expertise, and effort, with the payoff coming later. Agents are able to complete increasingly compl… Develop smarter AI agents with data fabrics16.června Every organization has data scattered across data warehouses, data lakes, SaaS platforms, cloud drives, and data centers. Data fabrics enable organizations to centralize and control data access, making it easier for users, such as data scientists and citizen data analysts , to find and use trusted and governed data sources. Data fabrics, data meshes, and distributed data clouds are all platforms … Nvidia PCs don’t need cloud for AI16.června Nvidia’s new RTX Spark is one of the most interesting personal computing announcements in years. That’s because it’s not just another PC platform, but tries to redefine the role of the personal computer in the age of AI . Announced at Computex 2026, RTX Spark is Nvidia’s new platform for slim Windows laptops and compact desktops, designed to combine an Arm-based CPU, Blackwell-based RTX graphics,… DocLang aims to make documents readable by AI, not humans16.června AIs struggle to understand documents designed for humans; the DocLang working group seeks to flip that imbalance with its specification for machine-readable business documents “built from the ground up for LLM tokenizers.” The working group, founded by IBM, Nvidia, and Red Hat and hosted by the Linux Foundation’s LF AI & Data project, aims to create an open, universal, AI-native document format d… The causes of cloud outages are changing15.června For years, the cloud market has made a simple promise: Move workloads to large-scale platforms, gain better resilience, and worry less about downtime. That promise was never entirely wrong, but it is becoming less complete. The latest findings from Uptime Institute’s seventh Annual Outage Analysis suggest that the outage landscape is changing in ways that should concern both cloud providers and c… 33 LLM metrics to watch closely15.června We’ve all heard the mantra from the quants in the business community: you can’t manage what you can’t measure. And if that’s true for human intelligence, it should be true for the artificial kind too. How do we measure agents and large language models (LLMs)? We’re just beginning to come up with statistical metrics. Here are several of the most common metrics that designers and users toss about w… AI needs young developers – and old developers15.června Enterprises are increasingly investing copious amounts of cash in AI without a lot to show for it. This could be, in part, because the wrong people are leading the change. As I’ve argued before , AI isn’t likely to eliminate developers so much as change what we need from them. For example, we keep asking whether junior developers are needed in a world where large language models can write code f… |