TL;DR — Key TakeawaysAnaconda’s acquisition of Kilo Code moves the company beyond Python tooling and into the AI coding-agent layer. Kilo gives Anaconda direct access to the developer interface where models are selected, agents are directed and organizational data is routed.The deal forms part of a broader platform strategy. Anaconda’s package and environment management, Outerbounds’ workflow orchestration and Kilo’s agentic development tools could become an end-to-end enterprise AI development platform.The opportunity is significant, but integration and developer trust will be critical. Anaconda must provide enterprise governance, security and visibility without undermining Kilo’s open-source flexibility, model neutrality or developer experience.The Kilo…
Author: drweb
TL;DR — Key TakeawaysJira wants to run the agentic workflow. Atlassian is turning Jira into the place where AI work gets assigned, tracked, reviewed and governed.Coding agents are moving into Jira. Teams can connect Claude Code, Cursor, GitHub Copilot and Codex directly to development workflows.Jira Coding Agent can turn tickets into pull requests. Developers hand off the task, then review the result.Atlassian today extended the scope of tasks that artificial intelligence (AI) can automate directly from its Jira project management software, including assigning work to an AI coding agent.Initially, Jira integration with AI coding tools includes Claude Code from Anthropic,…
Every check is green. Every validation rule passed. The pipeline reports success in cheerful little checkmarks. And the dashboard is still, somehow, wrong. That gap is exactly what data pipeline monitoring exists to close, and most teams do not have it.This confused me for an entire afternoon once. Every rule I had written was passing, because every rule asked about the contents of a row, and every row was fine. What none of them could tell me was that yesterday’s file had never arrived. The table was full of perfectly valid data that was exactly one day stale.My checks were…
AI has moved very quickly from experimentation to production. A few years ago, many organizations were still asking whether AI could improve their products or internal workflows. Today, the question is different: how can teams ship AI-enabled software safely, reliably, and responsibly?That shift matters because AI is no longer just a research project or a boardroom talking point. It is being added to customer support platforms, fraud detection systems, developer tools, compliance workflows, cloud operations, marketing engines, and enterprise applications. The opportunity is real, but so is the risk.Traditional software usually behaves in predictable ways. If the logic is written…
The slides for my session “The €100 data warehouse on the Azure data platform” can be found on GitHub. It was a calm event, probably because of the good weather outside The post DataSaturday Rheinland – Slides first appeared on Under the kover of business intelligence.
This is actually inspired by an article SQL Server Central, which taught me something new. I decided to verify what was in the article and do some research. The summarytl;dr if you change the schema owner, all permissions are dropped.Another post for me that is simple and hopefully serves as an example for people trying to get blogging as #SQLNewBloggers.The ScenarioWe start by creating three logins and their corresponding database users. Think of them as three colleagues with different roles:User1 — will own the schemaUser2 — will be granted access to a tableUser3 — will eventually take over schema ownershipImagine…
Senior Laravel Engineer
GitHub has made its overhauled pull requests dashboard generally available, giving developers and engineering managers a single view at github.com/pulls to track, prioritize, and act on the pull requests that need their attention.The dashboard moves out of public preview after a rollout that started in March 2026 and shifted to opt-out preview in April. The centerpiece is Inbox, a home base that surfaces review requests, pull requests that need fixing due to CI failures or new comments, and pull requests that are ready to merge or sitting in the merge queue. Developers can reorder or hide sections to fit their…
A team I worked with was proud of their data quality checks, and they had earned it. Dozens of validations, all thoughtful, all catching real problems. Then a bad batch sailed straight into the published table, got read by three dashboards, and set off the usual fire drill.The checks were not wrong. Every one of them fired. They fired at the very end of the pipeline, after the data was already published, which is a bit like wiring your smoke detector to go off the morning after the fire. Technically accurate. Not much help.A check is only as useful as…
IBM has rolled out a significant update to Bob, its agentic software development platform, adding multi-agent coordination, built-in spend tracking, and three prebuilt workflows aimed at some of the toughest modernization jobs in enterprise IT: Mainframe COBOL, IBM i, and large Java codebases.The update lands as most engineering organizations are running into a problem nobody predicted a year ago: AI made writing code the easy part. A recent GitLab survey found that 85% of respondents agreed that AI has shifted the main bottleneck from writing code to reviewing and validating code. IBM cites that same data point in its announcement,…