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Laracon US 2026 is the flagship conference for the Laravel community and one of the leading events for modern web application development. The conference attracts developers, software architects, startup founders, and technology leaders interested in building scalable, high-performance web applications.

ÜberConf 2026 brings together software developers, architects, DevOps engineers, and technology leaders for four days of immersive technical learning and professional networking. Hosted by the No Fluff Just Stuff community, the conference is recognized for delivering practical, hands-on content focused on solving real-world software development challenges.

Datadog is expanding its AI-powered observability portfolio with new developer-focused tools aimed at helping engineering teams manage increasingly complex AI-driven applications and workflows.

Red Hat announced a major expansion of its developer portfolio this week with new tools focused on agentic AI development, signaling how enterprise software companies are rapidly adapting to the next phase of AI-powered software engineering. The announcement, made during the company’s Red Hat Summit event in Atlanta, introduced Red Hat Desktop and new capabilities within Red Hat Advanced Developer Suite.

Even though many in the IT industry are worrying about how AI will replace them, we need to recognize the advantages AI brings to business. In this podcast, Botond Seres and Dave Erickson are going to examine strategies for using AI to scale up your business - with Phil Masiello, CEO & Founder of CrunchGrowth.

AI innovation runs on data, but the industry has developed a bad habit of pretending that its data practices are simpler, cleaner, and more controlled than they really are. Behind the dashboards and governance slogans lies a growing discomfort: the more data organizations collect, the harder it becomes to justify how it’s used, secured, and shared. This tension sits at the heart of modern AI development - and it’s exactly where federated machine learning begins to change the story. 

Everyone has had that moment: a search or recommendation engine suggests something so oddly specific that it feels either magical or mildly unsettling. “How did it know I wanted that?” quickly becomes, “Should I be concerned that it knows that?” That tension - between delight and skepticism - is exactly where explainable AI enters the conversation.

The Automate 2026 (June 22–25 in Chicago) is set to showcase how software, AI, and robotics are converging to transform real-world operations. Organized by Association for Advancing Automation, this event brings together engineers, developers, and industry leaders working at the intersection of automation and intelligent systems.

Emergent has raised $70 million in new funding as it looks to accelerate the adoption of no-code software development across enterprise and mid-market organizations. The investment underscores growing demand for platforms that allow non-technical users to build, deploy, and scale applications without writing traditional code.

OpenAI and ServiceNow have announced a strategic partnership aimed at embedding advanced AI agents directly into enterprise software development workflows. The collaboration is designed to help organizations automate complex tasks, accelerate development cycles, and improve operational efficiency across IT and business teams.

Like a snake slithering in the grass, Technical Debt has an uncanny ability to stay hidden - until it suddenly does not. It rarely announces itself with flashing warnings or dramatic system failures. Instead, it shows up as small annoyances: a feature that takes longer than expected, a bug that “should have been simple,” or a release that feels riskier than it used to.

The central question is no longer just whether machines can write better tests than humans. It is whether AI systems - training pipelines included - can be built to respect privacy by design. Generative AI in testing relies heavily on data. Systems such as large language models, anomaly detection engines, and automated test case generators need vast and diverse datasets to function effectively. Yet this data dependency increasingly clashes with emerging privacy regulations that limit how persona