Vivek Raghunathan, SVP of engineering at Snowflake, joins Leaders of Code at Snowflake Summit to break down the five-stage framework his org used to go from "let chaos reign" to a repeatable, org-wide system for AI-assisted engineering.
Engineering teams have upgraded their tools. Have they upgraded how they work?
On this episode of Leaders of Code, Eric Anderson, director of engineering at Intuit, joins Stack Overflow engineering director Ben Matthews to talk about what happens to software teams when AI makes code generation seemingly free.
Because someone still has to own the consequences of what gets built and whether it can function at scale.
Jon Hyman, co-founder and CTO of Braze, shares how he's led the company's engineering organization over nearly 15 years of growth — and how they transformed into an AI-first team in just a few months.
The most valuable AI tools in your enterprise stack do more than generate answers. They help developers determine which answers to trust.
Adoption and trust are moving in diametrically opposed directions, and that gap has real implications for organizations deciding how to spend money on software.
Dana Lawson, CTO of Netlify, shares her insights on leading a lean, globally distributed engineering team that powers 5% of the internet.
The difference between AI that impresses people in demos and AI that drives production value is context.
For most of the web's history, content platforms operated on a simple binary: open or blocked. Then generative AI changed everything.
Developer trust is synonymous with a willingness to deploy AI-generated code to production systems with minimal human review, as well as assurance that AI tools aren’t introducing unacceptable risks and technical debt that will burden you down the line.
Not only is there a future for software development, but we’re on the cusp of enormous demand for code developed by humans.
Successful implementation and scaling of enterprise AI projects is fundamentally a people and operating model challenge, not just a technology problem.
What we learned from the first year of Leaders of Code.
Read how Xerox achieved an under two-hour 97% answer rate across 400 in-house engineers with Stack Internal.
How we feel about AI-generated content, what AI detectors tell us, and why human creativity matters. Also, what is art?
Here, we’ve distilled the survey findings, laid out action items for leadership, and dug into recommendations around agentic AI for the enterprise. Spoiler alert: It all comes back to data quality.
Today at Microsoft Ignite, we’re showcasing the next step in our evolution: Stack Overflow for Teams is now Stack Internal. It’s the next phase of our enterprise knowledge platform, reimagined for the AI era.
The most effective learning doesn’t happen in a classroom. It happens during work.
Striking the balance between speed and strategy is a major challenge for business and tech leaders. That’s where aligned autonomy comes in.
Positioned at the intersection of automation, decision intelligence, and data orchestration, AI agents are quickly emerging as essential tools for aligning business outcomes with technical workflows.
Douwe Kiela, CEO and cofounder of Contextual AI, joins Ryan and Ben to explore the intricacies of retrieval-augmented generation (RAG). They discuss the early research Douwe did at Meta that jump started the whole thing, the challenges of hallucinations, and the significance of context windows in AI applications.
Christophe Coenraets, SVP of Developer Relations at Salesforce, tells Eira and Ben about building the new Salesforce Developer Edition, which includes access to the company’s agentic AI platform, Agentforce. Christophe explains how they solicited and incorporated feedback from the developer community in building the developer edition, what types of AI agents people are building, and the critical importance of guardrails and prompt engineering.
