Articles on business, SaaS, and the software that powers organizations.
Andi Gutmans, head of Agentic Data Cloud at Google and co-creator of PHP, joins Leaders of Code to talk about why agentic development feels less like a break from the past and more like the next chapter of the same story. This is part one of a two-part conversation.
The “find the special ones and promote their traits” approach isn’t the best or only way to drive AI adoption and productivity on an engineering team.
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.
Inside the pay-per-crawl model colaunched by Stack Overflow and Cloudflare.
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.
Learn how IBM deployed and integrated AI tools in the ultimate enterprise environment.
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.
Lessons learned building a global API platform, navigating hyper-growth, and API-powered AI agents.
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.
Discover how leveraging an intelligent, community-driven knowledge layer is the key to grounding probabilistic tools, preventing AI hallucination, and validating high-quality code.
This episode draws on insights from the 2025 Stack Overflow Developer Survey to equip leaders with ways to navigate the current AI landscape and capture value beyond the hype.
While AI coding assistants are helping developers become more productive, the true value of AI lies in its ability to automate the non-coding tasks that have historically been bottlenecks, allowing leaders to create more agile teams and focus on higher-level strategic problems.
