Explorers, exploiters, and the myth of the 100x engineer
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.
Eira May
How do you turn AI coding chaos into a repeatable playbook?
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.
The new bottleneck
Engineering teams have upgraded their tools. Have they upgraded how they work?
When the cost of code approaches zero, what does engineering leadership look like?
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.
“You can't vibe code scale”: What the AI hype gets wrong about software engineering
Because someone still has to own the consequences of what gets built and whether it can function at scale.
How Braze’s CTO is rethinking engineering for the agentic area
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.
Why AI hasn't replaced human expertise—and what that means for your SaaS stack
The most valuable AI tools in your enterprise stack do more than generate answers. They help developers determine which answers to trust.
What the AI trust gap means for enterprise SaaS
Adoption and trust are moving in diametrically opposed directions, and that gap has real implications for organizations deciding how to spend money on software.
Building a global engineering team (plus AI agents) with Netlify
Dana Lawson, CTO of Netlify, shares her insights on leading a lean, globally distributed engineering team that powers 5% of the internet.
The context problem: Why enterprise AI needs more than foundation models
The difference between AI that impresses people in demos and AI that drives production value is context.
Beyond block or allow: How pay-per-crawl is reshaping public data monetization
For most of the web's history, content platforms operated on a simple binary: open or blocked. Then generative AI changed everything.
Mind the gap: Closing the AI trust gap for developers
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.
Why demand for code is infinite: How AI creates more developer jobs
Not only is there a future for software development, but we’re on the cusp of enormous demand for code developed by humans.
Scaling enterprise AI: lessons in governance and operating models from IBM
Successful implementation and scaling of enterprise AI projects is fundamentally a people and operating model challenge, not just a technology problem.
8 lessons from tech leadership on scaling teams and AI
What we learned from the first year of Leaders of Code.
How Xerox and Stack Overflow partnered to preserve knowledge and power innovation
Read how Xerox achieved an under two-hour 97% answer rate across 400 in-house engineers with Stack Internal.
The AI ick
How we feel about AI-generated content, what AI detectors tell us, and why human creativity matters. Also, what is art?
Essential ingredients for enterprise AI success
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.
Introducing Stack Internal: Powering the human intelligence layer of enterprise AI
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.
Making continuous learning work at work
The most effective learning doesn’t happen in a classroom. It happens during work.
Building stronger engineering teams with aligned autonomy
Striking the balance between speed and strategy is a major challenge for business and tech leaders. That’s where aligned autonomy comes in.
Integrating AI agents: Navigating challenges, ensuring security, and driving adoption
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.
“The future is agents”: Building a platform for RAG agents
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.
