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90 results found
Special Olympics: From Paper Schedules to the Most Ambitious App in USA Games History
Using AI, WWT built the Champions app to deliver more than anyone thought possible for every athlete, coach, fan, volunteer, delegate and guest in attendance.
The Great Unlock: An Executive Guide to AI-Native Engineering
How C-suite leaders can turn AI-native engineering into faster execution, stronger customer outcomes and durable competitive advantage.
Navigating the Mythos Reality: 3 Questions Every Exec Must Answer About Frontier AI
Frontier AI is moving faster than traditional cyber defenses while access to models such as Mythos and Fable can change without warning. In this episode of the AI Proving Ground Podcast, WWT's Chief National Security and Critical Infrastructure Advisor Madison Horn and Field CTO Robert Geis explain why security leaders must look beyond any single model and answer three urgent questions: How do we defend at machine speed? Which tools can we trust? And who is accountable when a critical dependency changes or disappears?
Why 82% of Your AI Coding Budget Isn't Shipping Software
AI can now write code and accelerate development, but using AI tools isn't the same as becoming AI-native. Recorded at Cisco Live, this episode of the AI Proving Ground Podcast features Cisco SVP and Chief Product Officer of the Cisco Security Product Management organization Raj Chopra, WWT SVP of Global Solutions and Architecture Brian Ortbals and WWT VP of Digital Experiences Joe Berger discussing how AI is reshaping development and transforming the enterprise.
What Good Looks Like: Early Signs AI-native Engineering is Working
The indicators that AINE is taking hold are operational and behavioral, not financial. Here is what leaders should watch for.
How to Pick the Right LLM
Welcome to the How to Pick the Right LLM Learning Path! Designed for engineers and technical practitioners, this course moves beyond benchmark-driven defaults to build a repeatable approach to model selection, rooted in a core truth: there is no single best model, only the right model for a specific task. You will first master a framework built around six key selection factors: use case, performance, latency, cost at scale, deployment, and security. From there, the path dives into the distinct LLM call types that power agentic systems—including classification, planning, and tool dispatch—and explores how architectural properties like reasoning mode and structured-output compliance dictate fit. Finally, you will jump into a hands-on JupyterLab environment to benchmark models across four capability tiers on canonical agent tasks. By measuring real-world latency and token consumption, you will build a data-driven scorecard to confidently design optimized, multi-tier model architectures.
Beyond the Prompt: Closing the AI Delivery Gap with AI-Native Engineering
AI has reshaped how software gets written, but not yet how it gets shipped. Teams are generating more code than ever, yet delivery metrics haven't kept pace, creating a widening AI Performance Gap between high performers and everyone else. This article explores that productivity paradox, the three pillars of AI-native engineering, and how to rebuild your delivery pipeline to match the speed and risk of machine-generated code.
The AI-Native Engineer Framework: Three Questions, Nine Scenarios
When you have a task in front of you, how do you actually decide which tool to reach for? This part gives you a framework that makes most tool decisions take seconds: three questions (scope, reasoning depth and autonomy) applied across nine common scenarios, from writing features in familiar code to multi-repo migrations to production debugging. This is the reference piece of the series.
The AI-Native Engineering Moat and Why Same-Tool Teams Pull Apart
Two teams running the identical Cursor or Claude Code installation can produce wildly different output; the gap almost always traces back to one thing. This part covers the extensibility layer: skills, subagents and Agent Teams, rules, MCP, the emerging ACP standard, plugins and hooks. Tool selection is a thirty-minute decision; building this layer is a six-month investment that compounds into a real competitive moat.
AI-Native Engineer Playbook: Tool Convergence and Why Your 2024 Tool Map Is Obsolete
The mental map most teams still use — Copilot is autocomplete, Cursor is autocomplete-plus, Claude Code is the terminal one, Devin is the autonomous one — was roughly right eighteen months ago and is wrong now. This part walks through where the six major platforms (Cursor, GitHub Copilot, Devin Desktop, Claude Code, OpenAI Codex, and Google Antigravity) actually stand today, and the four dimensions that still separate them once raw capability no longer does.
The AI-Native Engineer's Playbook
Most teams adopt AI-native engineering as a tool decision — pick Copilot or Cursor or Claude Code, roll it out, call it done. That's the mistake. The real shift is in the practitioner's job, which moves from writing code to orchestrating its production. This first part of the series breaks down the five activities that now define the role: scoping, delegating, reviewing, extending and redirecting.
The AI-Native Engineer Discipline: Stacks That Compound, Patterns That Don't
The final part of the series covers how to assemble a stack that holds up: the four patterns that work for most teams, from the two-tool stack to the vertically integrated option, plus the role-specific approach. It closes with the anti-patterns that quietly waste tool capability — model-version chasing, skipping the extensibility investment, delegating unscoped work — and the bottom line on what separates teams getting compound returns from teams just producing more code.