Neely Dunlap

Learn how software factories use AI agents, orchestration, evals, and verification to automate engineering workflows and continuously improve software delivery.

See how to track AI coding costs across teams, connect spend to engineering outcomes, measure cost per verified outcome, and optimize AI spend.

New Relic’s 2026 State of AI Coding found 94% of leaders rate AI code above human code. Faros’s Acceleration Whiplash report shows what happens downstream.

The true cost of AI coding goes far beyond license fees. See how to budget the full cost of buying, running, and maintaining AI coding tools—plus the costs most models miss.

AI coding costs are exploding, but cutting access kills productivity. Faros observes, optimizes, and governs token spend so you lower cost per outcome without slowing engineers down.

More AI token spend doesn’t mean better engineering. Our on-demand webinar shows you what productive AI work actually looks like and how to close the gap between AI spend and business value.

Compare the best open-weight models for coding in 2026—GLM, DeepSeek, Qwen, Kimi and more—and learn when each one can cut your AI coding costs.

Throughput is up, quality is down, and CFOs are asking hard questions. Watch Faros CEO and a McKinsey senior partner unpack the AI engineering gap—and how to close it.

Finance wants to know what AI spend produced. These 3 outcome signals and 11 guardrail metrics give engineering leaders the answer.

Enterprise AI token spend is surging. Learn how AI tokenomics and token intelligence help engineering leaders track, forecast, and control AI costs.

Claude Opus 4.8 hits 88.6% on SWE-bench and 0% hallucination rate on flawed data. See what else is new across agentic SWE performance, prompt injection resistance, tool use improvements, and evaluation awareness risks.

Agent = Model + Harness. Harness engineering is what makes AI agents reliable in production. See the five layers and the metrics that matter.

Tokenmaxxing—treating AI token consumption as a productivity metric—is repeating the lines-of-code mistake. Data from 22,000 developers points to a better way to measure AI engineering impact.

At enterprise scale, AI engineering necessitates a connected system spanning strategy, tooling, cost management, adoption, workforce evolution, measurement, governance, and the context layer that makes AI output production-ready.

Compare top AI coding models and learn how to leverage the best one by matching the model tier and level of effort to each task, using the right context.

A developer-focused look at the best AI coding agents in 2026, comparing Claude Code, Cursor, Codex, Copilot, Cline, and more—with guidance for evaluating them at enterprise scale.

Understand the DRY principle in programming, why it matters for safe, reliable AI-assisted development, and how to prevent AI agents from generating duplicate or inconsistent code.

AI is raising the bar for clarity in engineering workflows. Discover how to create a Jira ticket that’s complete, context-rich, and actionable for both your teammates and the autonomous agents supporting them.

Learn what is a ticket in Jira: types, core fields, workflow stages, and why well-crafted, context-rich tickets elevate software delivery, engineering performance, and AI autonomy.

Discover what data-driven engineering is, why it matters, and the five operational pillars that help teams make smarter, faster, and impact-driven decisions.

Unearth the truth about ghost engineers and the hidden underperformance lurking within engineering organizations.

A guide to converting GitHub Copilot advantages into productivity gains.

Advice and benchmarks for converting GitHub Copilot benefits into meaningful ROI.