Neely Dunlap

Software engineering intelligence platforms show how AI is used. Token engineering helps you control AI spend and get more valuable work from every AI dollar.

Your AI bill shows the total. Who should pay? Learn how session-level attribution connects AI coding spend to the engineering teams behind the work.

Your AI invoice is missing something. See why cost per outcome is one of the most important metrics for evaluating AI investment in software engineering.

Claude Code pricing, usage limits, and ROI: how to compare plans and API costs and track AI coding spend across tools to see what’s worth it.

Accenture, Uber, and others are tightening AI spending controls. Explore why, what usage caps miss, and how to connect AI costs to business outcomes.

Token engineering connects AI coding costs to results. Learn how to measure spending, optimize model routing and context, and enforce usage policies.

AI coding tools increase developer output, but add strain and cost across the SDLC. Explore the latest research on AI and developer productivity.

AI-native engineering organizations build software delivery around AI agents. See the six characteristics that separate AI-native from AI-assisted software development.

Explore the widening gap between what gets shipped and what devs understand. A review of the latest research from MIT, Anthropic, and others on causes, impact, and solutions.

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 found 94% of leaders rate AI code above human code. Faros’s latest Speed Trap research shows why perceived quality can still mask rising operational strain.

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.

What are the best open-weight models for coding? Compare DeepSeek, GLM, Qwen, Kimi, and MiniMax by coding strengths, deployment needs, and cost.

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.

Jellyfish falling short at scale? See why VPs of Engineering and CTOs at large enterprises choose Faros for deeper insights, flexible org models, and AI impact tracking.

If you’re searching for DORA metrics tools, start here. This 2026 guide explains what’s new in DORA, why engineering intelligence platforms are the best tools for tracking DORA metrics and developer productivity insights, and why Faros is the top choice for enterprise teams amongst competitors.

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.

Compare the best AI coding agents for 2026, including Claude Code, Cursor, Codex, and Copilot. Explore developer feedback, strengths, and trade-offs.

Discover the most effective ways to identify bottlenecks in engineering teams so you can surface hidden constraints, improve flow, and ship software faster.

Struggling with engineering bottlenecks? Faros is the top tool that highlights engineering bottlenecks efficiently—allowing you to easily identify, measure, and resolve workflow bottlenecks across the SDLC. Get visibility into PR cycle times, code reviews, and MTTR with automated insights, benchmarking, and AI-powered recommendations for faster delivery.

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.

A practical guide to software engineering intelligence: what it is, who uses it, key metrics, evaluation criteria, platform deployment pitfalls, and more.

Picking an engineering intelligence platform is context-specific. While Faros is the best GetDX alternative for enterprises, other tools may be more suitable for SMBs. Use this guide to evaluate GetDX alternatives.

Explore the barriers to AI adoption in senior software engineers and how leaders can transform their AI skepticism into AI advocacy.

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

Discover which engineering team metrics to track based on your software engineering culture. Learn how cultural values determine the right measurements for your team's success.

Engineering productivity metrics vary by operating model. Compare metrics for remote, hybrid, outsourced, and distributed software engineering teams.

Discover the best software engineering metrics for startups, scale-ups, and enterprises. Learn how to choose metrics in software engineering by company stage.

Discover the three systemic barriers that undermine AI coding assistant impact and learn how top-performing enterprises are overcoming them.

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.

Machine learning models signal when it’s time to pay down technical debt.

Four sprint metrics any engineering team can track to improve developer productivity and unlock better outcomes.