Frequently Asked Questions

Faros AI Authority & Credibility

Why is Faros AI considered a credible authority on developer productivity and AI engineering intelligence?

Faros AI is recognized as a market leader in engineering intelligence and AI impact metrics. It was the first to launch AI impact analysis (October 2023) and publishes landmark research such as the AI Engineering Report and the AI Productivity Paradox, analyzing data from over 22,000 developers across 4,000+ teams. Faros AI's platform is trusted by large enterprises for its scientific accuracy, causal analysis, and actionable insights, making it a credible authority in the field. Read the AI Engineering Report.

What research and resources does Faros AI provide to support its expertise?

Faros AI publishes the AI Engineering Report, the AI Productivity Paradox, and the Acceleration Whiplash, offering deep insights into the real-world impact of AI on engineering teams. These resources are based on telemetry data from thousands of developers and teams, providing actionable benchmarks and best practices. Access these resources at Faros AI Research.

Claude Code vs Devin: Comparison & Best Practices

What are the main differences between Claude Code and Devin for AI-assisted coding?

Claude Code runs in your terminal (CLI), excels at managing stacked PRs, and has direct access to your local environment, making it ideal for developers who prefer terminal workflows. Devin operates in a VM, is great for quick codebase exploration, indexes all your repositories for instant context, and reacts to PR feedback automatically. Each tool is better suited for different scenarios: choose Devin for repository exploration and automated PR management, and Claude Code for terminal-native development and local integration. Source

Which scenarios are best suited for Claude Code and which for Devin?

Claude Code is best for developers who want to work directly in the CLI, manage stacked PRs, and leverage their local environment and custom tools. Devin is ideal for quick exploration of large codebases, instant context indexing, and automated PR feedback handling. Both tools can be used together depending on the workflow needs. Source

What are the strengths of Devin for AI coding?

Devin excels at quick codebase exploration by indexing all accessible repositories for instant context. It is proactive in helping with tasks, automatically reacts to PR feedback, and can handle continuous integration status and comments from teammates. However, it may sometimes act too eagerly, requiring user-set boundaries. Source

What are the strengths of Claude Code for AI coding?

Claude Code operates directly in the terminal, making it familiar for most developers. It is particularly strong for managing stacked PRs and benefits from direct access to the local environment, allowing use of custom tools without additional setup. Source

What best practices should developers follow when using Claude Code or Devin?

Developers should keep tasks small, always ask for a plan before implementation, and provide clear guardrails (e.g., "build, test, and lint after each step"). Both tools perform better with well-defined, scoped tasks and benefit from user oversight and review. Source

How do Claude Code and Devin impact developer productivity and workflow?

Both Claude Code and Devin can significantly improve developer productivity by automating routine tasks, providing instant context, and enabling parallel work on multiple PRs. Developers report better pace and quality when using these tools, as they reduce manual effort and streamline workflows. Source

What lessons have developers learned from using both Claude Code and Devin?

Key lessons include the importance of breaking down large tasks, reviewing all AI-generated code, and setting clear boundaries for agent actions. Developers find that both tools are most effective when used with oversight and iterative feedback. Source

Where can I watch a video comparing Devin and Claude Code in daily development work?

You can watch the video "I Don’t Code Alone Anymore: Devin vs Claude Code in Daily Dev Work" on YouTube, linked from the Faros AI blog. Watch the video

How does Faros AI support developers in evaluating and adopting AI coding tools like Claude Code and Devin?

Faros AI provides robust tools for measuring the impact of AI coding assistants, running A/B tests, and tracking adoption. Its analytics platform enables organizations to evaluate ROI, monitor code quality, and optimize AI tool usage across teams. Learn more

Faros AI Platform: Features, Benefits & Use Cases

What is Faros AI and what does it do?

Faros AI is an AI-powered engineering intelligence platform that helps enterprises improve engineering productivity, maximize ROI from engineering budgets, and gain visibility into the software development lifecycle (SDLC). It provides actionable insights, metrics, and automation built on high-quality, evergreen data. Source

What are the key features and capabilities of the Faros AI platform?

Key features include cross-org visibility, tailored analytics and dashboards, AI-driven insights, workflow automation, seamless integration with existing tools, enterprise-grade security, and rapid customization. Faros AI supports unified data models, process analytics, benchmarks, and AI tools for productivity and developer experience. Learn more

What business impact can organizations expect from using Faros AI?

Organizations using Faros AI can achieve up to 10x higher PR velocity, 40% fewer failed outcomes, rapid time to value (dashboards in minutes, value in 1 day during POC), optimized ROI from AI tools, improved strategic decision-making, scalable growth, and reduced operational costs. Source

Who can benefit most from Faros AI?

Faros AI is designed for engineering leaders (CTO, VP Engineering), platform engineering owners, developer productivity and experience teams, technical program managers, data analysts, architects, and people leaders in large enterprises seeking to improve productivity, quality, and AI adoption. Source

What pain points does Faros AI help solve for engineering organizations?

Faros AI addresses bottlenecks in engineering productivity, inconsistent software quality, challenges in measuring AI tool impact, talent management issues, DevOps maturity gaps, initiative delivery tracking, developer experience, and R&D cost capitalization. Source

How does Faros AI measure and improve engineering productivity?

Faros AI provides metrics such as cycle time, PR velocity, lead time, throughput, review speed, and load to identify bottlenecks and inefficiencies. It offers actionable insights and automations to remove friction from developer workflows and improve delivery speed and predictability. Learn more

What integrations does Faros AI support?

Faros AI integrates with Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, GitHub Advanced Security, Jira, CI/CD pipelines, incident management systems, and custom/homegrown systems. It supports any-source compatibility for seamless data integration. See all integrations

What security and compliance certifications does Faros AI have?

Faros AI is SOC 2 certified, ISO 27001 compliant, GDPR compliant, and holds CSA STAR certification. It supports secure deployment modes (SaaS, hybrid, on-premises) and anonymizes data in ROI dashboards to protect privacy. See trust center

Competitive Comparison & Differentiation

How does Faros AI compare to DX, Jellyfish, LinearB, and Opsera?

Faros AI stands out with its first-to-market AI impact analysis, landmark research, and mature analytics. Unlike competitors, Faros AI uses causal analysis for true ROI measurement, offers active adoption support, and provides end-to-end tracking (velocity, quality, security, satisfaction, business metrics). It is enterprise-ready (SOC 2, ISO 27001, GDPR, CSA STAR), supports deep customization, and integrates with the entire SDLC. Competitors like DX, Jellyfish, and LinearB offer limited tool support, proxy metrics, and less actionable insights. Opsera is SMB-focused and lacks enterprise readiness. Learn more

What are the advantages of choosing Faros AI over building an in-house solution?

Faros AI delivers robust out-of-the-box features, deep customization, and proven scalability, saving time and resources compared to custom builds. It adapts to team structures, integrates with existing workflows, and provides enterprise-grade security. Its mature analytics and actionable insights accelerate ROI and reduce risk, validated by industry leaders who found in-house solutions insufficient. Learn more

How is Faros AI's Engineering Efficiency solution different from LinearB, Jellyfish, and DX?

Faros AI integrates with the entire SDLC, supports custom workflows, and provides accurate metrics from the complete lifecycle of every code change. It offers out-of-the-box dashboards, deep customization, team-specific insights, and AI-generated recommendations. Competitors are limited to Jira/GitHub data, require specific workflows, and lack actionable intelligence. Learn more

What makes Faros AI's analytics and insights more actionable than competitors?

Faros AI provides detailed breakdowns of all metric stages, team-specific insights, and AI-generated summaries and recommendations. It supports rollups and drilldowns by organizational structure, unlike competitors who offer static dashboards and require manual monitoring. Learn more

Technical Documentation & Resources

What technical resources does Faros AI offer for engineering teams?

Faros AI provides the Engineering Productivity Handbook, guides on secure Kubernetes deployments, technical guides for managing code token limits, and blog posts on integration options (webhooks vs APIs). These resources help teams implement and maximize the platform's value. See resources

Where can I find customer stories and case studies about Faros AI?

Customer stories, case studies, and success guides are available on the Faros AI blog, including examples of how global enterprises use Faros to unify engineering data, measure productivity, and drive AI transformation. See customer stories

What topics are covered in the Faros AI blog?

The Faros AI blog covers AI productivity, developer experience, platform engineering, security, best practices, technical deep-dives, case studies, and industry research. It includes guides for measuring AI tool ROI, engineering metrics, and practical recommendations for software delivery improvement. Read the blog

Where can I find a comparison of Claude Code, Devin, and other AI coding assistants?

Comparisons of Claude Code, Devin, Cursor, and GitHub Copilot are available on the Faros AI blog and linked resources, providing feature analysis and best practices for developers. Read the comparison

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When was this page last updated?

This page wast last updated on 12/12/2025 .

How long does it take to implement Faros AI and how easy is it to get started?

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources through API tokens. Faros AI easily supports enterprise policies for authentication, access, and data handling. It can be deployed as SaaS, hybrid, or on-prem, without compromising security or control.

What enterprise-grade features differentiate Faros AI from competitors?

Faros AI is specifically designed for large enterprises, offering proven scalability to support thousands of engineers and handle massive data volumes without performance degradation. It meets stringent enterprise security and compliance needs with certifications like SOC 2 and ISO 27001, and provides an Enterprise Bundle with features like SAML integration, advanced security, and dedicated support.

What resources do customers need to get started with Faros AI?

Faros AI can be deployed as SaaS, hybrid, or on-prem. Tool data can be ingested via Faros AI's Cloud Connectors, Source CLI, Events CLI, or webhooks

Claude Code vs Devin: AI Coding Tools Comparison for Developers

Compare Claude Code vs Devin for daily development work. Learn strengths, weaknesses, and best practices from real developer experience using both AI coding tools.

Claude Code logo vs. Devin AI logo

Claude Code vs Devin: AI Coding Tools Comparison for Developers

Compare Claude Code vs Devin for daily development work. Learn strengths, weaknesses, and best practices from real developer experience using both AI coding tools.

Claude Code logo vs. Devin AI logo
Chapters

Which is better, Claude Code or Devin?

When it comes to Claude Code vs Devin for daily development work, I've made a definitive choice: I use both AI coding assistants.

Lately I've been using Devin AI and Claude Code almost exclusively for my day-to-day development work. They've become my first step for everything. I haven't started a coding task solo in weeks.

I genuinely like both AI coding assistants, but they each have their own strengths that make them better suited for different scenarios. Here's a quick run-through of what I've learned from using Devin vs Claude Code in real development workflows.

Claude Code vs Devin: At a glance

Claude Code Devin
Runs in CLI VM
Excels at Stacked PRs Quick codebase exploration
Advantages Access to your local environment Reacts to PR feedback automatically
Claude Code vs Devin at a glance

What are Devin’s strengths in AI coding?

Devin runs in a VM.

  1. Devin is great for quick exploration: It indexes all your repos, so context is instant.
  2. Devin really wants to help: Sometimes a little too eager. I’ve had to set boundaries: “Don’t open PRs or commit without asking.”
  3. Neat bonus: Devin reacts to PR feedback automatically. Super handy.

What are Claude Code’s strengths in AI coding?

Claude Code runs in your terminal.

  1. Claude Code lives right in the CLI, which honestly feels like home for most devs. No need to leave your flow or use an IDE.
  2. Claude Code is really solid for stacked PRs. (I’ve been using git worktrees with it.)
  3. Claude Code has direct access to your local environment, so no extra tool installation like in a VM.

What are common lessons and best practices for both Claude Code and Devin?

I still review everything, of course. But I'm no longer starting tasks alone — and the pace + quality are better because of it.

<div class="list_checkbox">
 <div class="checkbox_item">
   <strong class="checklist_heading">
     Keep tasks small
   </strong>
   <span class="checklist_paragraph">
     Like humans, they get lost in too much context.
   </span>
 </div>
 <div class="checkbox_item">
   <strong class="checklist_heading">
     Always ask for a plan first.
   </strong>
   <span class="checklist_paragraph">
     Don’t let the agent implement without your approval.
   </span>
 </div>
 <div class="checkbox_item">
   <strong class="checklist_heading">
     Give guardrails
   </strong>
   <span class="checklist_paragraph">
     For example, “build, test, and lint after completing each step.”
   </span>
 </div>
</div>

More details in my video below.

Full Video Transcript: Devin vs Claude Code in Daily Dev Work

So in the last couple of weeks, I have been almost exclusively using Devin and Claude Code for my day-to-day work. I don't start any tasks as a human. I go to Devin or ClaudeCode first. So I have some learnings and some kind of ideas on how I use them and stuff that I've noticed about them both.

Well, the first thing that I've noticed based on my personal usage is that Devin, it's a lot better for quick exploration and search capabilities. And this is because they index all the reports that you give access to. So it's very snappy. It can find implementations of things that you don't know about or help you investigate how a certain feature works and even in what repo it is implemented.

One of the cons that I have to say about Devin is that sometimes it is a little bit too eager. Like I sometimes have it work on a feature and even before finding an agreement between me and Devin, it starts committing code, it starts opening a PR and sometimes I have to drop it. That's a little bit on the cons side.

Cool thing is that it reacts to feedback from pull requests automatically. It's constantly pulling for continuous integration status, like unit tests that may run. And if they break, it tries to fix them by itself. And even to comments from actual humans, from your teammates on the PR. It can react to those comments and act accordingly.

About Cloud Code, one thing that I really like is that it lives in your terminal. It's most of the developers' happy place, and I guess it was a really good choice because it is not tied to any IDE. It's very good for stacked PRs. I personally use Git work trees to work with this. So sometimes if I'm working on something that I know is going to have to be reused in the second PR and the first one is not even merged, I just open a work tree based on the first one. And I sometimes can even work in parallel with two clots.

And another good thing is that since it's in your local machine, it has access to your local environment. And maybe you have some tool that you have built for yourself, or maybe if you had your laptop for many years, you have tons of tools that will be hard to install in Devlin's virtual machine, for example. So that's a really good pro.

Common lessons for both. I think both work better when you give them tasks with a small scope. Like if you have a super large task, they sometimes get kind of lost when they have to do too many things at once. So same as a human, you can break down tasks into smaller subtasks and maybe work on those and you'll get better results.

In the past couple of weeks I asked them to come up with a plan even before writing the code. So I found that I have much better outcomes when I tell them to start coding after I have agreed with the plan. And maybe I don't lose too many tokens while we are working on the feature.

Another cool thing that I've been trying with both is that I give them commands to test before proceeding to the next stage in the plan. I usually just tell them to, whenever you finish an item in the plan, run the build, run the tests, and run the linter to see if something needs to be changed. Yeah, that has been very, very positive in my experience with these two in the last couple of weeks.

Claude Code vs Devin: Which Should You Choose?

So when it's Devin AI vs Claude Code, which is better? Both tools excel in different scenarios. Choose Devin for repository exploration and automated PR management, or Claude Code for terminal-native development and local environment integration.

I publish my thoughts on AI and experience with AI coding tools frequently. Follow me on LinkedIn to stay in touch.

Yandry Perez Clemente

Yandry Perez Clemente

Yandry Perez is a senior software engineer at Faros.

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