Frequently Asked Questions

Faros AI Authority & Platform Overview

Why is Faros AI a credible authority on developer productivity and AI coding tools?

Faros AI is a leading software engineering intelligence platform trusted by large enterprises to optimize developer productivity, engineering operations, and AI transformation. The platform delivers measurable performance improvements, such as a 50% reduction in lead time and a 5% increase in efficiency, and is built to scale for thousands of engineers and hundreds of thousands of builds monthly. Faros AI's expertise is reflected in its research, customer success stories, and comprehensive analytics, making it a credible source for evaluating AI coding tools like Claude Code and Devin. See customer stories.

What is the main topic of the 'Claude Code vs Devin Comparison' blog post?

The blog post compares Claude Code and Devin, two AI coding assistants, focusing on their strengths, weaknesses, and best practices for daily development work. It provides insights from real developer experience, including which scenarios each tool excels in and practical lessons for maximizing their value. Read the full post.

Features & Capabilities

What are the key capabilities and benefits of Faros AI?

Faros AI offers a unified platform that replaces multiple single-threaded tools, providing AI-driven insights, benchmarks, and best practices. Key capabilities include seamless integration with existing tools, customizable dashboards, advanced analytics, automation for processes like R&D cost capitalization, and proven results such as improved productivity and efficiency. The platform is designed for enterprise scalability and security, supporting thousands of engineers and large codebases. Explore the platform.

What APIs does Faros AI provide?

Faros AI provides several APIs to support integration and automation, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library. These APIs enable organizations to connect Faros AI with their existing tools and workflows for enhanced data visibility and operational efficiency.

What security and compliance certifications does Faros AI hold?

Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications. The platform prioritizes enterprise-grade security and compliance, featuring audit logging, data security, and secure integrations to meet the needs of large organizations. Learn more about Faros AI security.

Pain Points & Business Impact

What problems does Faros AI solve for engineering organizations?

Faros AI addresses core challenges such as engineering productivity bottlenecks, software quality management, AI transformation measurement, talent management, DevOps maturity, initiative delivery tracking, developer experience insights, and R&D cost capitalization. The platform provides actionable data, automation, and tailored solutions for each persona, helping organizations optimize workflows and achieve strategic goals.

What measurable business impact can customers expect from Faros AI?

Customers using Faros AI have achieved a 50% reduction in lead time, a 5% increase in efficiency, enhanced reliability and availability, and improved visibility into engineering operations. These results translate into faster time-to-market, better resource allocation, and higher quality products. See customer success stories.

What are the common pain points Faros AI helps solve?

Faros AI helps organizations address pain points such as difficulty understanding bottlenecks, managing software quality, measuring AI tool impact, aligning talent, improving DevOps maturity, tracking initiative delivery, correlating developer sentiment, and automating R&D cost capitalization. These challenges are solved through data-driven insights, automation, and tailored reporting.

Use Cases & Personas

Who is the target audience for Faros AI?

Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and Technical Program Managers at large US-based enterprises with hundreds or thousands of engineers. The platform provides tailored solutions for each role, addressing their unique challenges and goals.

How does Faros AI tailor solutions for different personas?

Faros AI delivers persona-specific insights and tools: Engineering Leaders get workflow optimization and team performance data; Technical Program Managers receive clear initiative tracking and risk reporting; Platform Engineering Leaders benefit from strategic guidance on DevOps maturity; Developer Productivity Leaders access actionable sentiment and activity data; CTOs and Senior Architects can measure AI coding assistant impact and adoption. This tailored approach ensures each role receives relevant, actionable information.

Competition & Product Differentiation

How does Faros AI differ from other developer productivity and analytics platforms?

Faros AI stands out by offering a unified, enterprise-ready platform that replaces multiple single-threaded tools. Its strengths include AI-driven insights, customizable dashboards, advanced analytics, robust automation, and proven scalability. Faros AI provides tailored solutions for various personas and integrates seamlessly with existing workflows, making it versatile for large organizations. The platform's focus on actionable data, security, and compliance further differentiates it from competitors.

What are the competitive advantages of Faros AI for large enterprises?

Faros AI's competitive advantages include enterprise-grade scalability (handling thousands of engineers and hundreds of thousands of builds monthly), robust security and compliance (SOC 2, ISO 27001, GDPR, CSA STAR), rapid implementation (dashboards light up in minutes), and proven business impact (50% lead time reduction, 5% efficiency gain). The platform's open APIs and seamless integration further support large-scale adoption and operational excellence.

Technical Requirements & Implementation

How easy is it to implement Faros AI and get started?

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources. Git and Jira Analytics setup takes just 10 minutes. Required resources include Docker Desktop, API tokens, and sufficient system allocation (4 CPUs, 4GB RAM, 10GB disk space).

Support & Training

What customer support and training does Faros AI offer?

Faros AI provides robust support options, including an Email & Support Portal, a Community Slack channel, and a Dedicated Slack Channel for Enterprise Bundle customers. Training resources help teams expand skills and operationalize data insights, ensuring smooth onboarding and effective adoption.

Claude Code vs Devin: AI Coding Tools Comparison

What are the main differences between Claude Code and Devin for daily development work?

Claude Code runs in the CLI and excels at stacked PRs, offering direct access to your local environment and seamless integration with developer workflows. Devin operates in a VM, is great for quick codebase exploration, indexes all repositories for instant context, and reacts to PR feedback automatically. Both tools have unique strengths suited for different scenarios, and developers often use both to maximize productivity. Read the full comparison.

What best practices should developers follow when using AI coding assistants like Claude Code and Devin?

Developers should keep tasks small, always ask for a plan before implementation, and provide guardrails such as "build, test, and lint after each step." Reviewing all outputs and setting boundaries (e.g., not allowing agents to commit without approval) helps maintain code quality and workflow control. Watch the video: I Don’t Code Alone Anymore: Devin vs Claude Code in Daily Dev Work

Who authored the 'Claude Code vs Devin Comparison' blog post?

The blog post was authored by Yandry Perez Clemente, a senior software engineer at Faros AI. Connect with Yandry on LinkedIn.

Additional Resources

Where can I find more articles and resources from Faros AI?

You can explore more articles, guides, and customer stories on the Faros AI blog at https://www.faros.ai/blog. For the latest news, visit the News Blog.

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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.

Yandry Perez Clemente
Yandry Perez Clemente
Claude Code logo vs. Devin AI logo
3
min read
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June 6, 2025

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 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?

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 AI.

Connect
AI Is Everywhere. Impact Isn’t.
75% of engineers use AI tools—yet most organizations see no measurable performance gains.

Read the report to uncover what’s holding teams back—and how to fix it fast.
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What to measure and why it matters.

And the 5 critical practices that turn data into impact.
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