I recently took Devin AI for a spin on a real development task, and the experience felt like something between magic and mentorship. I had a straightforward job: implement an API endpoint to generate user access tokens for a third-party service. Normally I’d crank this out in ~15 minutes of coding. This time, I decided to hand it off to my new “AI teammate” and see what happened. Here’s how it went, step by step.
The Task & Initial Plan 🚀
My instruction to Devin was simple:
Implement the backend API based on this documentation [link to 3P website]Add it to [microservice API name]Utilize the existing CDK secrets logic to store the 3P API Key.
Devin’s response was almost immediate:
Understanding the goal: It parsed my request and the service docs, and quickly outlined a plan to create the token-generation endpoint.
Collecting details: Devin identified required endpoints and data (thanks to the link I gave) and noted it would need to handle authentication, token storage, etc.
Generating a plan: Before writing code, Devin presented a high-level game plan. This included creating a new route in our backend, calling the third-party API for the token, and returning the result to our app. I was impressed – the plan was thorough and made sense given the task.
In essence, Devin did a great job figuring out what needed to be done without me hand-holding the requirements. It felt like I was working with an autonomous engineer who eagerly drafts a design spec after a short request.
From Prompt to Pull Request 💻
With the plan in place, I gave Devin the green light. It jumped into coding mode. Within minutes, Devin had opened a GitHub pull request on our repository with the new API implementation. I could follow its progress in real-time through Devin’s interface. (The UI actually provides a step-by-step log of what the AI is doing – very cool!).
Watching Devin work was surreal. The UI/UX of the tool is amazing – it felt seamless and intuitive to use. As one early user noted, “Devin feels UI/UX first, not GenAI first,” emphasizing that the surrounding experience is the star. I have to agree. In my case, giving instructions felt as easy as chatting with a colleague, and I could see Devin’s thought process and actions clearly. The combination of a chat interface, an embedded code editor, and live updates made it feel like pair-programming with a supercharged junior dev.
Soon, the PR was ready for review. The code Devin produced was surprisingly solid for a first pass. It had set up the new API endpoint, made calls to the third-party service, and hooked everything into our backend. All of this happened while I was hands-off.
Hiccups and Iterations 🛠️
Of course, it wasn’t perfect out of the box. Upon reviewing the pull request, I spotted a few issues that needed addressing before this code could go to production. Notably:
Missing API key logic: Devin forgot to include the authentication API key when calling the third-party service. A human engineer knows that’s a must for the request to succeed, but the AI overlooked this detail on the first try.
Code style differences: Some of the naming conventions and formatting didn’t match our project’s style guidelines. (Minor issue, but something we’d fix in any code review – even with human contributors.)
Best practice tweaks: I noticed that the API logic for calling the 3P service should have been abstracted into our "clients" library. While Devin’s solution was functional, integrating this logic into the shared library would improve maintainability and align with our standards.
The great thing was that, addressing these issues felt very natural. I went into the PR on GitHub and dropped comments exactly like I would for a human colleague. For example, I pointed out where the API key should be injected, suggested where to move code around, and reminded to clean up some unneeded additions.
Devin took this feedback in stride. It truly felt like collaborating with a keen junior developer: I’d leave a note, and Devin would go off to fix it. Devin was eager to improve and quickly pushed new commits to the PR, incorporating my suggestions.
We went through about 2-3 iterations like this. Each cycle, I’d review the updates, find fewer things to tweak, and comment on the remaining issues. Devin would promptly address them. After this iterative back-and-forth, the API code was production-ready. All tests passed, the style was consistent, and the integration worked flawlessly with the third-party service.
Time Trade-Off: 15 Minutes vs 1 Hour ⏱️
You might be wondering: Was using Devin worth it, time-wise? By the numbers, doing it myself would have been faster – roughly 15 minutes of coding versus about 1 hour to get it done via Devin (including the initial setup, waiting for the AI to do its work, reviewing the PR, and guiding the fixes). That’s a 4x increase in wall-clock time for the task, which sounds like a loss in efficiency.
In fact, others have noted this current limitation of AI coding agents. The waiting and iterative feedback loop can indeed make the process longer than just writing the code yourself, especially for a simple task.
However, here’s the catch: while Devin was working, I wasn’t stuck waiting idly. During that 1 hour I was free to focus on other work. I answered a couple of messages, reviewed a different PR from a teammate, and even started brainstorming a design for an upcoming feature – all while Devin handled the heavy lifting for this task in the background.
In essence, that hour wasn’t me twiddling my thumbs; it was more like delegating to a capable assistant. Yes, the calendar time was longer, but my personal time investment was much less than an hour of active coding. I probably spent only a few minutes giving instructions and about 5 minutes total reviewing and commenting. The rest of the time, Devin was on the job autonomously.
It’s a trade-off: faster solo vs. parallelized teamwork. If I had 10 such small tasks in a sprint, I could theoretically assign them all to Devin and attend to bigger challenges, checking in occasionally for reviews. That ability to multitask is where a tool like this shows its value.
The UI/UX: A Smooth Ride 🎨
I have to come back to Devin’s user experience, because it really enhanced the whole process. The interface made it super easy to interact with the AI:
I gave my instructions in a chat-like format (in our case, through Devin’s Slack integration and then via GitHub PR comments). No complex setup or configuration; it was like talking to a teammate.
Devin kept me updated with a live log and even a “plan file” of notes. I could literally see what it was thinking – the steps it was taking, the commands it ran, files it created or modified, etc. This transparency is huge for trust when an AI is writing your code.
The pull request it opened was clear and well-structured. It included a description of what the change was and even referenced the task (just as a diligent dev would do).
When I left feedback, the UI (and GitHub integration) notified Devin immediately. It felt like the system was built around a smooth feedback loop, which is crucial. I commented and within a minute Devin’s next update had the fix implemented.
The polish and thought put into Devin’s UX did not go unnoticed. It didn’t feel like using a clunky experimental tool; it felt like working in an environment built for developers’ comfort. This level of refinement in developer tools is refreshing – it let me focus on the results rather than wrestling with the tool itself.
Final Thoughts: A Promising Co-Pilot, Not a Replacement 🚧
After this trial run, here’s my reflection: Devin AI is not replacing engineers anytime soon, but it’s certainly a promising co-pilot. The experience was akin to working with a supercharged junior engineer who can execute tasks and learn from feedback. It had its blind spots and needed guidance, but ultimately it delivered real value.
The limitations I encountered (missing a key detail, needing adjustments, slower turnaround) underscore that human expertise is still crucial. In real-world development, context and subtle requirements matter – things an experienced human developer intuitively catches, but an AI might miss without a proper prompt. I had to be the quality control, just like I would with a less-experienced team member. Devin isn’t about to take over my job, and I wouldn’t trust it to run completely unsupervised on anything critical just yet.
That said, the benefits were significant. By offloading a chunk of work to the AI, I freed up mental space and time. I found myself thinking more about what needed to be done, and less about the minute details of how to code it in the moment. It’s a different way of working – more high-level orchestration, less in-the-trenches coding for certain tasks. As the creators of Devin intended, it’s meant to be a collaborative helper rather than a threat.
For a first-gen AI developer agent, Devin exceeded my expectations in UX and autonomy. It felt like I had an eager intern who works blindingly fast and never gets tired, but occasionally needs me to double-check the work. I can live with that! The technology will only get better from here. With more polish and learning from each interaction, I imagine tools like Devin will handle bigger chunks of the development workload, and do so more reliably.
Bottom line: Devin AI isn’t a replacement for an engineer – it’s a new kind of teammate. Using it won’t instantly halve your development time (yet), but it will change how you can allocate your time. I was able to focus on other priorities while it cranked out code. That kind of parallel productivity is game-changing if used right.
I’m excited to continue using Devin as a co-pilot for future projects. It’s like having a junior dev in the background who speeds through the boring stuff and lets me concentrate on the fun and hard parts of engineering. Not perfect, but very promising. The future of development might not be “AI vs Human”, but AI + Human, working in tandem – and my experience with Devin AI this week certainly makes me feel that way. 🚀👏
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