I Created My First AI-assisted Pull Request and I Feel Like a Fraud | Nelson Figueroa

HomePostsCategoriesSearchLinksAboutSupportI Created My First AI-assisted Pull Request and I Feel Like a Fraud2026-03-23 · Updated 2026-03-23 · 3 min · 461 wordsAI Opinion I created
my first AI pull request for
Chroma, which is the default syntax highlighter used in
Hugo.I used Claude Code for that PR. I didn’t learn anything. I felt like I was flinging slop over the wall to an open-source maintainer. I felt like a fraud and my impostor syndrome got worse.But here’s the thing, I still contributed something of value. It’s something I have been wanting in hugo for a long time to syntax highlight
ERB snippets in my posts. It was approved and merged by the maintainer (thanks for dealing with my slop,
Alec).It just feels odd. I know there are many people that are excited this new era of writing code. But to me this has sucked out all of the fun. I have never felt like a bigger fraud in this field. I’ve always thought that I’m privileged that I get to have fun at all in my line of work. As Ori Bernstein says:
“using LLMs to write code is as fun as hiring a taskrabbit to solve my jigsaw puzzles”.But then again, I know that realistically I would not have the mental capacity or skill to create a pull request like that without AI tooling. My brain is already fried from work on most days. I don’t think I would have been able to learn the codebase and get enough context to make that PR all by myself. It’s a lot. I just wanted some ERB syntax highlighting for my little blog.Even at work I’ve used Claude Code and other AI tooling to deliver fixes and improvements that have real customer impact. But no matter how big the impact, I feel empty. I agree with Xe Iaso in the first sentence of their post:
“Whenever I have Claude do something for me, I feel nothing about the results”.Now that using AI is a normal expectation at work and how I’m evaluated in performance reviews, I suspect that this fraud feeling will only grow. The industry as a whole is incentivizing delivering code/features/fixes at a quick pace even if it’s all just slop.I keep thinking a lot about how I perhaps tied my identity too much to my career. I’m not the greatest engineer, but I’ve always worked hard to deliver good work and learn as much as possible. I care a lot about understanding underlying systems as much as possible. I care about the craftsmanship of my code (to the best of my abilities). Unlike me, AI tools don’t care about any of these values.At the end of the day, the shareholders care about delivering features, gaining customers, and making money. They don’t care how software is built.I don’t know what to make of this.Next »I Hate Workday« See a Random Post »
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Nelson Figueroa’s personal reflection, “I Created My First AI-assisted Pull Request and I Feel Like a Fraud,” details a complex and somewhat unsettling experience with utilizing artificial intelligence – specifically Claude Code – to contribute to an open-source project, Chroma for Hugo. The core of Figueroa’s experience revolves around a significant emotional response: a pervasive feeling of fraudulence coupled with intense impostor syndrome. This feeling stems from submitting a pull request for ERB syntax highlighting to the Chroma project, feeling as though he was merely “flinging slop” at a maintainer, Alec, and not genuinely contributing value. He acknowledges a broader sentiment shared by many regarding the increasing role of AI in software development, drawing a parallel to the perceived emptiness of outsourcing problem-solving, likening it to using a taskrabbit for intricate puzzles.

Despite the initial feelings of inadequacy, Figueroa emphasizes that his contribution was, in fact, approved and successfully merged into the project – a significant validation that he admits he would not have been able to accomplish independently given his workload and cognitive capacity. This realization is tempered by a continued sense of detachment, mirroring the experience he describes when utilizing AI tools in his professional role, stating that he “feels nothing about the results” when prompted by AI. He notes that this feeling is likely to intensify given the prevailing industry trend of prioritizing rapid delivery of features, often irrespective of underlying quality or design considerations.

Figueroa’s introspection extends to his own professional identity as a software engineer, questioning whether he has become overly reliant on external validation for his work. He articulates a core value system rooted in understanding systems, appreciating meticulous code craftsmanship, and a commitment to learning—values that he perceives are absent in AI tools. Contrasting his approach with the shareholder-driven focus on short-term gains and feature delivery, he recognizes that the motivations of companies often overshadow the principles of thoughtful software development.

The experience has triggered a deeper consideration of Figueroa’s relationship between his career and his sense of self-worth. Recognizing the potential to have over-invested in his professional identity, he reflects on his dedication to acquiring knowledge and developing skills, and contrasts this with the detached, output-oriented nature of AI-assisted development. Ultimately, Figueroa’s narrative presents a poignant reflection on the evolving role of developers in an era of increasingly sophisticated AI tools, highlighting the potential for both opportunity and a profound sense of displacement and uncertainty.