Nate Swanner

I am currently sourcing expert commentary for an upcoming Dice Career Advice feature titled: "How to responsibly use AI when coding." Efficiency is great, but responsibility is what keeps systems secure and careers intact. We are looking for experienced, hands-on software engineers and developers to help us define the ethical and professional standards for AI in the modern dev workflow. Submission Requirements: -Human-Only Insights: We are looking for genuine professional perspectives. AI-generated responses will be discarded. -Verification: A LinkedIn profile link is required so we can verify your current role and expertise. -Direct Contact: Please include a professional email address for follow-up. -Verification Process: We will contact you directly via LinkedIn or email to confirm you submitted these answers before publication. Have a lot to say about the ethics of AI? We want to hear from you! The Questions: -How should developers define "original work" today? Is there a professional obligation to disclose when AI has generated significant portions of code? -What does a "responsible" security audit look like for AI-generated code compared to human-written code? -How can engineers mitigate "automation bias" and maintain a healthy skepticism of AI-suggested logic? -What are the best practices for ensuring AI-generated snippets don't introduce licensing or copyright risks into proprietary projects? -Should "responsible use" include being mindful of the high compute/environmental costs of using LLMs for simple tasks? -When AI-assisted code fails in production, how should a team’s accountability framework handle the blame?

I am currently working on an upcoming article for Dice Career Advice titled: "Is ‘vibe coding’ the future, or a trend? We ask the experts." We are looking for insights specifically from experienced, hands-on software engineers and developers who are currently navigating the shift toward AI-assisted development. Submission Requirements: -Human-Only Insights: We are looking for authentic, lived experience. We will be filtering out any AI-generated responses. -Verification: To ensure the integrity of the article, please include your LinkedIn profile link so we can verify your professional background. -Direct Contact: Please provide a professional email address where we can reach you directly. -Verification Process: We take "vibe checking" seriously—we will message you directly via LinkedIn or email to verify that you were the one who submitted the answers. If you have a strong perspective on whether "vibe coding" is a vital new skill or a technical debt trap, we want to hear from you ASAP! The Questions: -Where do you draw the line between where "vibe" ends and "engineering" begins? -Is "vibe coding"—the ability to describe and iterate on high-level logic through natural language—actually a new technical discipline, or is it simply a layer of abstraction that will eventually hit a ceiling where traditional computer science fundamentals remain the only way forward? -If vibe coding becomes a standard part of the workflow, what does "mastery" look like for a modern engineer? Does it shift from understanding memory management and syntax to mastering prompt engineering, architectural oversight, and "vibe" debugging? -Can natural-language-driven development ever produce the rigorous, maintainable, and secure codebases required for enterprise-level applications, or is it strictly for rapid prototyping? -If AI handles the implementation "vibes," how do we train the next generation of engineers? Are we at risk of creating a skills gap where future senior engineers lack deep-level understanding? -Looking at the history of software engineering (from assembly to high-level languages), is "vibe coding" just the next logical step in abstraction, or is it qualitatively different from "coding"? -From a hiring and management perspective, should companies prioritize "vibe coders" who can ship at 10x speed, or will the technical debt created by AI make traditional engineering more cost-effective in the long run?

I’m currently interviewing experts for a new article for Dice Career Advice titled: "When to use AI coding assistants - and when to avoid them." As AI tools become ubiquitous, the real skill isn't just knowing how to use them—it's knowing when to shut them off. We want to hear from experienced, hands-on software engineers and developers who have navigated these boundaries in production environments. Submission Requirements: -Human-Only Insights: We value your personal expertise. AI-generated responses will be filtered out. -Verification: Please include your LinkedIn profile link so we can verify your professional experience. -Direct Contact: Provide a professional email address where we can reach you. -Verification Process: To ensure authenticity, we will message you directly via LinkedIn or email to verify your submission. We are specifically looking for contributors who have "war stories" or clear frameworks for auditing AI-generated code. If you have a strong perspective on AI coding assistants, we want to hear from you! The Questions: -At what point does a project’s complexity or security requirements make using an AI coding assistant a liability rather than an asset? -Is there a significant difference in the reliability of AI assistants when working on modern "green field" projects versus refactoring or maintaining legacy codebases? -Should the "rules of engagement" for AI assistants differ based on an engineer's seniority? -What are the specific "red flags" a developer should look for in AI-generated code that suggest the assistant is hallucinating logic? -Beyond the code itself, what are the often-overlooked legal or IP risks that should force a developer to keep the AI assistant turned off? -When does the time spent auditing AI-generated code begin to outweigh the speed of writing it manually? How do you measure that ROI?