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How Software Engineers and Students Use AI to Move Faster than Ever (without breaking things)
The “AI revolution” is really augmented intelligence: humans + tools. super{set} backs teams that are AI-native—putting AI at the core, not as an add-on. For students, the risk is either over-relying on AI (and skipping fundamentals) or under-using it (and falling behind). Universities should teach both rigorous reasoning and modern AI workflows. For developers, agentic tools (e.g., Cursor, Copilot) turn one engineer into a mini dev team—automating boilerplate, tests, docs, and legacy audits—so humans can focus on architecture, edge cases, and creative problem-solving. AI won’t erase jobs; it shifts them, rewarding those who adapt. The near future belongs to builders who pair deep software craft with AI fluency.

Running cloud platforms efficiently while keeping them secure can be challenging. In this blog post, learn how two of super{set}’s portfolio companies, MarkovML and Kapstan, are leveraging tools like KEDA for event-driven scale and Boundary for access management to remove friction for developers. Get insights into real-world use cases about optimizing resource usage and security without compromising productivity.

Ever find yourself scratching your head about product management decisions? Join India Lossman, co-founder of boombox.io, as she unpacks the product mindset for engineers. Unravel the art of synergy between PMs and engineers and delve into strategies to enhance collaboration and craft products that users will adore.

Tom Chavez, writing in TechCrunch, calls for new approaches to the problems of Ethical AI: "We have to build a more responsible future where companies are trusted stewards of people’s data and where AI-driven innovation is synonymous with good. In the past, legal teams carried the water on issues like privacy, but the brightest among them recognize they can’t solve problems of ethical data use in the age of AI by themselves."
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