Detailed Analysis
A marketing intern with no coding or cryptography background used Claude Code to build VitalVault, an application that encrypts health and lab data using Fully Homomorphic Encryption (FHE) and computes a wellness score and estimated biological age without ever decrypting the underlying values server-side. The project emerged organically from her day job at Niobium Microsystems, a company working on FHE hardware acceleration, where she began learning about the technology and decided to test whether an AI coding assistant could help her actually implement it rather than just understand it conceptually. Working from Niobium's public GitHub tools as a starting point, she spent roughly three days iterating with Claude, hitting technical roadblocks she lacked the background to diagnose independently, and pushing her aging laptop's hardware to its limits before landing on a working demo.
The significance of this case lies less in the app itself and more in what it represents: a nontechnical user successfully directing an AI system through the implementation of one of the more mathematically demanding areas of applied cryptography. FHE allows computations to be performed directly on encrypted data, producing encrypted results that only the data owner can decrypt, which means a server can process sensitive information like biometric data without ever seeing it in plaintext. Historically, FHE has been the province of specialized cryptography researchers due to its computational complexity and the subtlety required to avoid implementation errors that could silently undermine security guarantees. That someone with an economics degree and zero prior technical training could produce a functioning prototype in a few days is a striking illustration of how far AI-assisted coding has lowered the barrier to entry for domains that previously required years of specialized study.
At the same time, the project underscores a persistent tension in the "vibe coding" trend: the gap between getting code to run and knowing whether it is actually correct or secure. The intern explicitly acknowledges this, noting she "didn't have any knowledge to understand what was happening half of the time" and is now soliciting scrutiny from people who understand FHE to check for cryptographic mistakes. This is a meaningful admission, because cryptographic implementations are notoriously prone to subtle flaws that function perfectly in testing while being fundamentally insecure — issues like improper key management, side-channel leakage, or misuse of encryption parameters are not the kind of bugs that surface through casual use. The fact that the app runs and produces plausible output is not the same as it being cryptographically sound, and the burden of that verification necessarily falls on human experts rather than the AI or the user who directed it.
This episode fits into a broader pattern seen across Claude and other coding-capable AI models: nontechnical users increasingly building functional software in domains like FHE, blockchain, and machine learning that were once gatekept by specialized expertise. Anthropic has actively marketed Claude Code as a tool that extends software creation to non-engineers, and stories like this one serve as both proof of concept and cautionary tale simultaneously. As AI models become more capable of scaffolding complex technical systems, the industry faces a growing need for parallel investment in automated verification, security auditing tools, and clearer signals to end users about the difference between "code that works" and "code that is safe." The willingness of the builder here to publicly ask for brutal feedback rather than assume correctness reflects a healthy instinct that may become increasingly important as AI-assisted development pushes further into high-stakes technical territory like cryptography, security, and healthcare-adjacent applications.
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