TLDR

From Criminal Justice to AI Developer David transitioned from a criminal justice background into fullstack software engineering with AI specialization. He failed the Codesmith technical interview once but persisted and later graduated from the immersive program in 2024.

His team built OS Analytics, a Google Analytics alternative, during the project phase. David led the AI integration using AWS Bedrock and solved cost/accuracy issues by designing a custom data aggregation algorithm. After graduating, he built two more AI tools: Teskro.com and a Python-based CLI for SQL analysis.

His AI skills gave him a competitive edge in a job market rapidly demanding AI-literate developers.

Background

David didn’t imagine he'd be debugging algorithms while studying criminal justice at John Jay College in Manhattan. After graduating, he worked mainly on police topics, lots of papers, with some law here and there. Then his friend showed him some programming projects.

"When I saw what technology can do and how the things you build impact people, that really intrigued me," David says. Although his mind was made up on transitioning to tech, the shift wasn't instant. While he had ideas for products, when he really started learning to code, he became completely lost and the ideas disappeared.

"I wanted to build tools, but I didn't know anything about frontend or backend. I didn't even know what the difference was."

He heard about the Software Engineering + AI/ML Immersive, a program designed to train engineers in fullstack development and practical AI integration, and decided to throw himself into it.

Unfortunately, he failed the technical interview the first time around—although this is very common amongst Codesmith’s residents. Many might have given up, but David saw this as a positive challenge.

"It made me even more excited about studying and passing. So I studied a lot harder and I went again and I passed."

He ended up doing the full-time Software Engineering Immersive, graduating in August 2024.

Program Experience

"The most challenging aspect of the program was how fast everything went, the speed at which you're learning different topics and having to apply them."

Despite his earlier struggles with frontend and backend, the connection between the two was the easiest concept for him to wrap his mind around on the program.

"It was very logical in the way. Like you send a request, you get a response. It became very easy to connect the dots in my head."

For someone with a criminal justice background, the cause-and-effect relationship was a familiar one.

David's team’s first idea was a React debugging tool for tracking duplicate hooks. But after some careful thought, they instead landed on OS Analytics.

OS Analytics

They discussed MVP scope and how to iterate if they finished early. David describes OS Analytics as similar to Google Analytics but free and built from scratch.

Users integrate an API into their codebase, click data gets tracked, everything flows into a database, and users log into a dashboard to see what's happening on their site.

With each member of the team assuming responsibility for separate parts of the product, it was David who took on the AI integration - the part that would analyze all the click data and generate actual insights, not just aesthetic charts.

Challenges in AI Integration

The first problem he encountered was which AI model to use. David opted for AWS Bedrock due to cost considerations.

"You can run into a scenario where the user has a lot of data and you can't just send a thousand rows of data to an AI, or else you're going to pass the token limit and it could become expensive."

This led David to develop an aggregation algorithm that compresses large datasets before sending them to Bedrock for processing.

Instead of sending 1,000 individual data points, his algorithm would create digestible summaries that maintain the essential patterns.

They ran out of time for formal test code, so David created mock data in the database, did manual calculations, and fed it to his AI system to verify accuracy.

"I manually did the calculations myself to make sure I had what was correct on paper. And then I processed it to the AI to make sure it also had the same result as me."

Conclusion

After David graduated, he entered a competitive job market but one where demand for AI-skilled software engineers is booming.

David created two more AI-integrated tools since graduating: Teskro.com generates practice exams for various certifications, while another CLI tool combines multiple AI agents to analyze SQL databases.

"I'm seeing a lot of job posts looking for someone who can incorporate AI into their organization," David notes. AI roles are expanding quickly across multiple industries.