Gynger

Right now I'm working on our website. Gynger has grown and accomplished so much in the last two years and we want that success and evolution to be reflected in our public-facing material.

I don't know much about cars but I do know about Pixar. I would say our company culture reminds me of Doc Hudson, the older champion car played by Paul Newman in Cars, the 2006 Pixar movie. Like Doc Hudson, the people of Gynger are passionate, cool, wise and know how to have fun!

Collaboration within a company (heck, within a team) is never linear. It involves various strategies and tools and we do it at Gynger under an "open feedback" culture. For us, collaboration includes: direct communication, solid software, and - of course - shared vision and goals.

Our strength is doing these things efficiently and with transparency.

I stay on top of my engineering skills by consistently building, whether that's solving real-world problems at work, working on side projects or taking project-based courses. Hands-on experience is far more valuable to me than passive learning. When I want to explore a new concept or technology, I turn to a curated list of trusted engineers in specialized areas.

With so many opinions out there, it is important to cut through the noise and focus on reliable sources. I keep up with new trends but prioritize depth over hype. Rather than chasing every new shiny tool, I focus on technologies that are relevant to my work in web development and fintech.

I also pay attention to patterns in tech evolution to anticipate what will have a lasting impact. When I am passively learning, I enjoy reading books on topics of interest. I have collected a whole shelf of programming books over the years that I have barely scratched the surface of.

It is hard to put an exact number on it, but I would estimate that I spend at least three to four hours a week dedicated to expanding my knowledge and skills.

I follow a select number of news articles and blogs religiously, making sure to read everything they publish. I don't subscribe to much content, but when I do, I prioritize it. Beyond that, my learning is fairly evenly split between YouTube videos and official documentation.

Work projects often introduce new tools or third-party services, so I always start with the formal docs to build a solid foundation. Reviewing pull requests is another great way to sharpen skills. Seeing how coworkers tackle complex problems and asking clarifying questions helps expand my own understanding.

The learning culture on my team is strong. Everyone clearly wants to share their insights and continuously improve. Pull requests are the best places to spark discussions and help everyone level up.

Everyone is encouraged to ask questions, challenge assumptions and explore better ways to solve problems. It's a culture that values learning in the flow of work rather than treating it as a separate activity.

Working at Gynger as a Software Engineer has been the most fulfilling role of my career. The trust and flexibility here are unmatched. I get to build with insanely talented devs, and it's a place where good ideas are heard and you're empowered to make real impact.

At Gynger, we are building the only accounts receivable platform with embedded financing specifically designed for the technology sector. We solve a core B2B problem: buyers want flexible payment terms, sellers need cash upfront. Our platform lets tech vendors offer custom terms to customers while getting paid immediately.

We offer three products. Gynger Receivables is an AR automation giving finance teams receivables visibility, automated collections and cash flow insights. Gynger Capital is a financing engine providing upfront vendor payment and flexible buyer terms.

Involves credit decisioning, pre approvals flows and qualification, lending limits and payment processing. Gynger Pay provides a seamless offer-to-payment flow handling invoice presentation through payment processing.

We use AI extensively across our product team, from writing and grooming tickets to reviewing PRs. Developers choose whatever tools provide them the most value. Personally, I lean heavily on Claude Code for planning and debugging work.

There are few bug tickets I tackle without first asking Claude what the issue might be. AI coding tools evolve rapidly and we maintain an open mind about leveraging them responsibly while shipping production-ready code faster. You ask about a specific project, but the reality is AI is embedded in all of them at some level.

It's not a separate proof of concept or experimental consideration, it's simply part of our daily development process. With the advent of model context protocol agents and third parties opening up their capabilities to models like ChatGPT and Claude, the leverage available to developers and product teams is only expanding.

Since AI is part of our daily workflow, the impact is cumulative rather than project specific. Without AI, I'd spend significantly more time context switching. Debugging our credit underwriting flow means tracking down logs and tracing data through services.

With AI tools, I describe symptoms, paste code and get a hypothesis in seconds, collapsing 20 minutes of investigation into two. Code reviews would be slower. AI catches obvious issues, missing error handling, race conditions, unclear naming, before I push.

Without it, those surfaces in PR comments require another round trip. Documentation takes less time. Translating vague bug reports into structured tickets with repro steps and acceptance criteria used to take 15 to 20 minutes.

Now it's more like five minutes. The bigger shift isn't speed, it's cognitive load. AI handles pattern matching and boilerplate, letting me focus on architecture and business logic that requires human judgment.

You still need strong fundamentals to know when AI is wrong. But with them, AI becomes a force multiplier for shipping quality code faster.