Skip to main content

How ChatGPT o1 Helped Build XRPayroll

Image
By
Listen:

 Up until recently, I was a skeptic about AI’s role in coding. The idea that AI could replace developers? Let’s just say I wasn’t buying it. But after this December, my perspective has shifted. Not only can AI support developers—it can dramatically speed up development.

Here’s the story of how XRPayroll, my new app, came to life with the help of OpenAI’s ChatGPT o1.

From Simple UI to Functional App

When I started in December, I had a straightforward goal: build a simple XRP UI. Fast forward a few weeks, and XRPayroll is now an app with user management, admin login, and basic role-based access control (RBAC).

What’s incredible is that approximately 70% of this app was AI-generated. Using OpenAI’s ChatGPT o1, I managed to implement:

  • Vue.js Views: From basic layouts to dynamic components, o1 helped me structure and write reusable code efficiently.
  • HTML: Generating clean, functional markup without getting bogged down in the details.
  • SQLite Queries: Writing database calls with accuracy and speed, cutting down on time spent troubleshooting.

Yes, there were bugs. Debugging took effort, but the results were worth it. A quick look at the Git diff statistics shows over 20,000 additions and 6,000 removals. Without o1, achieving that level of progress would’ve taken me days—if not weeks.

What Is XRPayroll?

XRPayroll is a research project and a proof of concept (PoC). It connects to the XRP Ledger Community and Ripple’s testnet to manage payroll users in a simple, decentralized way.

Here’s what it does today:

  • User Management: Add, edit, and delete payroll users.
  • Admin Login: Role-based access ensures only authorized users can manage sensitive data.
  • Basic RBAC: Assign permissions to users based on their roles.

It’s far from perfect, and there’s still a lot to build. CSV import is currently in progress, and while API calls are implemented, they need refinement. Still, this foundation proves that managing payrolls with XRP and stablecoins is a viable use case for businesses.

The Road Ahead

XRPayroll isn’t ready for production yet, but the groundwork is solid. My focus now is on expanding its functionality, including:

  • CSV Import: Simplify user data onboarding.
  • Improved API Calls: Ensure seamless integration with external systems.
  • Enhanced Features: Add more robust RBAC, reporting, and compliance tools.

Why Stablecoins?

One of the core visions for XRPayroll is to act as a clearing provider between salary issuers and receivers using stablecoins like #RLUSD as a transfer hedge. The volatility of cryptocurrencies has always been a hurdle for mass adoption, but stablecoins offer a bridge.

With XRPayroll, companies could leverage blockchain technology to handle payrolls securely, transparently, and cost-effectively—all while hedging against market fluctuations.

Verdict

I’ll admit, I didn’t expect AI to handle so much of the workload. But ChatGPT o1 surprised me in three ways:

  1. Rapid Prototyping: Need a Vue.js component? Describe it, and o1 delivers a working draft.
  2. Efficient Debugging: While not perfect, o1 offered logical solutions for most bugs, cutting down on trial-and-error cycles.
  3. Code Refinement: o1 didn’t just write code; it suggested ways to improve structure, readability, and maintainability.

Sure, I still needed to oversee and guide the process, but the sheer speed and accuracy it brought to the table were impressive.

If you’re interested in contributing, the GitHub repo is open: XRPayroll. Feel free to submit PRs, suggest ideas, or just take a look around.

Labels:

Comments

Post a Comment

Popular posts from this blog

Beyond Ctrl+F - Use LLM's For PDF Analysis

By
PDFs are everywhere, seemingly indestructible, and present in our daily lives at all thinkable and unthinkable positions. We've all got mountains of them, and even companies shouting about "digital transformation" haven't managed to escape their clutches. Now, I'm a product guy, not a document management guru. But I started thinking: if PDFs are omnipresent in our existence, why not throw some cutting-edge AI at the problem? Maybe Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) could be the answer. Don't get me wrong, PDF search indexes like Solr exist, but they're basically glorified Ctrl+F. They point you to the right file, but don't actually help you understand what's in it. And sure, Microsoft Fabric's got some fancy PDF Q&A stuff, but it's a complex beast with a hefty price tag. That's why I decided to experiment with LLMs and RAG. My idea? An intelligent knowledge base built on top of our existing P...
Post a Comment
Read more

Deal with corrupted messages in Apache Kafka

By
Under some strange circumstances, it can happen that a message in a Kafka topic is corrupted. This often happens when using 3rd party frameworks with Kafka. In addition, Kafka < 0.9 does not have a lock on Log.read() at the consumer read level, but does have a lock on Log.write(). This can lead to a rare race condition as described in KAKFA-2477 [1]. A likely log entry looks like this: ERROR Error processing message, stopping consumer: (kafka.tools.ConsoleConsumer$) kafka.message.InvalidMessageException: Message is corrupt (stored crc = xxxxxxxxxx, computed crc = yyyyyyyyyy Kafka-Tools Kafka stores the offset of each consumer in Zookeeper. To read the offsets, Kafka provides handy tools [2]. But you can also use zkCli.sh, at least to display the consumer and the stored offsets. First we need to find the consumer for a topic (> Kafka 0.9): bin/kafka-consumer-groups.sh --zookeeper management01:2181 --describe --group test Prior to Kafka 0.9, the only way to get this in...
Post a Comment
Read more

Why Is Customer Obsession Disappearing?

By
 It's wild that even with all the cool tech we've got these days, like AI solving complex equations and doing business across time zones in a flash, so many companies are still struggling with the basics: taking care of their customers.The drama around Coinbase's customer support is a prime example of even tech giants messing up. And it's not just Coinbase — it's a big-picture issue for the whole industry. At some point, the idea of "customer obsession" got replaced with "customer automation," and now we're seeing the problems that came with it. "Cases" What Not to Do Coinbase, as main example, has long been synonymous with making cryptocurrency accessible. Whether you’re a first-time buyer or a seasoned trader, their platform was once the gold standard for user experience. But lately, their customer support practices have been making headlines for all the wrong reasons: Coinbase - Stuck in the Loop:  Users have reported being caugh...
Post a Comment
Read more