We've built the UI for a model-agnostic LLM that enables anyone to generate content in rich formats and do deep research on any topic. The UI includes a rich text editor with powerful AI features allowing for block-level remixing of any content including images, texts, and metrics.

Right now we're building the UI for a model-agnostic agentic AI workflow that can power enterprise systems.

I'm currently working as a Software Engineer specialized in Clojure and ClojureScript in the Payments Team at Lumanu to develop a payment solution that offers the best way for content creators to get paid and for advertiser brands to pay their creators.

As a striving content creator myself, I really believe in my company's mission to take the headache of running a business out of content creators' mind so that they can focus on doing what they love - creating amazing content that they can share with the rest of the world.

Here are some of the things I've accomplished so far on the job:

• Worked with the Payments Pod to build the Lumanu Payments service that processed more than $42 million in total payment volume between January of 2021 and July of 2022

• Decreases data load time on the frontend from a couple hours to a couple seconds by integrating pagination and more efficient filter queries

• Support the Payment Operations team by helping resolving money issues, manually transferring millions of dollar between customer accounts, rectifying failed migration, and reconciling inconsistent transaction records

• Contributed in building out the bulk payment feature that allows customer to process multiple payments quickly and efficiently

• Helped integrate Lumanu with Quickbooks to automatically generate QB invoices for every Lumanu invoice sent

• Picked up Clojure and ClojureScript on the job and was contributing to prod deploy within the first week

• Improved stock monitoring by implementing a script that processes market events in 5-minute intervals to capture the intraday price action, volume, and VWAP of every stock traded on a certain day

• Speeded up data analysis by creating a tool that reads market data files and performs various calculations for Sharpe’s ratio, average notionals/spread captures, rate of returns, etc.

• Worked with Amazon AWS and EC2 to create Jenkins servers, which are used to perform various automated tasks such as distributing code from main server to slave nodes and displaying system logs of various servers on a simple Apache web server

• Learned on the go how to code in C, and use various platforms such as Jenkins, Amazon AWS/EC2

• Enhanced Lupus clinical trial experience by creating an Alexa Skill that patients can interact with

• Performed unit testing to assess the performance of various features of the Alexa Skill

• Learned Alexa Skill Kits API, AWS Lambda, Amazon DynamoDB, Node.js