Regal Arbor



An epic tale told over pixels, 8 years in the making. The closed alpha testing site is here and will have public statistics about the build progress of the game. Regal Arbor was originally built in Unity 3D version 2~3 before sprites were supported first class. Logic was written in unityscript, then C# back in those days. Third time is the charm! A more modernized approach with terraform handling IaC (infrastructure as code) gels together the Vue website. PhaserJs 3 is the current engine with some new advantages and disadvantages. With the main advantage being project orginization and modern vanilla javascript.


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S.A.T. Score Sends



Taking the S.A.T.? Sending your perfect results to a prestigious (or not so prestigious) college? Then there is a good chance you will be using this tool and workflow. A great number of people and talent behind the scenes that made such a tool possible. Choosing what you want to send to prospective universities is easier now with the score sending workflow


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Realtime Consumer Opportunity



An equity data mining solution. Monitors dms data feeds realtime for participating dealerships then arms sales persons within 15 minutes with customer proximity, a list of opportunities, and conversation starters that match the profiled personality to boot! The current offering is not public facing, but may become public facing in the near future.


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Universal DMS Feed



An evolution in how we've handled historical dms data in house. After discovery, data from N unknown sources runs through a series of rules. Failure to meet specification places individual rows to the side. Since we have at least five dms providers with changing schema and some manual entry. This allows alerts to be precise whilst continuing to deliver on the n% good data that always exists in a setting where users hand type information.


Terraform Vue Python AWS Dynamo Aurora S3 SNS SQS Lambda Cloudfront Cloudwatch API Gateway IAM CircleCi Github Cloud9

CDK DMS Feed Integration



Anyone whom has worked with the dms provider cdk knows just how much of a snowflake (unique server) cluster they have in the backend. I stood up a fault tolerant API interface in front of the CDK API so that our data driven products could be reliably powered by current dealership data. Datasets are not prejoined; we have to aqquire auxillary data whilst avoiding race conditions introduced by cdk's server overload and retries. Our service translates the xml structure into a more modern json while abstracting the taxing time sensative join complexities. Finishing touches included a chat bot named CDKen.


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