I Asked an AI to Build Me a Bank Data Platform. Here's What Happened. Claude wrote this post for me and we reviewed and revised it together. I know this won't be popular with at least a few of my regular readers, but it felt like the right thing to do given the project it's describing. Lambdas don't play well with RDS databases. Connection pooling, VPC cold starts, idle timeouts — it's a well-documented headache. But a Lambda is the obvious choice for a cloud-native Open Banking API implementation. And RDS is usually the best choice when you want to query and analyse data. I'd hit this tension before and never resolved it cleanly. So I wanted to see if Apache Iceberg could be the answer. It works with Lambdas (just write Parquet files to S3), and it can be queried like a relational database via Spark or Athena. I was also keen to see what the Lambda integration actually looked like in practice. I decided to pair with Claude Code on the build — treating it...
I have been reviewing Fran Buontempo's new C++ book , and somewhere between the chapters on modern idioms and the bits where she nudges you to actually do something with the language, I caught the itch. I have not written any C++ in more than twenty years, and the book reminded me how much the language has moved on without me. I wanted to host something small, written in modern C++, on a URL I could share. This project is the result. The brief I gave myself was deliberately tiny. A single HTTP endpoint that returns `Hello, World!`, built in C++, deployed somewhere real, with the whole thing reproducible from a fresh checkout. The point was not to ship a product. For the HTTP layer I picked Crow , a header-only framework with a Flask-shaped API. Header-only matters here because it keeps the build simple The whole server is twelve lines: #include <crow.h> int main() { crow::SimpleApp app; CROW_ROUTE(app, "/")([]{ return ...