AI & Machine Learning

AI agents are a new category of API consumer. They are not humans clicking through a UI, and they are not scripts following a predetermined sequence of API calls. They are semi-autonomous programs that decide at runtime which endpoints to call, in what order, and

Everyone has an opinion about microservices. The internet is full of conference talks about how Netflix split their monolith and blog posts about how you should not split yours. Both sides are right, depending on context. At Harbor Software, we completed a 14-month migration of

Load testing a traditional web application is well-understood: generate a realistic traffic pattern, measure response times and error rates at increasing load, find the breaking point, optimize. Load testing an AI application is a different beast entirely. The response times are orders of magnitude longer

Content summarization sounds simple: take a long document, produce a short version. In practice, building a summarization system that works reliably across document types, handles edge cases without hallucinating, respects length constraints consistently, and scales to thousands of documents per day is a genuine engineering

The Vector Database Decision If you’re building anything with embeddings — semantic search, RAG pipelines, recommendation engines, image similarity — you need somewhere to store and query vectors. The market has exploded with options, and the decision isn’t obvious. We’ve deployed all three of the

Every team building with LLMs eventually arrives at the same place: the model needs access to private data, and fine-tuning is either too expensive, too slow, or too rigid. Retrieval-Augmented Generation (RAG) is the answer most reach for. The problem is that most RAG implementations