Adi Polak

As AI agents evolve from stateless prompt-response tools into stateful, long-running systems, context - not just compute - becomes the true bottleneck. Yet most architectures today treat context retrieval as an afterthought, bolting vector stores onto LLMs and hoping for the best.

Data streaming is a really difficult problem. Despite 10+ years of attempting to simplify it, teams building real-time data pipelines can spend up to 80% of their time optimizing it or fixing downstream output by handling bad data at the lake.

Have you ever asked an AI language model like ChatGPT about the latest developments on a certain topic, only to receive this response:" I'm sorry, but as of my last knowledge update in January 2022, I don't have information on the topic at hand.."If you have, you've encountered a fundamental limitation of large language models.

For most of my professional life, I dealt with data. As a data practitioner, I developed algorithms to solve real-world problems leveraging machine learning techniques.