Writing secure code is hard. When you learn a language, a module or a framework, you learn how it supposed to be used. When thinking about security, you need to think about how it can be misused.

When you are connecting your company’s internal data to Large Language models through RAG, APIs, SQL, etc., are you sure that it is completely safe? There might be contracts signed with the LLM ...

hetida designer is a scalable, production analytics engine for Python Data Science with a focus on timeseries data. It manages (versioning, lifecycle), exposes (Rest, Kafka) and runs Python Data ...

tcapy is a Python library for doing transaction cost analysis (TCA), essentially finding the cost of your trading activity. Across the industry many financial firms and corporates trading within ...

After years of hype and speculation, OpenAI has officially launched a new lineup of large language models (LLMs), all different-sized variants of GPT-5, the long-awaited successor to its GPT-4 model ...

HANDS ON Getting large language models to actually do something useful usually means wiring them up to external data, tools, or APIs. The trouble is, there's no standard way to do that - yet.

Large language models (LLMs) by themselves are less than meets the eye; the moniker “stochastic parrots” isn’t wrong. Connect LLMs to specific data for retrieval-augmented generation (RAG) and you get ...