Transformers power modern NLP systems, replacing earlier RNN and LSTM approaches. Their ability to process all words in parallel enables efficient and scalable language modeling, forming the backbone of models like GPT and Gemini. In this article, we b…
Vendors selling AI-powered security operations platforms have built their pitches around a consistent set of promises: autonomous threat investigation, dramatic reductions in analyst workload, and an accelerating path toward humanless operations. Pract…
Prompt engineering has become a standard part of how large language models are deployed in production, and it introduces an attack surface most organizations have not yet addressed. Researchers have developed and tested a prompt-based backdoor attack m…
Large language models carry a persistent scaling problem. As context windows grow, the memory required to store key-value (KV) caches expands proportionally, consuming GPU memory and slowing inference. A team at Google Research has developed three comp…
From MHA and GQA to MLA, sparse attention, and hybrid architectures
Remember when it was fun to play around with LLMs?
Open-source models are now one of the main engines of progress in AI. When strong models like Nemotron, Llama 3.1, and Qwen3-Next are released openly, the community can reproduce results, improve training recipes, and build new products on top…