Aaron Erickson discusses the evolution of AI workflows, shifting from "vibe checking" to building reliable, multi-agent ...

Abstract: The mainstreamTransformer-based Large Language Models (LLMs) have demonstrated to exhibit remarkable performance in various Natural Language Processing (NLP) tasks. However, high ...

AI writing tools are supercharging scientific productivity, with researchers posting up to 50% more papers after adopting them. The biggest beneficiaries are scientists who don’t speak English as a ...

Abstract: Increasing competition in software development is accelerating the pace of delivery. At the same time, it remains crucial to maintain software quality and prevent budget overruns. One ...

We tested both on writing, coding, research, and video. See which one fits your workflow, budget, and use case.

Model-based testing (MBT), whereby a model of the system under test is analyzed to generate high-coverage test cases, has been used to test protocol implementations. A key barrier to the use of MBT is ...

In recent years, the industry has seen a rapid push toward the implementation of agentic AI and LLM systems in KYC processes, combining multiple responsibilities into a single AI-driven pipeline.

As vision-centric large language models move on-device, performance measured in raw TOPS is no longer enough. Architectures need to be built around real workloads, memory behavior, and sustained ...

State control of the media is shown to alter the training data of large language models (LLMs) through its impact on the information environment. This has a substantial effect on the output of LLMs, ...

Tiny-LLM keeps the repository surface deliberately small: CUDA/C++17 kernels, W8A16 quantization, explicit KV cache management, and a narrow runtime path that is easier to audit and maintain.