Browsing: LLMs

Google has officially released Android Bench, a new leaderboard and evaluation framework designed to measure how Large Language Models (LLMs) perform specifically on Android development tasks.…

Organizations increasingly deploy custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving frameworks—such as SGLang, vLLM, or TorchServe—to help gain…

Evaluating LLMs’ Bayesian capabilities As with humans, to be effective, an LLM’s user interactions require continual updates to its probabilistic estimates of the user’s preferences based…

def executor_agent(step: Dict[str, Any], context: Dict[str, Any]) -> StepResult: step_id = int(step.get(“id”, 0)) title = step.get(“title”, f”Step {step_id}”) tool = step.get(“tool”, “llm”) ctx_compact = { “goal”:…

Customizing Large Language Models (LLMs) currently presents a significant engineering trade-off between the flexibility of In-Context Learning (ICL) and the efficiency of Context Distillation (CD) or…

The generative AI race has long been a game of ‘bigger is better.’ But as the industry hits the limits of power consumption and memory bottlenecks,…

This post is cowritten with Remi Louf, CEO and technical founder of Dottxt. Structured output in AI applications refers to AI-generated responses conforming to formats that…

Large language models like ChatGPT, Claude are made to follow user instructions. But following user instructions indiscriminately creates a serious weakness. Attackers can slip in hidden…

AI-assisted fraud has grown sharply, making phishing campaigns more convincingDeepfake-enabled identity attacks caused verified losses exceeding $347 million globallySubscription-based AI crimeware creates a stable, growing underground…

Talking to software feels natural now, until you need real business data. That’s where things usually break. MCPToolbox to Databases fixes this by giving AI agents…