Industry
The Problem with AI Terminology: Why ‘Hallucination’ is a Misnomer
An exploration of how marketing-driven AI terminology obscures technical reality and the need for a standardized, precise lexicon for developers.
Latest
180 stories in the archive
Policy
The Vatican’s Influence on AI Alignment and the Holy See’s Strategy
The Vatican attempts to influence AI alignment at labs like Anthropic to ensure Catholic social teaching is integrated into AI moral frameworks.
Industry
Shift AI: Training Embodied AI Through Free House Cleaning Services
An analysis of Shift’s strategy to collect physical training data for robotics by offering free house cleaning in exchange for surveillance.
Models
Liquid AI LFM2.5-8B-A1B: Efficient On-Device MoE Model Analysis
Liquid AI’s new MoE model balances 8.3B total parameters with 1.5B active parameters to optimize local inference speed and reasoning.
Models
Claude Opus 4.8: A Polished Refinement Rather Than a Cognitive Leap
An analysis of the Claude Opus 4.8 update, arguing that minor refinements in steerability and pricing are not substitutes for genuine intelligence gains.
Hardware
Google’s Coral Board: Local Gemma 3 Execution and the Hardware Gap
Google launches a compact board for local Gemma 3 execution, but faces challenges with SDK accessibility and competition from existing GPUs.
Models
Soro: A Specialized Gemma 3 Fine-Tune for the Tajik Language
Soro leverages Gemma 3 to provide a local, culturally nuanced LLM specialized for Tajik, prioritizing efficiency and local inference over generalist models.
Models
Evaluating the Trade-offs of the 4B Parameter Zerank-2 Reranker
An analysis of the latency and VRAM costs of using the 4B parameter Zerank-2 reranker in production RAG pipelines.
Models
Stability AI Releases Stable Audio 3 Open Weights for Local Inference
Stability AI releases open weights for Stable Audio 3 Small and Medium variants, enabling high-quality audio generation on consumer GPUs.
Research
EAGLE 3.1: Fixing Attention Drift in Speculative Decoding
EAGLE 3.1 addresses attention drift to provide more consistent and predictable throughput for LLM inference via speculative decoding.