GLiNER2.5-Decide is a 340M parameter classification model that handles intent, routing, sentiment, and multi-label classification tasks in a single forward pass without prompt templates or generated tokens. Designed for operational decisions across customer service, banking, travel, and healthcare domains, it outperforms larger open models on a 17-domain held-out benchmark and can be loaded locally via AutoExtractor.
Bananabread AI launched an API that analyzes thousands of customer reviews across multiple platforms to identify what consumers love about products and what improvements they desire. The tool extracts specific quotes and evidence from Amazon, TikTok, Reddit, YouTube, Instagram, Walmart and other sources to help companies understand customer satisfaction and unmet needs.
A tool that analyzes Hacker News comments for sentiment, sarcasm, emotion, toxicity, and usefulness using Laya, a local System One decision model. It fetches random comments via the Hacker News API and scores them without requiring an LLM, API keys, or internet after the initial model download.
Oryn is a compact, low-latency decision model based on BERT Mini that performs classification by scoring dynamically supplied options at inference time rather than using a fixed output layer. At approximately 21 MB with 3 ms latency, it handles binary, multiclass, and ordinal tasks and was trained on phishing detection, reading comprehension, news classification, and sentiment analysis datasets. The model is optimized for local, CPU-friendly inference and works best on tasks similar to its training data.