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.
NobodyWho is an inference engine for running large language models locally and offline across multiple platforms including Android, iOS, and desktop. It supports various LLMs in GGUF format with features like multimodal input, text-to-speech, speech-to-text, and GPU acceleration via Vulkan or Metal. The tool provides SDKs for Kotlin, Swift, Python, Flutter, React Native, Expo, and Godot.
The article argues that autonomous Agent AIs trained with reinforcement learning will inevitably outcompete confined Tool AIs in both economic value and intelligence, because the same learning mechanisms that enable action-taking also enhance inference and self-improvement. The author contends that limiting AIs to pure computation without agency is an unstable equilibrium, as Agent AIs' superior capabilities across multiple dimensions will make Tool AI restrictions impractical.
This article explains LLM inference optimization techniques for production deployment. It covers the two-phase inference process (prefill and decode), memory management strategies like KV caching and PagedAttention, and methods including model compression and speculative decoding to improve speed, cost, and reliability without retraining.
A 200-clause Tsetlin Machine MNIST digit classifier implemented entirely on a Tang Nano 9K FPGA board, running inference through a UART interface. The design receives 98 raw image bytes and outputs a single predicted digit (0-9) with 100% accuracy on a 100-sample test batch.
Machine learning token costs are dropping orders of magnitude annually, with GPUs achieving exponential efficiency gains similar to 1960s Moore's Law. LLMs are becoming infrastructure rather than products, with frontier-quality models likely running locally on commodity hardware within 3-6 years, making quality and access—not token quantity—the limiting factor for AI adoption.
Open-weight models reached 56% of AI Gateway token volume in August 2026, up from 7% in December 2025, while token costs fell 23.2% that month. Anthropic's cheaper Opus 5 model tripled its spending share as users shifted away from the pricier Fable 5, though Anthropic retained 64% of total gateway spend. New models like OpenAI's Astra and Anthropic's Jev achieved rapid adoption upon launch.