Bothell, WA, Jan. 05, 2026 (GLOBE NEWSWIRE) — ReelTime Media (OTCID:RLTR) today issued its 2025 Year-in-Review, highlighting the Company’s most significant milestone to date: the successful birth, validation, and rapid advancement of its proprietary intelligence platform, Reel Intelligence (“RI”), launched in 2025.
The release describes 2025 as the year ReelTime validated Reel Intelligence (RI) as a distributed, self-learning, chip-agnostic AI platform able to generate video, images, music, voices, research, and code from a single system.
“2025 was the proof year,” said Barry Henthorn, CEO and CTO of ReelTime Media. “Reel Intelligence demonstrated that world-class AI does not require massive data centers, escalating power costs, or dependence on any single chip manufacturer. RI is self-learning, globally fluent, and improves as the connected world improves, while centralized AI platforms become increasingly expensive and constrained.”
Highlights From The Release
- Successful launch of a fully distributed AI platform designed to operate without centralized data centers.
- Validation of chip-agnostic architecture, eliminating dependency on any single hardware provider
- Delivery of integrated multi-modal AI outputs, video, images, audio, research, and code, from one system in virtually all languages
- Continuous capability improvement through self-learning mechanisms, without costly retraining cycles
- Significantly reduced energy concentration and operating costs compared to centralized AI models
ReelTime Media further reported that during 2025, Reel Intelligence achieved broad cross-lingual capability spanning virtually all major modern and historical languages, enabling use across global markets and disciplines without language-based limitations. Unlike traditional AI platforms optimized for a limited set of modern languages, RI was designed as a self-learning intelligence capable of interpreting, generating, and translating content across diverse linguistic structures and legacy languages. RI’s distributed and self-learning architecture allows its language capabilities to expand continuously without retraining cycles, geographic constraints, or infrastructure rebuilds, making the platform inherently global from inception.