Hunbl-134 ((top)) -

98 % prediction accuracy, 5‑year battery life (with solar trickle charge), and compliance with the new “Zero‑Data‑Export” city ordinance.

Current AR headsets stream raw video to the cloud for processing, leading to latency >150 ms—unacceptable for real‑time collaboration.

Published on April 10, 2026

Use hun-quantize (part of the SDK) to automatically convert FP32 models to INT4 with <1 % accuracy loss—ideal for LLM inference on the edge.

Integration with existing search API, front-end adjustments for filter options. hunbl-134

Enhanced Search Functionality Description: Allow users to filter search results by date, file type, and relevance.

Existing edge solutions either sacrifice raw AI performance (microcontrollers) or incur high power/thermal budgets (x86/ARM server‑grade boards). HUNBL‑134 bridges that gap by around low‑power, high‑throughput AI. 98 % prediction accuracy, 5‑year battery life (with

| Block | Specs | Role | |-------|-------|------| | | 2 × 2.6 GHz, 8 KB L1 I‑Cache, 64 KB L1 D‑Cache | General‑purpose OS & high‑level app logic | | Cortex‑M55 | 4 × 1.2 GHz, DSP extensions | Real‑time sensor processing, low‑latency control loops | | NPU‑v3 | 16 TOPS, 8‑bit/4‑bit quantized ops, INT8/INT4 support | Deep‑learning inference (CNN, Vision Transformers, LLMs) | | RISC‑V Accelerator | 4 × Custom ISA extensions (cryptography, compression) | Secure boot, on‑device encryption, compress‑&‑store pipelines | | Shared L2 | 8 MB, unified cache, coherent interconnect | Low‑latency data sharing across cores |