#edgeai

Complete technical blueprint of our Passive Edge + LoRa Mesh + Dual-Ledger Privacy architecture, Bill of Materials (BOM), and 5-Pillar mapping for the AquaAirX MATSYA Challenge 2026.

Direct engineering answers to: How fish weight is calculated, where the camera is physically mounted on different Indian fishing boats, and how the device detects net drop/haul events.

All-in-one comprehensive technical documentation of MATSYA-Node for AquaAirX MATSYA 2026. Detailed hardware selection rationale, physical mechanics, and end-to-end operation.

Comprehensive Phase 1 Idea & Solution Report for MATSYA-Node: An edge-native ocean observation, automated catch biomass estimation, and privacy-preserving data mesh system for Indian marine fisheries.

Edge AI view of VKORE-325M: pretrained from zero on a Rebellions ATOM-Max inference NPU, matching a B200 GPU twin on unseen text (0.7993 vs 0.7994).

Small Language Models: Bringing private, real-time AI directly to edge devices. How compact,...

Traditional deep learning has a flaw: once trained, its parameters are frozen. Great for static...

A 24B MoE with 2.45B active parameters: how much RAM, KV cache and memory bandwidth it needs on a laptop, computed from config.json and GGUF sizes.

Flox Intelligence builds edge devices running on-device AI models to deter wildlife from critical infrastructure like rail lines and airfields using sensor

Samsung's LittleBit headline is 0.1 bits per weight. We rebuild the file sizes from the paper and Hugging Face metadata to show what the number hides.

Running an LLM on a tablet or edge box means trading precision, memory, latency, and cold start. A practical guide to the choices that decide whether

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass ...