YOLOv5

YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset and created By Ultralytics Team in 2020. This architecture uses single Neural Network to process entire image. It divides image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities. YOLOv5 is available in the Hub as a PyTorch module. You can use it directly in your code or in the Hub.

General
Relese date	June 25, 2020
Author	YOLO
Repository	https://github.com/ultralytics/yolov5
Type	Real time object detection

Libraries

Discover YOLOv5

YOLOv5 Repository Object detection architectures and models pretrained on the COCO data set
YOLOv5 Documentation GPT-4 Product description page
PyTorch Hub

Super-Infrared Vultr RAG - 6 Filter Agent

Super-Infrared v3.1: 2-mode + Bio Healthy + Ruang Teduh + SDM - No Phone Has This Spec. Inspired by Pasar Kebayoran Lama fire risk & garment factories Jakarta. SAFETY 4.7s: MLX90640 32x24 thermal (300C surface, 612C WHITE HOT), IMX219 8MP + 850nm IR fusion CCTV, ESP32-S3 relay buzzer 95dB + shutdown. 4.7s response for factory/mall/kos. AIR+BIO HEALTHY: NDIR 4-ch 4.26um CO2, 3.3um H2O, 3.4um Naphtol, 7.7um emissivity + SHT41 Swiss ±0.2C ±1.8% + MQ135 gas. Ideal 22-26C RH40-60% CO2<1000ppm. If >1000ppm: auto fan+relay+WhatsApp. Ruang Teduh 12-City AQI - shaded QC area for garment & obsidian altarum. SDM RECRUITMENT (Vultr Agent Rush HR track - required by jury): 1) Candidate screening via AS7341 spectral color blindness test + HX711 weight 4067/39, 2) Interview scheduling buzzer+Telegram+Meet, 3) Onboarding checklist training device, 4) Workflow tracking IMX219+HX711 KPI. All via super-infrared.local on phone, no extra tools. BRAIN: RPi5 edge AMD ROCm+Ryzen AI (Delegate not just Operate) + Vultr Cloud Inference AMD Developer Cloud EPYC+MI355X+Cisco Silicon One. Pattern: TensorWave 2x perf 40-60% saving, AT&T single-GPU millions, Cisco Healthcare. Open by Design, no CUDA lock-in, More AI/Watt/Dollar. Proof: amd_check.py + ROCm 6GW validation in repo. Repo super-secure-syst-v23 32 commits: gallery, Bio_Healthy_Room_Spec.md, Ruang_Teduh_Spec.md, SDM_Rekrutmen_Module.md. Pricing v3.0 outright system & technique license Rp1.5-3.5M (not hardware MLX Rp1.07M). Demo super-infrared.local, OTA, MQTT, Tailscale, Telegram /status. No phone has NDIR+MQ135+SHT41+MLX+AS7341+HX711.

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

Data infrastructure for Real World Robot Training

Robots fail outside the lab because their training data doesn’t transfer. We’re building a data infrastructure platform that converts real-world human demonstrations into reusable robot training data using consumer AR/VR headsets and guided capture workflows. Each demonstration is validated and enriched in a closed loop with action annotations, occlusion handling, and simulation-based verification. Instead of storing isolated trajectories, we decompose demonstrations into spatially and temporally structured atomic actions — reusable skill patterns that generalize across tasks and robots. The resulting skills are exported in industry-standard formats (LeRobot, ManiSkill, Isaac Sim) and can be retargeted across robot embodiments without custom engineering. Because XR devices are widely available, data collection can scale beyond robotics labs to everyday users.

RIFFAI

RIFFAI’s hackathon prototype demonstrates an end-to-end pipeline for energy site intelligence. The prototype ingests multi-source satellite imagery (Google Earth Engine + direct providers), weather feeds and customer training sets; applies an ultra-fast image/geo-metadata alignment pipeline; runs geospatial segmentation (U-Net/CNN) and time-series forecasting (LSTM); and produces explainable suitability scores and a ranked list of candidate polygons for renewable energy deployment. A minimal React web UI or API visualizes results and emits a sample monitoring alert (thermal anomaly or vegetation/water change). The goal is to show that fast metadata processing + energy-tuned models produce practical, developer-ready outputs in near-real time. We’ll provide domain guidance, sample tiles and a small labeled set for teams that request it.