Humanoid robots operating in human-shared environments must implement multi-layered safety systems that prevent harm through hardware redundancy, real-time collision detection, and graceful fault isolation strategies. This article presents the safety architecture for the Open Humanoid platform (160–180 cm, ≤80 kg), covering hardware e-stop mechanisms with sub-100 ms response times, software wat...
Computer Vision: Depth Perception, Object Detection, and SLAM for Humanoid Robots
Autonomous humanoid robots operating in human-shared environments require a multi-layered computer vision stack capable of simultaneously perceiving scene geometry, detecting and classifying objects, and building persistent spatial maps — all within strict real-time latency budgets. This article presents the computer vision subsystem specification for the Open Humanoid platform, covering depth ...
Sensing and Perception: IMU, Depth Cameras, Force-Torque Sensors, and Sensor Fusion for Humanoid Robots
Reliable locomotion and manipulation in a bipedal humanoid robot depend fundamentally on the quality, latency, and fusion of sensory data. This article presents the sensing and perception subsystem specification for the Open Humanoid platform, covering inertial measurement units (IMUs), stereo depth cameras, six-axis force-torque sensors, tactile arrays, and joint encoders. We analyse sensor pl...
Review: Beyond the Illusion of Consensus — What the LLM-as-a-Judge Paradigm Gets Dangerously Wrong
Song, Zheng, and Xu (2026) argue that the LLM-as-a-judge paradigm rests on a fundamentally flawed assumption: that high inter-evaluator agreement signals reliable, objective evaluation. Through a large-scale empirical study involving 105,600 evaluation instances (32 LLMs evaluated across 3 frontier judges, 100 tasks, and 11 temperature settings), they introduce "Evaluation Illusion," wherein ju...
Measuring State Fragility: An Empirical RSI Framework Applied to Ukraine
We built something. Not a dashboard, not a report, not another data visualization that looks impressive but tells you nothing actionable. We built a ruler. A ruler that measures the same thing — instability — whether you point it at a country, a city, or a neighbourhood. The same 0-to-1 scale. The same formula. The same question: how close is this place to falling apart?
When the Economy Collapses, the Government Follows: Mapping the Dependency Between Economic and Political Instability
Venezuela's GDP contracted by more than 80 percent between 2013 and 2021 — one of the largest peacetime economic collapses ever recorded. Its political system, meanwhile, had not yet fully collapsed when the economy began its descent. The government survived by concentrating power, suppressing opposition, and externalizing blame. But the sequence is unmistakable: the economy fell first, and pol...
The World Is Less Violent Than in 2000. It Is Also Less Stable. Here Is Why.
The conflict proxy score — our model's aggregate measure of active armed conflict intensity across 87 countries — has fallen from 6.85 in 2000 to 5.20 in 2023. That is a 24% decline over 23 years. By the oldest and most intuitive measure of global danger, the world is meaningfully safer than it was at the turn of the millennium.
The Confidence Gate Theorem: A Framework That Promises More Than It Proves
Ronald Doku's "The Confidence Gate Theorem: When Should Ranked Decision Systems Abstain?" (arXiv:2603.09947, March 2026) addresses a practical but undertheorized problem: in ranked decision systems — recommenders, ad auctions, clinical triage — when does it help to withhold a prediction rather than fire it? Doku proposes two formal conditions — rank-alignment and absence of inversion zones — un...
The Algorithm That Watches the World Fall Apart
This article describes the development and deployment of the World Stability Intelligence (WSI) system — a machine learning-driven geopolitical risk monitoring platform that continuously tracks 87 countries across three risk dimensions: war risk (45%), political risk (35%), and economic risk (20%). Drawing on an ML-enhanced heuristic prediction framework (HPF-P), the system generates normalized...
When Your Research Gets Cited on Medium: A Clarification, a Thank You, and Why AGI Is Closer Than the Pessimists Think
A personal commentary on an unexpected Medium citation of research on AI infrastructure ROI. Clarifying the nuance between measured economic analysis and pessimistic interpretations, with a reflection on AGI proximity and a thank you to the author who sparked the conversation.