February 2026

The Clean Way to Access AWS, Azure, and GCP From Kubernetes (No Secrets, No Rotations)

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Introduction In modern cloud-native architectures, it’s increasingly common to run workloads in one cloud provider while needing to access resources in another. Whether you’re running a multi-cloud strategy, migrating between providers, or building a distributed system, your Kubernetes pods need secure, passwordless authentication across AWS, Azure, and GCP. This guide demonstrates how to implement cross-cloud authentication using industry best practices: AWS IRSA (IAM Roles for Service Accounts) Azure Workload Identity GCP Workload Identity Federation We’ll cover three real-world […]

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Two Ways to Build a Skill Server for Your AI Agent

Two Ways to Build a Skill Server for Your AI Agent

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How I stopped fighting context window limits and started giving my agents deeper, richer tool knowledge — on demand. The Problem with Loading Everything at Startup If you’ve built AI agents with more than a handful of tools, you’ve probably noticed something: the more tools you register, the worse the agent gets at choosing the right one. This isn’t a model limitation. It’s an architecture problem. When you register a tool in any agent framework — LlamaIndex, LangChain, Microsoft Agent Framework, or through MCP — its name, […]

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Authorization Belongs Inside the Agent Loop (Not at the Edge)

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The primary claim I make in Why Authorization is the Hard Problem in Agentic AI is that static authorization models are insufficient for systems that plan, act, and replan over time. In agentic systems, authorization cannot be a one-time gate checked before execution begins. It must be evaluated as part of the agent’s control loop. In this post, I’ll walk through a concrete demo that shows what this looks like in practice. Using OpenClaw and Cedar, we modify the agent […]

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AI-Chat with Strava — Developing an LLM-Integration with MCP

AI-Chat with Strava — Developing an LLM-Integration with MCP

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Ask your favorite AI to analyze your training data and give you suggestions for improvement! No problem! Here I’ll show you how to make that happen. >> Fast forward to my Github Repo. In my efforts to eliminate my perceived AI backwardness, I asked Google Gemini which AI is the best. After Gemini recommended Anthropic’s Claude — supposedly because it delivers the best results for coding — I treated myself to the Pro version of Claude. To be fair, I also signed up for […]

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Authentication Customer Segmentation — BEACON: K-Means Clustering

Authentication Customer Segmentation — BEACON: K-Means Clustering

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What is BEACON? BEACON (Behavioral Evaluation for Authentication Cohorts & Outcomes Network) is a data-driven framework that segments customers based on their authentication behavior using machine learning (k-means clustering). It groups users along interpretable dimensions of engagement and risk to better understand login behavior, fraud patterns, and retention outcomes. How do customers engage with the product/system? Customers engage with BEACON indirectly through their authentication behaviors — such as login frequency, success rates, friction encountered (e.g., challenge verification), and security-related interactions. Their […]

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Hallucinations in LLMs: A Deep Technical Dive into Causes, Detection, and Mitigation

Hallucinations in LLMs: A Deep Technical Dive into Causes, Detection, and Mitigation

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Large language models such as GPT, LLaMA, and Claude excel at producing fluent text, yet they share a critical failure mode that blocks many deployments: hallucinations. These occur when a model generates confident, plausible-sounding answers that are factually incorrect or unsupported by the available evidence. This write-up explores hallucinations through the lens of: training objectives probabilistic decoding model calibration retrieval and grounding evaluation and detection mitigation strategies in production systems What is a hallucination? An LLM hallucination is any […]

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How TikTok-Style Feeds Learn What You Want in Minutes

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Every day, billions of users scroll through personalized feeds on TikTok, Instagram, and YouTube, each experiencing a uniquely tailored stream of content. Behind this seemingly effortless personalization lies one of the most challenging problems in modern machine learning: how do you build recommendation systems that adapt to user interests in real-time, learning from every interaction within the same scrolling session? The engineering required to make this work, processing billions of events per second, updating models every few hours, […]

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Why Brazil’s Biggest Institutions Chose XDC, XRP Ledger, and Polygon for Tokenization

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Brazil has recently tokenized R$5.4 5.4billion of real-world assets, more than any other Latin American country. Surprisingly, only three blockchains cover 93 per cent of the latter market. According to the RWA Monitor data published in January 2026, the XDC Network has R$2.68, the XRP Ledger has R$1.7, and Polygon has R$506 million. Why did Brazilian giant institutions—Banco Itaú, VERT Capital, and Mercado Bitcoin—choose these three networks? Each blockchain solved a unique problem. Why Brazil Became Latin America’s […]

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Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling

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Federated learning on connected electric vehicles (BEVs) faces severe instability due to intermittent connectivity, time-varying client participation, and pronounced client-to-client variation induced by diverse operating conditions. Conventional FedAvg and many advanced methods can suffer from excessive drift and degraded convergence under these realistic constraints. This work introduces Fractional-Order Roughness-Informed Federated Averaging (FO-RI-FedAvg), a lightweight and modular extension of FedAvg that improves stability through two complementary client-side mechanisms: (i) adaptive roughness-informed proximal regularization, which dynamically tunes the pull toward […]

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A Novel Hybrid Quantum Circuit for Integer Factorization: End-to-End Evaluation in Simulation and Real Quantum Hardware

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The literature indicates that the qubit requirements for factoring RSA-2048 remain on the order of 1 million, under commonly assumed architectures and error-correction models, leaving a substantial gap between current resource estimates and near-term practical feasibility. Reducing this requirement to the low thousands qubit regime therefore remains an important open research objective. This work proposes a hybrid classical-quantum algorithm using a classical modular exponentiation subroutine with a Quantum Number Theoretic Transform (QNTT) circuit, to increase the speed and […]

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