February 2026

Introduction: The Hypermedia Renaissance We are living in the middle of a hypermedia renaissance. Frameworks like HTMX have correctly identified that the “Single Page Application” (SPA) model often introduces unnecessary complexity, pushing developers to manage state in two places and build expensive APIs for what should be simple UI updates. The core premise of HTMX is brilliant: Hypermedia as the Engine of Application State (HATEOAS). By returning HTML fragments (partials) from the server instead of raw data, we keep […]

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The efficiency of Gröbner basis computation, the standard engine for solving systems of polynomial equations, depends on the choice of monomial ordering. Despite a near-continuum of possible monomial orders, most implementations rely on static heuristics such as GrevLex, guided primarily by expert intuition. We address this gap by casting the selection of monomial orderings as a reinforcement learning problem over the space of admissible orderings. Our approach leverages domain-informed reward signals that accurately reflect the computational cost of […]

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Quantum Machine Learning (QML) promises significant computational advantages, but preserving training data privacy remains challenging. Classical approaches like differentially private stochastic gradient descent (DP-SGD) add noise to gradients but fail to exploit the unique properties of quantum gradient estimation. In this work, we introduce the Differentially Private Parameter-Shift Rule (Q-ShiftDP), the first privacy mechanism tailored to QML. By leveraging the inherent boundedness and stochasticity of quantum gradients computed via the parameter-shift rule, Q-ShiftDP enables tighter sensitivity analysis and […]

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Coordinating traffic signals along multimodal corridors is challenging because many multi-agent deep reinforcement learning (DRL) approaches remain vehicle-centric and struggle with high-dimensional discrete action spaces. We propose MA2B-DDQN, a human-centric multi-agent action-branching double Deep Q-Network (DQN) framework that explicitly optimizes traveler-level equity. Our key contribution is an action-branching discrete control formulation that decomposes corridor control into (i) local, per-intersection actions that allocate green time between the next two phases and (ii) a single global action that selects the […]

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Predict-then-Optimize (PTO) pipelines are widely employed in computing and networked systems, where Machine Learning (ML) models are used to predict critical contextual information for downstream decision-making tasks such as cloud LLM serving, data center demand response, and edge workload scheduling. However, these ML predictors are often vulnerable to out-of-distribution (OOD) samples at test time, leading to significant decision performance degradation due to large prediction errors. To address the generalization challenges under OOD conditions, we present the framework of […]

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A revenue dashboard drops 18% overnight. The pipeline is ‘green.’ The lineage graph looks right. Query history shows the job ran successfully. Yet you still can’t answer the only question leadership cares about: what changed—and can we prove it? Traditional lineage is built for discovery: it shows what depends on what. Incidents demand evidence: what exactly ran, on which versions, with which logic and checks, and what blast radius that run created. Graphs show paths; incidents require proof. […]

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If you are into vibe coding, you probably know what Codex is. For those unaware, Codex is OpenAI’s cloud-based software engineering agent that can answer questions, fix bugs, and explore ideas right from Slack, Linear, GitHub, and other tools. It automatically pulls context from the conversation to choose the right repo and start working. If you wanted OpenAI to launch a standalone Codex app, that too for macOS, well, it’s finally here.  After months of expanding Codex across […]

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Elon Musk, the richest person in this world, is the owner of six different tech and engineering companies that are dedicated to five different things. First up, you’ve Tesla, which is an electric vehicle and energy company. Next up, you’ve SpaceX (aerospace and satellite internet), X (formerly Twitter), Neuralink (brain-computer interfaces), The Boring Company (tunnels), and xAI (artificial intelligence). Speaking of xAI, it has now been acquired by SpaceX. Yes, that’s the same SpaceX that I mentioned above. […]

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Diffusion policy sampling enables reinforcement learning (RL) to represent multimodal action distributions beyond suboptimal unimodal Gaussian policies. However, existing diffusion-based RL methods primarily focus on offline settings for reward maximization, with limited consideration of safety in online settings. To address this gap, we propose Augmented Lagrangian-Guided Diffusion (ALGD), a novel algorithm for off-policy safe RL. By revisiting optimization theory and energy-based model, we show that the instability of primal-dual methods arises from the non-convex Lagrangian landscape. In diffusion-based […]

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