I handle over 50 emails every day. At first, I assumed that AI would merely help me speed up my response time. And indeed, it did just that. However, after a while, I began to feel that something was off. Although the replies were clearly worded, polite, and efficient. When AI Replies Stop Sounding Like You Most advice about using AI for email focuses on speed. Write faster, clear your inbox, move on. That works until every message […]

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Welcome to the latest HackerNoon Projects of the Week installment. Each week, we shine a light on standout projects from our Proof of Usefulness Hackathon—a contest built around the core question every builder should answer: Is my product actually useful in the real world? For each edition, we’ll highlight projects that demonstrate clear usefulness, technical execution, and real-world impact; all backed by data and not witty buzzwords. This week, we’re excited to share three projects that have proven […]

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How I built an AI-native Second Brain for engineering management — with Obsidian, Claude Code, and zero subscriptions. Seven unread Slack DMs. Three stacked 1:1s before lunch. A promotion packet review you can’t find. An incident follow-up thread someone archived. A decision from last quarter that lives in a Slack thread you can no longer search because it’s past the retention window. This is the job now. Not the work — the scaffolding around the work. Your brain […]

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arXiv:2602.03974v1 Announce Type: new Abstract: Planning in interactive environments is challenging under partial observability: task-critical preconditions (e.g., object locations or container states) may be unknown at decision time, yet grounding them through interaction is costly. Learned world models can cheaply predict missing facts, but prediction errors can silently induce infeasible commitments. We present textbf{Active Epistemic Control (AEC)}, an epistemic-categorical planning layer that integrates model-based belief management with categorical feasibility checks. AEC maintains a strict separation between a emph{grounded […]

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arXiv:2602.16873v1 Announce Type: new Abstract: As large language models from diverse providers converge toward comparable benchmark performance, the traditional paradigm of selecting a single best model per task yields diminishing returns. We argue that orchestration topology — the structural composition of how multiple agents are coordinated, parallelized, and synthesized — now dominates system-level performance over individual model capability. We present AdaptOrch, a formal framework for task-adaptive multi-agent orchestration that dynamically selects among four canonical topologies (parallel, sequential, hierarchical, […]

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arXiv:2603.15802v1 Announce Type: new Abstract: In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing. Ignoring these exogenous signals can substantially degrade forecasting accuracy, particularly when they drive spikes, discontinuities, or regime and phase changes in the target series. Most current time series foundation […]

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arXiv:2601.05374v1 Announce Type: cross Abstract: Empirical models of demand for differentiated products rely on low-dimensional product representations to capture substitution patterns. These representations are increasingly proxied by applying ML methods to high-dimensional, unstructured data, including product descriptions and images. When proxies fail to capture the true dimensions of differentiation that drive substitution, standard workflows will deliver biased counterfactuals and invalid inference. We develop a practical toolkit that corrects this bias and ensures valid inference for a broad class […]

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