January 2026

Think first, code later TL;DR: Set your AI code assistant to read-only state before it touches your files. Common Mistake ❌ You paste your failing call stack to your AI assistant without further instructions. The copilot immediately begins modifying multiple source files. It creates new issues because it doesn’t understand your full architecture yet. You spend the next hour undoing its messy changes. Problems Addressed 😔 The AI modifies code that doesn’t need changing. The copilot starts typing before it […]

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Offline policy learning aims to use historical data to learn an optimal personalized decision rule. In the standard estimate-then-optimize framework, reweighting-based methods (e.g., inverse propensity weighting or doubly robust estimators) are widely used to produce unbiased estimates of policy values. However, when the propensity scores of some treatments are small, these reweighting-based methods suffer from high variance in policy value estimation, which may mislead the downstream policy optimization and yield a learned policy with inferior value. In this […]

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[On agents using CLI tools in place of REST APIs] To save on context window, yes, but moreso to improve accuracy and success rate when multiple tool calls are involved, particularly when calls must be correctly chained e.g. for pagination, rate-limit backoff, and recognizing authentication failures. Other major factor: which models can wield the skill? Using the CLI lowers the bar so cheap, fast models (gpt-5-nano, haiku-4.5) can reliably succeed. Using the raw APl is something only the […]

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Based on recent practices in production environments using SeaTunnel CDC (Change Data Capture) to synchronize scenarios such as Oracle, MySQL, and SQL Server, and combined with feedback from a wide range of users, I have written this article to help you understand the process by which SeaTunnel implements CDC. The content mainly covers the three stages of CDC: Snapshot, Backfill, and Incremental. The Three Stages of CDC The overall CDC data reading process can be broken down into […]

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Accurately and efficiently solving nonlinear differential equations is crucial for modeling dynamic behavior across science and engineering. Physics-Informed Neural Networks (PINNs) have emerged as a powerful solution that embeds physical laws in training by enforcing equation residuals. However, these struggle to model nonlinear dynamics, suffering from limited generalization across problems and long training times. To address these limitations, we propose a perturbation-guided transfer learning framework for PINNs (PTL-PINN), which integrates perturbation theory with transfer learning to efficiently solve […]

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How are you, hacker? 🪐 What’s happening in tech today, January 17, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Persian Gulf War began in 1991, Popeye the Sailor made his first appearance in 1929, Google Videos launched in 2006, and we present you with these top quality stories. 680 Hours, 4 Rebuilds, and Getting Fired: How I Built Software While Working Warehouse Shifts By @huckler [ 4 Min read ] […]

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Spatio-Temporal (ST) Foundation Models (STFMs) promise cross-dataset generalization, yet joint ST pretraining is computationally expensive and grapples with the heterogeneity of domain-specific spatial patterns. Substantially extending our preliminary conference version, we present FactoST-v2, an enhanced factorized framework redesigned for full weight transfer and arbitrary-length generalization. FactoST-v2 decouples universal temporal learning from domain-specific spatial adaptation. The first stage pretrains a minimalist encoder-only backbone using randomized sequence masking to capture invariant temporal dynamics, enabling probabilistic quantile prediction across variable horizons. […]

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Many companies rely on external services to keep their operations running smoothly. However, while third-party vendors help power systems and support day-to-day operations, each new integration adds a potential access point that attackers can target. In 2026, third-party risk influences the speed at which incidents spread, the effectiveness of compliance, and the rate at which teams can recover. To prepare for what lies ahead, it is helpful to understand the current risks and know the steps IT teams […]

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Ive been trying to train a quadruped bot using reinforcement learning, mostly tryna teach it to trot and stabilize by itself. Ive tried different policies like PPO, RecurrentPPO and SAC but the results have been disappointing. Im mainly having trouble creating a proper reward function which focuses on stability and trotting. Im fairly new to RL so im looking for some feedback here. submitted by /u/iz_bleep [link] [comments]

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People can use generative AI to create art, text, and music from datasets of previous art, which is significantly impacting the current creative economy. The debate of what makes an artist and the lack of clear compensation are growing concerns, prompting the evolving issue of royalty battles over AI-generated work. The Evolution of Royalty Battles AI is changing royalty battles to a debate about what makes art original. The traditional notions of authorship and ownership are being abandoned, […]

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