February 2026

For two decades, product design has revolved around a stable premise: users initiate, software responds. Even as AI crept into products such as recommendation engines, predictive text, and fraud detection, the interface still framed intelligence as reactive. AI agents break that contract. An agent does not wait for instruction. It monitors context, forms intentions, takes actions, and adapts its strategy over time. It delegates to APIs, coordinates across systems, and sometimes executes decisions without asking first. In other […]

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An Interview with Android Engineer and AI Security Researcher Ivan Mishchenko As artificial intelligence becomes deeply embedded in mobile applications, the role of engineering judgment is changing. Today, building AI-powered mobile products is no longer only about innovation speed or model accuracy. It is increasingly about responsibility, security, and the ability to translate experimental ideas into production-ready systems. Hackathons have become an early reflection of this shift. Once focused primarily on rapid prototyping, they now function as testing […]

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Intent-Based Networking (IBN) simplifies network management, but its reliability is challenged by "intent drift", where the network’s state gradually deviates from its intended goal, often leading to silent failures. Conventional approaches struggle to detect the subtle, early stages of intent drift, raising alarms only when degradation is significant and failure is imminent, which limits their effectiveness for proactive assurance. To address this, we propose LEAD-Drift, a framework that detects intent drift in real time to enable proactive failure […]

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Class-incremental learning (CIL) with pre-trained models (PTMs) faces a critical trade-off between efficient adaptation and long-term stability. While analytic learning enables rapid, recursive closed-form updates, its efficacy is often compromised by accumulated errors and feature incompatibility. In this paper, we first conduct a systematic study to dissect the failure modes of PTM-based analytic CIL, identifying representation rigidity as the primary bottleneck. Motivated by these insights, we propose textbf{VILA}, a novel dual-branch framework that advances analytic CIL via a […]

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In real-world federated learning (FL) systems, client participation is intermittent, heterogeneous, and often correlated with data characteristics or resource constraints. Existing fairness approaches in FL primarily focus on equalizing loss or accuracy conditional on participation, implicitly assuming that clients have comparable opportunities to contribute over time. However, when participation itself is uneven, these objectives can lead to systematic under-representation of intermittently available clients, even if per-round performance appears fair. We propose cumulative utility parity, a fairness principle that […]

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The retreat challenged the narrative that AI eliminates the need for junior developers. Juniors are more profitable than they have ever been. AI tools get them past the awkward initial net-negative phase faster. They serve as a call option on future productivity. And they are better at AI tools than senior engineers, having never developed the habits and assumptions that slow adoption. The real concern is mid-level engineers who came up during the decade-long hiring boom and may […]

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