Daily curates a variety of tech resources, tools, tips, and news (5G, big data, cloud computing, AI), aiming to become a go-to popular science encyclopedia for everyone.
When large language models automate workflows, the real challenge isn't efficiency but handling exceptions—information gaps, rule conflicts, and responsibility mismatches—that require transparent handoffs to humans, preserving context and enabling safe rollback to maintain trust and continuability.
Frequent, unannounced LLM upgrades in industry applications can silently alter decision boundaries, tool usage, and confirmation logic, breaking established processes. The article proposes a four-dimension observation framework and argues for reversible, observable iterations with versioned knowledge, rules, and orchestration to maintain trust and compliance.
The article argues that abundant logs and alerts don't automatically yield understanding; security teams need structured fact chains linking time, actor, action, and impact to explain incidents, citing NIST and CISA frameworks that emphasize risk explanation over mere detection.
This article argues that data sharing initiatives often fail not due to access permissions but because identical metric names mask divergent definitions, statistical scopes, and processing logic, a problem amplified in AI-driven workflows where semantic context must be explicitly preserved for reliable automated decisions.
This article analyzes why industry AI projects become harder to cost as model prices drop, identifying four hidden cost categories—context preparation, continuous evaluation, exception handling, and accountability—and argues that sustainable AI systems require shifting focus from per-call pricing to the cost of an acceptable business outcome.
This article argues that while user authentication has become frictionless, authorization remains the critical challenge in digital services, proposing a three-layer framework—identity, authorization, and execution—to separate concerns, citing China's 2025 Network Identity Authentication Measures as a policy shift toward minimal data collection and verifiable accountability.
As AI-generated voices and personas become indistinguishable from real humans, traditional trust signals like familiar tone and speech patterns are no longer reliable for identity verification; the article proposes separating identity, fact, and authorization checks and building a trust chain across content transparency, identity verification, action authorization, and feedback loops to combat fraud.
The article argues that as low-altitude economy moves from demonstrations to daily operations, the critical bottleneck shifts from aircraft performance to a service infrastructure that ensures flights are schedulable, guaranteed, explainable, and continuously improved through platform-based collaboration across stakeholders.
As large language models excel at interpreting unstructured inputs, rule engines grow more vital for enforcing deterministic, auditable boundaries on automated actions, ensuring reliable execution in high-stakes business workflows.
China's new AI anthropomorphic interaction regulation distinguishes continuous emotional companionship from functional AI, warning that retention-driven metrics incentivize harmful emotional dependency; the article proposes a 'relationship intensity' framework to evaluate products and advocates for design boundaries that preserve user autonomy, exit rights, and real-world relationships.
