Tag: AI

DeepSeek Harness's model interface layer unifies Message, ContentBlock, GenerateOptions, and StreamChunk abstractions, turning heterogeneous model integrations—DeepSeek, OpenAI, LM Studio—into a pluggable translator system. This beginner-friendly post explains how the interface layer eliminates multi-model integration drudgery and how eight adapter rules let upper-layer business logic swap models without changing a single line of code.

In the past year, Agent products have proliferated, but DeepSeek Harness (DSH) asks a more fundamental question: if you want to build your own agent rather than use someone else's, what do you actually need? This post unpacks why DSH is not yet another terminal product, but a composable, auditable, and extensible agent infrastructure for builders.

Drawing on humanity's nearly fifty-year history of grappling with electronic screens, this piece speculates on five stages of AI domestication: from embrace and dopamine shift, to digital detox, anti-addiction systems, recognized mental disorders, and finally a return to retro-humanism. Pure entertainment / thought experiment.

From 'oracle' to 'boundary', the human-AI collaboration relationship has undergone four paradigm shifts in three and a half years. This article proposes a descriptive four-stage framework analyzing how control shifts from human dominance to human-defined boundaries, emphasizing regulatory risks and multiple evolutionary paths.

Proposes a Model-FSM-WF-RPA-HITL five-layer architecture that offers a risk-controlled, incremental engineering path for enterprise automation evolution from RPA to AI, illustrated through banking credit and high-end manufacturing quality inspection scenarios.