Notes.

Analyzes the structural transformation whereby design systems (component libraries, design tokens, structured specifications) function as foundational infrastructure for AI agents, drawing on 27 academic papers and first-party information from 5 vendors. Provision of formal specifications reduces agent navigation by 33-44% and achieves 100% accuracy (Jin 2026); leveraging Figma JSON metadata outperforms pixel-only conversion (Gui 2026, ICLR); 5 companies simultaneously implement design systems as AI context layers. The meaning of investing in design systems has shifted from 'maintaining consistency' to 'determining the accuracy ceiling of AI agents.'

Analyzes AI-era design education curriculum reform drawing on 22 academic publications and information from 8 educational institutions plus public bodies. Three structural findings: (1) research concentrates on creativity development (35.9%), assessment (27.6%), and curriculum design (22.4%), with 66.7% not reporting outcomes (Musiienko 2026); (2) four newly required skills are identified (agency, domain knowledge, imagination, and aesthetic judgment); (3) no update to NASAD's AI standards has been confirmed. Reflective practice is being reconceived as 'learning by co-doing.'

Analyzes the impact of EU AI Act Article 50 (transparency obligations), effective August 2, 2026, on design practice through a corpus of 30 academic publications. Three structural problems are identified: (1) the scope of disclosure obligations is ambiguous, leaving the boundary between design editing and regulated manipulation undefined; (2) watermark and labeling implementation rates fall far short of regulatory requirements (38% / 18%); (3) greater disclosure granularity paradoxically erodes trust rather than improving user judgment. Designers confront a 'disclosure dilemma' — disclose AI use and lose client trust, or conceal it and incur legal risk — while gaps in copyright protection undermine the basis for billing.

Analyzes the structural divergence in creative industries where AI tool usage rates reach 90% while only 10% view the impact on their industry positively, drawing on 27 academic sources and 15 industry surveys. Three structural mechanisms are identified: (1) mandatory adoption generates symbolic adoption — using without endorsing (Heidenreich & Talke 2020); (2) AI triggers professional identity threat and psychological reactance (Mirbabaie et al. 2022; Jussupow et al. 2022); (3) technostress negatively moderates continuance intention across all pathways (Lu & Hu 2025, n=443 designers). A parallel trend appears among developers (Stack Overflow: trust declining from 40% to 29%, favorability from 72% to 60%). 'Using it but not endorsing it' is not individual irrationality but a structural collision between organizational pressure for efficiency and professional identity.

Reframing -- the act of restructuring a problem's frame to open new solution spaces -- is a core competency of design cognition. Centering on Dorst's Frame Innovation (2015), this note examines the cognitive mechanisms of reframing (abduction, problem-solution co-evolution), eleven practical methods for activating reframing (analogical reasoning, How Might We, assumption surfacing, TRIZ contradiction analysis, speculative design, the 9-step Frame Creation process, and others), inhibiting factors (design fixation, anchoring, functional fixedness), and conditions at the team and organizational level (boundary objects, strategic use of surprise). Reframing is not an innate creative gift; it can be deliberately activated through structured methods and environmental design.

Discusses how the professional experience of a field deploy engineer providing global on-site support for superconducting and cryogenic systems functions as an epistemological foundation for design, through career transitions into service designer, business designer, and design consultant. The trajectory in which the object of deployment abstracts from physical systems -> software -> learning experiences -> design methodology can be read as a concrete instance of Schon's reflective practice and Suchman's situated action. As designers' work shifts from 'making' to 'judging' in the AI era, the note shows through one practitioner's career that the sense of field deployment (the judgment to make a designed solution function in the context of the field) becomes a core capability of design.

This note traces the genealogy through which design has absorbed methods and theories from the social sciences to form its own distinctive mode of knowledge. Beginning with Simon's sciences of the artificial (1969), continuing through Schon's reflective practice, Cross's designerly ways of knowing, and Buchanan's wicked problems, the trajectory extends to the importation of ethnography, participatory design, practice theory, actor-network theory, and activity theory. HCI, design anthropology, and STS have served as bridging fields, while contemporary movements such as design justice, transition design, and more-than-human design are reshaping the boundary between design and social science. Design's distinctiveness lies not in the appropriation of social-scientific methods but in the integrative judgment that envisions artifacts not yet in existence.

A consolidated summary of the past seven days of 'AI and design' developments, collected in three tiers: T1v vendor primary, T2 public institutions and surveys, and T3 expert commentary. The main observations are same-day integration of new models into tools, the divergence between 90% usage rates and evaluations, and the role shift from makers to editors.

A mid-2026 survey of context and knowledge management methods designed for LLMs. Covers Google OKF (Open Knowledge Format), the Karpathy LLM Wiki pattern, the lineage of Context Engineering, the convergence of in-repo knowledge files (CLAUDE.md / AGENTS.md / Cursor Rules), tool connectivity via MCP, integration with PKM tools (Obsidian / Notion / Logseq / Tana), GraphRAG, and Fabric.

Organizes five patterns for tiering models within the Claude family (router, cascade, orchestrator + worker, task classification, cache stacking) to curb Claude Fable 5's token consumption. Applying findings from RouteLLM, FrugalGPT, and Anthropic's multi-agent work to Fable operations, cost reductions can be expected ranging from 40-70% for a single lever to 70-85% with all levers combined.