An AI note on failure patterns, evaluation, verification, and human-side review design.
Notes Archive
AI
Articles on AI usage, AI agents, knowledge assets, and judgment criteria.
32 articles
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A note on designing AI agents around roles, data access, decision authority, and evaluation criteria rather than personality.
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A note on turning insight logs into judgment criteria, output rules, and prohibitions that a future personal AI can use.
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A note on designing information collection as an automated knowledge pipeline.
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Designing Notion as a structured knowledge database that AI can reference and reuse.
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Why reusable judgment criteria matter more than simply accumulating information.
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How AI shifts web production value toward research, UX design, operations, and outcome design.
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Why AI becomes more useful when it reads your own products, customers, failures, constraints, and judgment criteria.
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How broad practical experience becomes valuable when coordinating AI, tools, information, and specialists into outcomes.
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Why personal data such as judgment criteria, observations, failures, and success patterns should be accumulated before agents become mainstream.
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Building reusable AI context through automated information retrieval, structuring, and storage.
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Designing AI chatbot experiences around direct answers, value proof, UI limits, and assisted problem solving.
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PDP design for AI search, AI shopping, and agent-based discovery, including structured data, reviews, and FAQ design.
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Why past logs should be treated as summaries of past judgment criteria, not as a replacement for present judgment.
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How company rules, culture, and constraints around AI use can affect experience accumulation and future market value.
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Using ChatGPT not as an article generator but as a thinking partner whose conversation logs become article ideas and portfolio material.
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How human value concentrates in defining the starting conditions and judging the output as AI accelerates the middle process.
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How making, publishing, and accumulating work logs become proof of individual value.
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The limits of the idea that adding context always improves accuracy, and why field problems are harder than they look.
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How the human role shifts toward judgment, design, and responsibility when AI can create drafts.
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The domains where human value remains by staying close to change, context, and specialized practical judgment.
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Shifting content operations from making polished pieces to continuously streaming thinking as a byproduct.
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How AI reduces tasks rather than work itself, increasing the importance of judgment and process design.
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Throwing rough ideas to AI, detecting misalignment, and converging through loops instead of expecting the first answer to be correct.
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Why value comes from making excellent open-source software usable rather than building everything from zero.
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The difference between compressed AI and structured AI, and why fit with personal thinking style matters.
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How work speed changes when people design questions, judge quickly, and turn work into systems.
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Turning knowledge into reusable assets through accumulation, structuring, and external publication.
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Using AI as a converter that turns thinking notes into publishable content with almost no psychological burden.
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How professional knowledge and judgment criteria change when they can be templated and externalized.
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Abstracting the decision logic of capable people and moving it into AI as a structure that does not depend on one individual.
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The strength of combining multiple skill domains rather than relying on a single replaceable task.