This article examines how "vibe coding" – using LLMs to rapidly generate custom software – is transforming sensemaking and data visualization. Previously, bespoke tools demanded significant engineering resources or platform knowledge.
However, the emergence of AI has lowered these barriers, allowing users to create "disposable" interactive tools tailored to specific research tasks.
This empowers non-experts as "directors of design," but the author cautions against mindless trial-and-error, emphasizing the difference between exploratory tools for finding truth and classic visualizations for explaining it.
However, the emergence of AI has lowered these barriers, allowing users to create "disposable" interactive tools tailored to specific research tasks.
This empowers non-experts as "directors of design," but the author cautions against mindless trial-and-error, emphasizing the difference between exploratory tools for finding truth and classic visualizations for explaining it.
This article discusses how to conduct long-term research effectively using AI as a partner, moving beyond single-prompt queries. It emphasizes the need for "Long-Term Triangulation" – a continuous, iterative methodology. The author outlines four key pillars: building a persistent memory for the AI, tracking shifts in the AI's understanding, actively critiquing its responses with contradictory data, and performing meta-audits to identify blind spots in the research process. The goal is to foster productive friction and avoid intellectual echo chambers, ensuring both the human and the AI think critically.
A workshop at CHI 2026 in Barcelona focusing on how sensemaking is being changed by AI, including submissions for papers on sensemaking behaviors, tools, and the role of AI. The workshop will involve presentations, group discussions, and the development of insights into the evolving field of sensemaking.
This article discusses how AI tools can be used to enhance the reading experience by providing instant access to information and background details, similar to using a dictionary or Wikipedia, but with the ability to ask more complex questions. The author shares personal examples of using AI while reading 'The Dark Forest' and other books to clarify plot points and gain a better understanding of the material.
An analysis of the accuracy of image search tools like Google Lens, Gemini, and Bing, highlighting that while Google Lens is the most reliable, all tools can make mistakes and should be verified. The article uses examples from Yale University architecture to demonstrate these inaccuracies.
Explores how each generation believes it's uniquely facing unprecedented challenges, while often forgetting the struggles of those who came before. It discusses the cyclical nature of history and the tendency to view the present as uniquely fraught.
A review of the SearchResearch blog's 2025 posts, highlighting a shift towards AI-augmented research methods, testing AI tools, and emphasizing the importance of verification and critical thinking in online research.
This article details a method for finding books on your shelves using Gemini's text recognition capabilities. The author describes how taking pictures of bookshelves and using AI to scan them can help locate lost books and even provide insights into reading habits.
A blog post comparing when to use regular Google search versus LLMs for research, outlining the strengths and weaknesses of each. It details scenarios where search engines excel (facts, current events, specific sources) and where LLMs shine (analysis, synthesis, creative thinking). It also lists tasks LLMs struggle with, such as complex reasoning, real-time information, and fact verification.
An analysis of the quality of AI-generated summaries of a technical paper, comparing outputs from Gemini, ChatGPT, Claude, Grok, Perplexity, and NotebookLM. The author finds Gemini to be the best, highlighting the importance of context in prompting and the potential usefulness of AI summaries as 'extended abstracts'.
