Speaking of offloading thinking… I asked Gemini 3.0 Pro what it thought of this concept and criticisms in the comments: What are your thoughts on this project? Note criticisms in the comment section and tell me if you agree with any of them. https://news.ycombinator.com/item?id=46567400

This project, titled "Show HN: I used Claude Code to discover connections between 100 books," is an attempt to use an LLM (Claude) to perform "syntopical reading"—analyzing a library of 100 Hacker News favorite books to find thematic "trails" and connections between seemingly disparate texts. The author used CLI tools and LLM orchestration to index topics and generate these connections, aiming to move beyond simple summarization toward deeper insight.

Below are my thoughts on the project, followed by an analysis of the specific criticisms raised in the thread.

My Thoughts Conceptually, this is a fascinating experiment in "digital humanities." It attempts to automate a very high-level cognitive task: synthesis. Usually, we use computers for retrieval (search) or storage. Using them to find semantic bridges between concepts like "Jobs' reality distortion field" and "Theranos' fake demos" is a compelling use case for LLMs.

However, the execution reveals the current limits of this technology. The resulting "connections" often feel like a parlor trick—impressive that the machine did it, but often lacking the "click" of genuine insight. The project succeeds more as a technical visualization of vector embeddings than as a literary tool. It produces a map of linguistic proximity rather than conceptual necessity.

Criticisms & Agreement Analysis Here are the main criticisms from the comment section and my take on them:

1. The "Rorschach Test" / Spurious Connections Criticism: Users like tmountain, smusamashah, and timoth3y argue that the connections are "weaker than weak" or purely surface-level (e.g., linking "fracture" in one book to "crumble" in another). They suggest the project is an "LLM Rorschach test" where the human user forces meaning onto random noise generated by the model.

Do I agree? Yes. Reasoning: LLMs operate on statistical probability and vector similarity. They often confuse topical relatedness (these words appear in similar contexts) with causal or logical connection. A connection between "Us/Them" and "fictions" might make sense in a vector space, but to a human reader expecting a philosophical argument, it feels disjointed. Without the reasoning for the link being rigorous, the user has to do the heavy lifting to invent the connection, making the tool less of a "guide" and more of a "random prompt generator.”

2. Outsourcing Critical Thought Criticism: Users eloisius and DrewADesign argue that the project defeats the purpose of reading.[1] They contend that "the thinking is the point," and having a machine find connections robs the reader of the synthesis process that leads to understanding.

Do I agree? Partially. Reasoning: If the goal is learning, they are correct; you cannot learn by having a machine digest information for you. However, if the goal is discovery or research, this criticism is too harsh. Researchers have always used indices, concordances, and bibliographies to find connections they didn't know existed. If this tool is treated as a "super-index" rather than a "replacement reader," it has validity. The danger lies in mistaking the map (the AI's graph) for the territory (the actual knowledge).

3. Hallucinations and Conceptual Errors Criticism: User 8organicbits pointed out a weird label ("Thanos committing fraud" in a section about "useful lies") and questioned the logic of calling a fraud "useful" if the founder is in prison.

Do I agree? Yes. Reasoning: (Note: User Terretta clarified the commenter likely confused the comic villain Thanos with the company Theranos, which was in the text). However, the criticism about the label "useful lies" holds up. The LLM likely grouped "Theranos" under "useful lies" because the deception functioned for a time, but it lacks the nuance to understand that "fraud" and "useful tool" are categorically different to a human moral compass. This highlights the "alien" nature of LLM categorization—it organizes by semantic weight, not human logic.

4. "LLM Slop" and Fatigue Criticism: User typon and others noted the descriptions have a "distinct LLM voice" and dismissed it as "slop."[1] User Aurornis mentioned recognizing the writing style immediately.

Do I agree? Yes. Reasoning: By 2026 (the context of this thread), users are highly attuned to "AI-ese"—the perfectly grammatical but hollow, hedging, and overly enthusiastic tone of standard model outputs. This "slop" creates a trust deficit. When a human reads a connection written by a human, they assume intent. When they read one written by an LLM, they assume hallucination until proven otherwise. This high barrier to trust makes the project harder to enjoy.

Conclusion I agree with the consensus of the thread: Cool tech demo, shallow utility. The project is a great example of what LLMs can do (processing vast amounts of text to find patterns), but it inadvertently highlights what they cannot do (understand the deep, human significance of those patterns). It effectively automates the "what" but misses the "so what?"