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An LLM wiki can turn saved articles and documents into a linked, searchable research archive—but keeping it accurate takes regular work. Journalist Casey Newton’s experience shows both sides: his system helped him retrieve context and spot a potential story idea, yet he describes frequent maintenance and does not recommend his specific setup to everyone.
What Newton built—and how it worked
Newton’s idea followed Andrej Karpathy’s observation, quoted in Newton’s essay: “Something I’m finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest.” Newton used Claude Code to create a prompt for what he called a “Karpathy-style LLM wiki,” then ran it in a terminal to create Markdown files in Obsidian.
He began by adding his Platformer writing archive dating back to 2020. For new material, he saves stories with Obsidian’s web clipper, which converts them to Markdown. A local script reads those files and decides where they fit in the wiki, while an Obsidian community plugin lets him ask questions across the collection.
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The system creates timelines linked to original sources, generates pages for concepts in the news, and updates a home page each morning. In his August 2026 account, Newton reported more than 1,440 pages. His Meta page was over 12,000 words and linked to more than 1,300 other wiki pages. These are his reported counts, not independently audited measures of the system’s value.
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Where the wiki helped his reporting
Rebuilding context on a developing story
Newton used the wiki to refresh his understanding of a fast-moving story about an OpenAI/Hugging Face agentic breach. The accumulated page helped him recall developments, but he still opened the original sources to verify details and avoid repeating a hallucination. The distinction matters: the wiki helped him find and organize context; it did not replace source-checking.
Finding a possible story idea
Newly generated concept pages also helped Newton spot a possible podcast topic about AI and Congress. That is an example of an archive prompting a connection, not evidence that an LLM wiki will reliably generate useful story ideas for every user.
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Why he moved beyond earlier note-taking approaches
Newton describes two shortcomings in workflows he had tried before. A Notion agent-search feature could take effort to use and sometimes failed to cite sources unless he prompted it further. In Capacities, he manually maintained short “blips,” but found the growing collection difficult to keep current. His wiki instead automated some of the work of fitting new stories into a broader set of linked pages.
That shift trades one kind of effort for another. More automation can make it easier to add material and retrieve connections, but it also creates a system whose generated pages, code, and links need oversight.
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The upkeep that comes with an LLM wiki
- Long pages need editing. Newton says pages can grow too long and require compaction.
- Automation can break. Code errors can stop a process, so a workflow that depends on scripts needs attention when something fails.
- Generated prose may need rewriting. Newton found Claude’s compressed style unsuitable for his needs and used another model to rewrite much of the output closer to AP style.
- Source verification remains essential. A generated summary or timeline is not a substitute for checking the linked source when accuracy matters.
Newton characterizes the system as requiring “more or less constant maintenance.” He also calls his setup too specific and clunky to confidently recommend generally. Its usefulness in his reporting should be read as one journalist’s experience, not a controlled demonstration that these systems improve productivity for everyone.
How to judge whether this workflow suits you
Before building a similar system, weigh the work it could save against the work it creates. The key questions are practical:
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- How much material do you need to organize? A large, growing archive may make automated indexing more valuable than a small collection.
- Can you verify claims quickly? Source links should remain easy to open and check, especially if you use the wiki for reporting or decisions.
- How much automation is worth maintaining? A local script and generated pages can reduce manual filing, but introduce code and model-output failure points.
- Will you regularly edit and prune the result? If routine cleanup is unlikely, pages may become unwieldy or less useful.
- Does it fit your existing habits? The approach depends on Markdown files, Obsidian, a web clipper, a local script, and an LLM-assisted workflow. If that is more friction than your current notes system, the added structure may not pay off.
What Newton’s account does—and does not—show
His account shows a concrete way to turn saved sources into linked Markdown pages that can be updated and queried. It also gives specific examples of retrieving story context and surfacing a potential topic. It does not establish a general productivity gain, quantify time saved, or prove that a large page count makes a knowledge base more useful. The most defensible takeaway is narrower: an LLM wiki may help people with substantial research archives, provided they are willing to maintain the system and verify what it produces.
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Read Casey Newton’s account in Platformer.
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