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Article · 2026-08-19 · 6 moments

An LLM wiki changed how I work

And everything else I learned about productivity this year ✦ AI generated

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01
Claim

Using LLMs to build personal knowledge bases is the single most useful productivity improvement I tried this year.

The author built an LLM wiki after seeing Karpathy's tweet describing the concept, and found it to be the most impactful productivity change he made all year.

transcript

Casey Newton: As it so happens, it has. Of everything I tried this year to get better at the deskbound parts of my job, the LLM wiki has easily been the most useful. Like a vintage sports car, it requires a fair degree of maintenance, and there are almost certainly easier ways to create a personal knowledge base. But if you do any sort of work that has made you crave the help of a good research assistant, an LLM wiki might be worth your time.

02
Definition

The best test for whether a productivity tool actually works is longevity—whether you keep using it on new machines and can point to where it saves time.

The author argues that the most reliable measure of a productivity tool's value is whether you continue using it over years, install it on new devices, and can identify specific time savings.

transcript

Casey Newton: Given how often I switch apps, the best test for whether something actually makes me more productive is longevity. Do I install the app on a new machine? Do I renew the subscription when it's time? Can I point to the places where it actually saves me time?

04
Context

My previous memory systems failed because they required too much manual maintenance—Notion's agentic search was effortful, and my Capacities 'blips' system couldn't scale as topics proliferated.

The author tried two approaches to managing long-term story context—Notion's agent search and a 'blips' system in Capacities—but both required too much manual effort to maintain as his research expanded.

transcript

Casey Newton: It worked well enough, but I couldn't turn it into a habit. The search feature in the database itself is a simple keyword-based search; agentic search takes place on one of the app's many other surfaces; and the agent often failed to cite its sources without additional prompting. It worked, but it was effortful. Ultimately, though, even this system asked a bit too much of me. As the number of blips proliferated, my ability to consistently track stories across all of them waned. This, in turn, made me more reluctant to create new blips.

explains mechanism · 1

05
Example

The wiki is useful for fast-evolving, complex stories because it compiles detailed timelines with original sources, letting me quickly refresh my memory while verifying nothing was hallucinated.

For complex stories like the OpenAI/Hugging Face breach, the wiki's detailed timelines linked to original sources saved the author significant time preparing to write or appear on podcasts.

transcript

Casey Newton: How does this make me more productive? I've found it highly useful in fast-evolving, complex cases like the OpenAI / Hugging Face agentic breach, where we learned a little more about the story every few days for a matter of weeks. Each day as I prepared to write, or podcast, or go on someone else's podcast, I would pull up the page and refresh my memory — while also opening up the original sources to make sure nothing I was about to say was hallucinated.

explains mechanism · 1gives example · 1

06
Mechanism

The LLM wiki automates my old blips system—each morning it generates new pages for concepts in the news and updates a home page highlighting current stories, which directly generates podcast segments and story ideas.

The wiki automatically creates pages for new concepts as it reads daily news and maintains an updated home page, which has already led to podcast segment ideas by surfacing connections between current events and stored knowledge.

transcript

Casey Newton: It also gives me useful story ideas — and it does it by automating my old blips system. Each day as it reads, the wiki generates new pages for concepts in the news — like 'tokenmaxxing,' or 'youth social media bans,' or 'AI copyright.' It also updates a 'home' page every morning that highlights stories in the news. This week, spotting the 'AI and Congress' concept in my wiki led me to open the page and see a number of recent stories on the subject; I later pitched it as a podcast segment.

gives example · 2

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