u/OrewaDeveloper

After Karpathy's LLM Wiki gist blew up last month, I finally sat down and built one end-to-end to see if it actually good or if it's just hype. Sharing the honest takeaways because most of the writeups I've seen are either breathless "bye bye RAG" posts or dismissive 

"it doesn't scale" takes.

Quick recap of the idea (skip if you've read the gist): Instead of retrieving raw document chunks at query time like RAG, you have an LLM read each source once and compile it into a structured, interlinked markdown wiki. New sources update existing pages. Knowledge compounds instead of being re-derived on every query.

What surprised me (the good):

• Synthesis questions are genuinely better. Asked "how do Sutton's Bitter Lesson and Karpathy's Software 2.0 essay connect?" and got a cross-referenced answer because the connection exists across documents, not within them.
• Setup is easy. Claude Code(Any Agent) + Obsidian + a folder. 
• The graph view in Obsidian after 10 sources is genuinely satisfying to look at. Actual networked thought.

What can break (the real limitations):

• Hallucinations baked in as "facts." When the LLM summarized a paper slightly wrong on ingest it has effcts across. The lint step is non-negotiable.
• Ingest is expensive. Great for curated personal small scale knowledge, painful for an enterprise doc dump.

When I'd actually use it:

• Personal research projects with <200 curated sources
• Reading a book and building a fan-wiki as you go
• Tracking a specific evolving topic over months
• Internal team wikis fed by meeting transcripts

When I'd stick with RAG:

• Customer support over constantly-updated docs
• Legal/medical search where citation traceability is critical
• Anything with >1000 sources or high churn

The "RAG is dead" framing is wrong. They solve different  problems.

I made a full video walkthrough with the build demo if  anyone wants to see it end-to-end 

After Karpathy's LLM Wiki gist blew up last month, I 

finally sat down and built one end-to-end to see if it 

actually good or if it's just hype. Sharing the 

honest takeaways because most of the writeups I've seen 

are either breathless "bye bye RAG" posts or dismissive 

"it doesn't scale" takes.

Quick recap of the idea (skip if you've read the gist):

Instead of retrieving raw document chunks at query time 

like RAG, you have an LLM read each source once and 

compile it into a structured, interlinked markdown wiki. 

New sources update existing pages. Knowledge compounds instead of being re-derived on every query.

What surprised me (the good):

- Synthesis questions are genuinely better. Asked "how 

do Sutton's Bitter Lesson and Karpathy's Software 2.0 

essay connect?" and got a cross-referenced answer because the connection exists across documents, not within them.

- Setup is easy. Claude Code(Any Agent) + Obsidian + a folder. 

- The graph view in Obsidian after 10 sources is 

genuinely satisfying to look at. Actual networked 

thought.

What can break (the real limitations):

- Hallucinations baked in as "facts." When the LLM 

summarized a paper slightly wrong on ingest it has effcts across. The lint step is non-negotiable.

- Ingest is expensive. Great for curated personal small scale knowledge, painful for an enterprise doc dump.

When I'd actually use it:

- Personal research projects with <200 curated sources

- Reading a book and building a fan-wiki as you go

- Tracking a specific evolving topic over months

- Internal team wikis fed by meeting transcripts

When I'd stick with RAG:

- Customer support over constantly-updated docs

- Legal/medical search where citation traceability is 

critical

- Anything with >1000 sources or high churn

The "RAG is dead" framing is wrong. They solve different 

problems.

 

I made a full video walkthrough with the build demo if 

anyone wants to see it end-to-end 

Video version : https://youtu.be/04z2M\_Nv\_Rk

Text version : https://medium.com/@urvvil08/andrej-karpathys-llm-wiki-create-your-own-knowledge-base-8779014accd5

After Karpathy's LLM Wiki gist blew up last month, I 

finally sat down and built one end-to-end to see if it 

actually good or if it's just hype. Sharing the 

honest takeaways because most of the writeups I've seen 

are either breathless "bye bye RAG" posts or dismissive 

"it doesn't scale" takes.

Quick recap of the idea (skip if you've read the gist):

Instead of retrieving raw document chunks at query time 

like RAG, you have an LLM read each source once and 

compile it into a structured, interlinked markdown wiki. 

New sources update existing pages. Knowledge compounds instead of being re-derived on every query.

What surprised me (the good):

- Synthesis questions are genuinely better. Asked "how 

do Sutton's Bitter Lesson and Karpathy's Software 2.0 

essay connect?" and got a cross-referenced answer because the connection exists across documents, not within them.

- Setup is easy. Claude Code(Any Agent) + Obsidian + a folder. 

- The graph view in Obsidian after 10 sources is 

genuinely satisfying to look at. Actual networked 

thought.

What can break (the real limitations):

- Hallucinations baked in as "facts." When the LLM 

summarized a paper slightly wrong on ingest it has effcts across. The lint step is non-negotiable.

- Ingest is expensive. Great for curated personal small scale knowledge, painful for an enterprise doc dump.

When I'd actually use it:

- Personal research projects with <200 curated sources

- Reading a book and building a fan-wiki as you go

- Tracking a specific evolving topic over months

- Internal team wikis fed by meeting transcripts

When I'd stick with RAG:

- Customer support over constantly-updated docs

- Legal/medical search where citation traceability is 

critical

- Anything with >1000 sources or high churn

The "RAG is dead" framing is wrong. They solve different 

problems.