How much more common this term is here than in ordinary English. Higher values mean the topic is more characteristic of this corpus.
1.5x burst in 2025 Q1?
Peak quarter intensity across the topic's active span. Higher values mean attention was concentrated into a shorter stretch rather than spread evenly over time.
Related:?
Topics that appear in the same chunks as this one. Use this to find semantic neighbors, not ranking neighbors.
A short read on the topic's time range, peak episode, and strongest associations. Use it as the quick orientation before drilling into examples.
llms appears in 790 chunks across 131 episodes, from 2023-11-06 to 2026-08-03.
Its densest episode is Bits and Bobs 10/20/25 (2025-10-20), with 17 observations on this topic.
Semantically it travels with ChatGPT, Claude, and Google, while by chunk count it sits between Claude; its yearly rank moved from #2 in 2023 to #1 in 2026.
Over time
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Raw mentions over time. Use this to see absolute attention, not relative rank among all topics.
Range2023-11-06 to 2026-08-03Mean6.0 per episodePeak17 on 2025-10-20
Observations
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The primary evidence view for this topic. Sort it chronologically when you want concrete examples behind the larger pattern.
Showing 790 observations sorted from latest to earliest.
The key question: will LLMs just compound crap code quickly, or will it accumulate and accrete in useful ways?
Will code get so unworkable that it collapses under its own weight...
... cheaper than exploit.
That happens when any new input changes.
It's less about LLMs being great at explore (though that's part of it).
It's mainly that LLMs are ushering in a new paradigm.
I love LLMs and I hate chatbots.
I think chatbots are an embarrassing party trick.
Corporations pretending to be our friends.
Depressingly, this is all people th...
LLMs are gap-fillers and will fill all gaps implicitly with the most average input.
So it's your job to give them non-average gaps to fill, to inject the ...
Elevated and amplification are two related words around use of LLMs.
LLMs amplify whatever you apply them to.
You can apply them to something good or bad.
For example, curiosity vs laziness.
But elevated implies the r...
...t used to be the writer, the athlete, the actor, that we elevated.
But now with LLMs it will be the editor, the coach, the director who matter most.
There's a difference between vibecoding and Elevated Engineering.
Both use LLMs in new ways.
Vibecoding is a "make it work" mindset.
A good enough, satisficing mindset.
Elevated Engineering uses LLMs to extend your expertise.
For...
How will LLMs affect open source quality?
It definitely undermines the business models of e.g. Tailwind.
Those models are unlikely to ever work again.
But now engi...
LLMs are the best tech in the world to cheat at homework... and simultaneously, the best tech in the world to learn new things.
Is your default tendency l...
LLMs haven't seen significant traction in enterprise yet.
That's where users are more sophisticated and willing to pay.
Getting to low-sophistication, low...
...ike a betrayal.
That's why Google's data is a blessing and a curse in an era of LLMs.
They're sitting on a trove of data for each user… but if they preprocessed everyone's decades of emails it would feel like a crazy beytral, an invas...
...p and performant it can be.
Whereas if it assumes normal compute sweetened with LLMs there's no floor or ceiling.
And also if you assume LLM in the loop the only way to improve is model quality or tools.
Whereas normal code can accret...