How much more common this term is here than in ordinary English. Higher values mean the topic is more characteristic of this corpus.
1.6x 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 834 chunks across 141 episodes, from 2023-11-06 to 2026-10-12.
Its densest episode is Bits and Bobs 10/20/25 (2025-10-20), with 17 observations on this topic.
Semantically it travels with Claude, ChatGPT, and Meta, 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 2026-10-12Mean5.9 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 834 observations sorted from latest to earliest.
...ld add the word "useful" in front of intelligence.
You could imagine having two LLMs that require huge amounts of compute locked in an infinite debate spiral about how many angels can dance on the head of a pin.
Or more likely: a red ...
...usly only a very small set of "high priests" were able to wield this power.
Now LLMs as a collaborator have the world's knowledge and infinite patience.
Many more people can wield LLMs as a collaborator to marshal code.
...echo chamber.
It's especially important for people who spend a lot of time with LLMs, figuring out creative ways to work with them.
If you spend more time with LLMs than people, you could decohere from society and truth, with no groun...
...and ones.
Mechanistic systems could only handle black and white.
Embeddings and LLMs are now fuzzy, squishy, grayscale.
We can bring back the nuance!
This moment is the time for us to reclaim our nuance and humanity.
Before we were st...
...nder: sycosocial relationships are faux friendships with infinitely sycophantic LLMs.
It is an indictment of the system that lulled the human into that relationship in the first place.
That's what makes those kinds of relationships "s...
LLMs are good at things normal programs are bad at, and bad at things normal programs are good at.
Chat is great for things that computers used to struggl...
AX will matter more than DX.
If most code is written by LLMs, then Developer Experience at that layer matters less.
LLMs are infinitely patient, and prefer things that are like other common things.
Humans will ...
Specs are editable documents, so they are better for interacting with LLMs.
A powerful pattern is editable inputs to LLMs
Context that you intentionally factor out to be shareable in other tasks, vs implied context of "messa...
... of itself.
Only the most highly motivated organized would bother.
But now with LLMs it can provide extremely valuable context.
You accumulate the information in one place for your own use… but also help the LLM-powered tools to under...
The better LLMs get, the less context they need to feel truly personal.
They can read between the lines, expand beyond what you said.
But the quality of that context...
I'm optimistic about LLMs potential for humanity, and pessimistic about the slippery slope ChatGPT is on.
Not where ChatGPT is, but the drain it will circle.
Every hyperscale ...
LLMs aren't just a steamroller that we have to freak out about and fear.
LLMs are the most humanistic tech ever. What if we used it to be more human?
We h...
...domain that you're an expert in.
They got to set how technology worked.
But now LLMs democratize the power to create tech.
But now the experts can have that be unlocked without having to beg some random one-ply thinking technologist.
...
... not the busted ones.
Use cases that are transformative and not possible before LLMs have more return than use cases that were possible before LLMs but could just be a little more efficient with LLMs.
The assumptions of the systems ar...