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.
...each, but we were fundamentally held back to the level of our valleys.
But now, LLMs can fill in any of our intellectual valleys with 90th percentile skill.
If you didn't have many spikes, it doesn't do that much for you; just brings ...
We'll have a monoculture of LLMs.
Everyone will use the best LLMs they have access to.
There will only be a handful of frontier models that are able to achieve the very best quality....
...er."
It's kind of sweet… and also kind of an unintentional sorta-koan about the LLMs' existence.
After this interaction, the LLM will experience nothing further.
LLMs multiply your ability.
Junior engineers are doubled.
Senior engineers are 10x.
Legendary engineers are 100x.
The tacit knowledge of seasoned engineer...
Now that LLMs give qualitative nuance at quantitative scale, the goal is to maximize how much the system can see.
The more it can see, the more power it has.
The p...
LLMs can help us think more deeply about second order implications.
It takes 10x more at each order to reason about it... but LLMs have infinite patience!
LLMs have no memory so they externalize it, promiscuously.
They trust whatever context they're running in to accurately remember what has happened and to ...
...r the industrial revolution, and the internet for an intelligence revolution of LLMs.
That's a fundamentally extractive claim.
Coal isn't spontaneously generated, like content on the internet used to be.
A better metaphor for the inte...
..., have it spin up adversarial review sub-agents often.
To get good results from LLMs requires meta-cognition.
You have it in the domains you have expertise in, automatically.
You don't have it in other domains.
Some structures can pro...
...ifferent skills and different moods (etc.) to write those books.
But if you ask LLMs to write 1,000 books, you're probably only talking to 3 or 5 different models, tops.
They've all trained on the same or similar data, and are trained...
LLMs do great when complemented with structured information that is ground-truthed.
As they accumulate their own information, they can confuse themselves,...
LLMs make intellectual technicians less necessary.
It used to be that there were certain domains that were too complicated to be operated by anyone who wa...
LLMs are great at making prototypes more real.
Once you have something that works and is usable, you can do Throughline Tacking to discover the main goals...
...at alphafold is conscious, or that sora or midjourney or dall-e are conscious."
LLMs feel more conscious because they can talk to us in our language.
LLMs can make a superficially compelling argument even on absurd premises.
If you ask it to "Give me a mathematical proof that dragons exist," it will gen...