AI allows contextual computing.
...even more important than 'agentic' computing. LLMs allow qualitative insight at quantitative scale. That allows contextual computing for the first time ever.
Topic: quantitative scale
31 chunks · 20 episodes
4.4x burst in 2025 Q3
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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.
Topic summary
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A short read on the topic's time range, peak episode, and strongest associations. Use it as the quick orientation before drilling into examples.
- quantitative scale appears in 31 chunks across 20 episodes, from 2025-02-18 to 2026-10-12.
- Its densest episode is Bits and Bobs 8/25/25 (2025-08-25), with 5 observations on this topic.
- Semantically it travels with qualitative nuance, revealed preference, and llms allow, while by chunk count it sits between consistent bias and bobs; its yearly rank moved from #17 in 2025 to #107 in 2026.
Over time
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Raw mentions over time. Use this to see absolute attention, not relative rank among all topics.
Observations
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The primary evidence view for this topic. Sort it chronologically when you want concrete examples behind the larger pattern.
I think this paper on Full Stack Alignment is very important in an age of AI.
...f real human aspirations and desire. Luckily, LLMs allow qualitative insight at quantitative scale; for the first time, computers can understand what we want to want, and help us achieve it… if the system is aligned with the user's interest.
Bruce Schneier points out that LLMs will bring mass spying.
...ata happened rarely. That limited the oversight to things that could be done at quantitative scale, or the most egregious tails of behavior. A panopticon kind of game theoretic dynamic. But LLMs give qualitative insights at quantitative scale.[ge] ...
LLMs are infinitely patient so good enough ACLs aren't good enough any more.
...t was accidentally left open. Another outgrowth of the "qualitative insights at quantitative scale".
In the modern world you have to reduce a nuanced input to a number to collaborate.
...ern world you have to reduce a nuanced input to a number to collaborate. To get quantitative scale you need to lose qualitative nuance. But maybe you could use AI to collaborate on a higher dimension. Qualitative nuance at quantitative scale. What ...
LLMs applied to ads will be powerful and scary.
...s applied to ads will be powerful and scary. LLMs can do qualitative insight at quantitative scale. When applied to "ads" on top of billions of users' intimate context, that analysis and manipulation could be so subtle no one knows it's happening. ...
LLMs allow an imprecision in your goals in ranking.
...at couldn't be changed easily in the future. LLMs allow qualitative insights at quantitative scale. Now you could imagine the user just having a values.md file. A list of bullets and rough thoughts that the user periodically updates.
A data science approach is an industrial process.
...fore you had to do that to scale. But now LLMs give you qualitative insights at quantitative scale. In the past you had to reduce the data down to its common denominator to do math on it. Losing the nuance. Now you don't have to.
LLMs allow us to not force ontologies up front in a task.
...utting a damper on the very first step. But LLMs can do qualitative insights at quantitative scale. That means that more interfaces can allow a flexible data entry with clean up later. Especially if the LLM can help post-host structure. Post-hoc on...
Imagine a future where one entity has a dossier of everyone's deepest darkest secrets.
...d the data, but not the ability. It wasn't possible to do qualitative nuance at quantitative scale. LLMs allow qualitative insight at quantitative scale.
LLMs allow qualitative nuance at quantitative scale.
LLMs allow qualitative nuance at quantitative scale.[yf] Before, to get scale, we had to throw away a lot of nuance to get scalar values that could be easily summarized and interacted with. Qualitative...