Good is about down-side capping.
Good is about down-side capping. Hill climbing. Great is about upside-creating. Finding a new mountain. Optimizing is about downside capping on the hill you're already on.
Topic: hill climbing
15 chunks · 15 episodes
2.9x burst in 2024 Q4
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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.
- hill climbing appears in 15 chunks across 15 episodes, from 2024-02-20 to 2026-09-28.
- Its densest episode is Bits and Bobs 2/20/24 (2024-02-20), with 1 observation on this topic.
- Semantically it travels with local maxima, selection pressure, and critical mass, while by chunk count it sits between existence proof and junk food; its yearly rank moved from #66 in 2024 to #94 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.
Hill climbing is super easy to execute, and has clear incremental value.
Hill climbing is super easy to execute, and has clear incremental value. That means that all else equal, even if it's not the best move, it's likely to be the move...
You can't hill climb if you can't look at the real signals.
You can't hill climb if you can't look at the real signals. Hill climbing only works if you're touching the ground. If you aren't touching the ground, change the learning rate. The higher the learning rate, the bigger the j...
First, get the loop to close, then get it to be tight.
First, get the loop to close, then get it to be tight. Getting the loop to close is going from default-divergent to default-convergent. The shift is an infinite difference that in the moment feels mundane. Once it's default-convergent, tightening is a simple matter of hill-climbing.
If it's a hill climbing exercise, first, make sure you're on the right hill.
If it's a hill climbing exercise, first, make sure you're on the right hill. For example, performance work should come after required architectural changes. When you have us...
Someone's personality could emerge even from very small starting biases.
...rting bias never even thought to say something funny, to start that compounding hill climbing.
Hill climbing a moving hill doesn't work.
Hill climbing a moving hill doesn't work. LLMs are moving hills. The models are still improving rapidly. Don't over optimize for their current behavior.
Learning happens in feedback loops.
...ce it's a loop it is self cohering as it turns. Hill finding is finding a loop. Hill climbing is turning a loop. Process praise sets the bar lower for what counts as viable for the learner, where they get some encouragement to keep going. This...
If you peek into how multi-modal models work, they're a ball of cheap, random hacks that turn out to be terrifically, unreasonably effective.
...appen to work. There is a selection pressure you can't see, possible because of hill climbing: a clear objective metric to experiment with. But they're hacks on top of hacks; almost certainly hitting a local maxima.
A judo move: if you can frame a problem as an optimization problem (without dangerous externalities) you can now use hill climbing techniques on it.
...em as an optimization problem (without dangerous externalities) you can now use hill climbing techniques on it. If you can fully "capture" the complexities of the argument as an optimization problem, it changes the character of the problem. La...
LLMs make the "good enough" viable zone larger.
...a viable product (it has achieved PMF with some small audience) you can move to hill climbing. How much you have to plan and test before shipping is tied to how easy the "good enough" target is to hit for users, in a way that stands out from o...
Once you frame it as a smooth problem (differentiable), it can be optimized and use hill climbing.
... you frame it as a smooth problem (differentiable), it can be optimized and use hill climbing. One of the reasons that identifying a good "self steering metric" can be powerful. For example, for rolling out an ambitious technology: "Maximize a...
An interesting pattern: using LLMs to astroturf content in an ecosystem.
...oturf content in an ecosystem. The challenge of an ecosystem is not so much the hill climbing of quality, it's the creation of the ecosystem to a critical mass. And now you can astroturf it!
The tech industry is in the boring hill-climbing phase.
...r being artisanal and instead being more about program management. We're in the hill climbing phase of PMing. PMs don't decide anymore, the metrics do. The humans are subservient to metrics. The hill -finding artisanal PMs didn't go away. They...
Hill climbing is easier to organize and coordinate than hill finding.
Hill climbing is easier to organize and coordinate than hill finding. With hill climbing everyone can agree, easily and with their own eyes, the steepest upward sl...