I largely agree with the premise. I was just honing in on the coding claim because the statement is so obviously wrong that it distracts from an otherwise solid post.
We should put software squarely in the "complex system" category, ie "The only way to find out is to build the software system and try to break it". Writing code may be cheap but running the software IRL is still constrained by both computational bounds and the laws of physics. Not to mention social and market dynamics.
If anything this strengthens the overall argument. "The limit is contact with reality".
> Coding, math and most administrative work are similarly benign. What makes them easy is that they have relatively smooth solution spaces and are tractably verifiable.
Code most definitely does NOT have an easily verifiable search space. P!=NP? Halting problem?
It's easy to brush this off and think those are obscure computer science trivia, but these things come up all the time. If we could prove software correct, we wouldn't need to test it.
> Most of what matters doesn't behave like that. The universe is mostly the emergent behavior of complex systems..... In systems like these, no amount of reasoning delivers the answer, because there's no shortcut hiding in the gaps. You have to run the thing
Software is clearly a complex system, with plentiful examples of emergent behavior. To understand the behavior, you HAVE to run the thing, in prod, on the open network, with real users, under real conditions, with real data. You can simulate these in testing but you can never fully reason about them beforehand - they are computationally irreducible.
The difference is huge. An LLM conversation can contain within it a batshit crazy model of the world and still produce a plausible stream of tokens. No skin in the game. If you don't test your ideas against reality, you don't have a model of reality; you have a model of written human thought. It's critical to understand the difference.
My point is that creatine has had significant attention on it for decades. OP perceives an increase in attention but that's just a reflection of their knowledge (Baader–Meinhof effect)
Creatine is a health fad? No other supplement has been this widely-used for such a long time. It was considered safe and effective when I started using it 30 years ago. The benefits are/were well established, the mechanisms well-known, the safety is impeccable. If anything, it's the most well-established supplement on the market - the exact opposite of a fad!
Ironically that's where I started my career and now I'm hoping to get back there. I was a data-oriented environmental scientist who happened to code.
But at some point, I slipped into 100% software development roles and I never got over how strange it felt to be siloed, isolated from the domain knowledge on purpose. It was far too easy to "win" the tech struggle of the day but produce nothing of actual value.
Dedicated coders were somewhat necessary to wrangle the tech stack (which itself grew more complex to accommodate more devs, a positive feedback which nearly spun out of control in late 2010s). But now? A single coder with domain knowledge has a shot at wrangling their own tech stack as a part time job, the rest of the time focussed on IRL value. This is the future I want.
My heaviest LLM usage month came in at $45. So far this July, $6 and counting (it's been a light month). I can certainly imagine increasing my usage by several orders of magnitude but ... why? If I have something that needs to get done and an LLM can do it, great. But I'm not sitting here inventing reasons to waste money, which is apparently what the tokenmaxers are doing - as evidenced by the astounding lack of value produced by all this vibe coding.
It's not just "the code itself looks LLM generated" - it's also LOC/hr by a particular author which suggests vibe coding. You could look at the author's github contributions to identify time periods when the author was generating code at super-human speeds. Combine the two signals and you might get something better than a pseudoscience?
Point datasets have two distinct modes of visualization. First is an aggregate view which serves to show you the trends and spatial distribution. Second is the individual view showing details about the point itself, its attributes, etc.
Clustering (for all its faults) is the only off-the-shelf technique for seamlessly switching between these two modalities without having to change the underlying data representation. Need more detail? Zoom in. And the zoom level is adaptive so it works with any scale.
There are better aggregation techniques (summing to a hexagonal grid, heatmaps, etc) but they generally require a separate calculation (possibly server side) and then switching to the raw source for the individual point view, either manually or at some hardcoded zoom level. It's not the same experience - it feels like two separate map layers instead of one integrated clustered layer.
This is mostly a matter of what's available in the mapping libraries. You could imagine building an alternative to clustering that calculates a heatmap on the fly when zoomed out, eventually revealing points as you zoom in. But presently this is something you'd have to DIY. For now, clustering is the only thing that works right out of the box.
> Ask your favorite GPT to generate manifests, ...
> Before LLMs writing consistent YAMLs was PITA but today on low/development scale it's pretty much free lunch.
Writing manifests seems like a trivial thing to focus on. Who operates the k8s cluster in production? Who runs upgrades? Who's on call to monitor the system? Of course if someone else is doing all the work for you, it feels like free lunch!
This is a consequence of our "AI accusation culture" that has arisen in the last year. An obvious reaction to the "AI slop culture" (seriously, stop doing that) but its a shame to see legitimate creators caught in the crossfire. This is not the first human-written article that's been wrongfully accused and it won't be the last.
I don't think people fully grasp the scale of a trillion dollars.
A trillionaire is a million millionaires.
It's impossible to efficiently manage that much wealth in one brain. Distribute it!
I'd run an essay contest. Every year the best thousand ideas are selected by yours truly and get funded at $100 million each. Even then, it would take a decade to burn through one trillion. It's a tough job, but I accept.
At what point does this become an issue for data quality and global epistemology?
It seems inevitable that we ask for more AI assistance on topics we don't understand. And therefore have the least context to correct. Result: a flood of poor quality information.
In areas we DO understand, we'll either not ask AI at all, or treat its results with a higher degree of skepticism. Result: a lack of high quality information.
Inevitably this means a higher volume of non-expert prompts gets translated into the next generation of internet content. AIs are pumping out more novice-level text and less expert guidance.
The result will be an internet full content written from the perspective of an ignoramus; not addressing any complex issues, staying surface level on every topic. Which will cascade into future models, etc.
Learn SQL (because it's basically the only option) but much more importantly, learn databases. Know why atomicity, consistency, idempotency, and durability matter. Understand the wire protocol and the client-server model. Do relational data modeling; think beyond databases as a dumb store. Join. Know when to normalize. Internalize indexing strategies. Think deeply about what work belongs on the database server (work that can leverage relational set theory) and what work stays in the application. Once you figure the true capabilities of databases, SQL as the language interface is a side note - about as important as the leather on your steering wheel.
Friction is the mechanism by which mammalian brains acquire skills. This is as close to proven as we get in cognitive neuroscience; without a struggle, we literally don't learn. This is not a controversial statement. The only way to improve our cognitive skills is to intentionally add friction - aka practice. Use it or lose it.
This isn't necessarily anti-AI. Imagine a tool that could quiz you, provide context for decisions, and make sure you're up to date on your knowledge of the codebase - instead of just writing code for you. IOW an AI-based system could intentionally add the right kind of friction to improve understanding.
Yep. If there was one single thing that literally every person should do for their health, that is to greatly reduce or completely eliminate sugar. The evidence is overwhelming.
The evidence against seed oils is not quite as convincing. I see seed oils as a low quality food to be avoided - goes rancid too easily, requires chemical processing, etc. - but it's not strictly poison. These oils are in virtually every industrial "food product" which makes them unhealthy by association. Stop eating highly processed crap and you'll see the benefits - cutting out seed oils is a side effect.
I watched people ask LLMs for linting/refactoring help, burning easily 5 minutes for something that could be completed deterministically, locally, in ms using any modern editor.
Quite frankly it was embrassing. We've had tools for static analysis for ages. Use them.
Someone with better knowledge could work 100x faster using 100x fewer resources. They did it the slow, expensive way but at least didn't have to think? Odd flex.
We should put software squarely in the "complex system" category, ie "The only way to find out is to build the software system and try to break it". Writing code may be cheap but running the software IRL is still constrained by both computational bounds and the laws of physics. Not to mention social and market dynamics.
If anything this strengthens the overall argument. "The limit is contact with reality".