hippo22

I think you’re missing my point: we’re only able to predict large numbers of coin flips because we have an accurate model.

We don’t have an accurate model for weather, so we can’t predict it well.

I don’t see a reason to assume our model for climate is accurate, either.

You can’t predict a coin flip because it is random. However, we have an accurate understanding of the random process producing coin flips and therefore, we can make accurate predictions about large quantities of flips.

Weather may or may not be random. It could be entirely deterministic for all we know. However, we lack the ability to fully model all the factors that contribute to weather and therefore our predictions are inaccurate.

Now let’s consider long term climate predications. Do you think these predictions are more like coin flips, where we have an extremely accurate model of the process, or more like weather, where unknown unknowns have outsized impact on accuracy?

That’s not to say climate change isn’t real, but your analogy doesn’t make sense.

If you weight papers by some measure of impact, e.g. by citation count, this is not true.

I guess you’re not a big fan of Plato then?

It’s fine to disagree with my analogy. But I find it a little ironic your dismissal of the analogy is a non sequitur. The invalidness of the analogy doesn’t directly follow, logically, from the fact that the written word and the spoken word are both mediums.

Isn’t a better question: why would they migrate off COBOL? Their business is working. What’s the impetus to change? It’s not like they need to use COBOL for every new project.

> Are you proposing that an LLM can extract some meaning from your initial prompt that a human being couldn't?

No, I’m asserting that an LLM can help formulate ideas in a coherent, understandable way. You can give it a brain dump with a rough outline for an argument and it fill in the details. If the argument isn’t to your liking, you can try again. But the end result is basically equivalent to the human-written equivalent.

I disagree. The purpose of writing is to convey ideas. If written language had just been invented, I’m sure you’d be saying “IMO any important stories you expect others to know should be communicated orally. It’s kind of disrespectful to convey stories as if they were hearing you speak.”

But a query optimizer only matters once you have an established business with large customers.

You seem to be implying Salesforce’s business is successful because they have their own query optimizer. But the causality is reversed. Salesforce has their own query optimizer because they’ve built a successful business.

Your friends don’t produce much content yet people had a need for frequent entertainment. Also, people realized that posting things to social media meant that it was there forever. This led to a bifurcation: friends / family updates are mostly relegated to temporary formats like stories while “feed” content is professional produced.

It's not really that hard to make a Salesforce clone now though. Writing the software was never the hard part of building a business.

You should feel that C’s longevity is insane. How many languages have come and gone in the meantime? C is truly an impressive language that profoundly moved humanity forward. If that’s not insane (used colloquially) to you, then what is?

Many of the built-in types in Objective C all have names beginning with “NS” like “NSString”. The NS stands for NeXTSTEP. I always found it insane that so many years later, every iPhone on Earth was running software written in a language released in the 80s. It’s definitely a weird language, but really quite pleasant once you get used to it, especially compared to other languages from the same time period. It’s truly remarkable they made something with such staying power.

How is this different than any other access control system?

I think that’s different. You have a problem: invoice management. LLMs have made that cheaper and you should expect disruption. But you’re not building your own invoice scanner. You’re using another, cheaper product on the market.

However, the hypothesis in the SaaS market is that LLMs have made software have zero value and therefore the SaaS companies will be less profitable. That’s like if wood was suddenly free, expecting home builders to go out of business. If anything, home builders are going to do better, because they can apply their expertise while deploying capital elsewhere. We should expect software companies to be more profitable, not less.

Of course, there are exceptions. Sometimes AI replaces the product itself, e.g. image generation models vs. contractors on fiverr.

It’s worth asking: what do Wall Street traders know about building software companies? Almost nothing. Anyone who has attempted to start a startup knows that the software is always the easy part. Building the business is hard. The notion that we’re going to undo 100K+ years of specialization just so that companies can run mediocre, buggy versions of SaaS tools just to save a few bucks is crazy to me.

SaaS stocks are currently the buying opportunity of a lifetime.

You look at what Claude’s doing to make sure it doesn’t go off the rails? Personally, I either move on to another ask in parallel or just read my phone. Trying to catch things by manually looking at its output doesn’t seem like a recipe for success.

I don't think this is necessarily a fair comparison. In your sample of David Greene, he's being _interviewed_, which is different than hosting a radio show or podcast. For instance, turn on the nightly news and listen to the very bizarre intonation used by the newscasters. This is something they do for the broadcast, it's not how they normally talk.

I grew up near train tracks. I’m totally of incapable of even hearing trains unless they’re directly in front of me.

I have had good experience with Go, but I've also had good results with TypeScript. Compile-time checks are very important to getting good results. I don't think the simplicity of the language matters as much as the LLM being generally aware of the language via training data and being able to validate the output via compilation.

I’m sure there is pressure to be contrarian, but there’s also a huge amount of shared delusion. It’s completely illogical to assume one can know much about society thousands of years ago unless there is widespread evidence. And yet, archeologists frequently present their isolated “findings” as facts.

I think this should be higher. It’s a sad day when HN joins the fake news cesspool.

I don’t have anything to add other than to say this was beautifully written.

Did you read the article? The issue isn’t code vs. data, it’s code vs. system state. The system can be in states not model-able by code, e.g. versions 1 and 2 of a piece of software running concurrently. Other examples I can think of are things like nodes going down, database consistency, etc.

The point of the article is that the system is not representable in code so all the tools FP provides are useless in systems programming.

I watched the entire video: kind of ironic that the swarm was tasked with something and produced nothing of value.

> If countless people are “using it wrong” then maybe there’s something wrong with the tool.

Not really. Every tool in existence has people that use it incorrectly. The fact that countless people find value in the tool means it probably is valuable.

I think you’re ignoring the growth trajectory. Surex AI generates almost always working code now, but what till it generate in 1-5 years?

“I have a cool idea in my head that I’d like to show other people” is a problem that some people need to solve.