Comments URL: https://news.ycombinator.com/item?id=47902415

Points: 2

# Comments: 0

See for example Suno Studio, which is not very good in my opinion, but shows the direction they’re going.

The term was popularized when asking a computer to do it for you became the dominant form of cognition. "Ape thinking" first appeared in online communities as derogatory slang, referring to humans who were unable to outsource all their thinking to a computer. Despite the quick spread of asking a computer to do it for you, institutional inertia, affordability, and limitations in human complacency were barriers to universal adoption of the new technology.

I do miss the days when technical reports were clear and concise. This one has some interesting information, but it’s buried under a mountain of empty AI-written bloat.

If anything, we have more intractable problems needing deep creative solutions than ever before. People are dying as I write this. We’ve got mass displacement, poverty, polarization in politics. The education and healthcare systems are broken. Climate change marches on. Not to mention the social consequences of new technologies like AI (including the ones discussed in this post) that frankly no one knows what to do about.

The solution is indeed to work on bigger problems. If you can’t find any, look harder.

But as I understand the situation, even the major Deep Research systems still have this issue.

I expect the benefit is from better Skill design, specifically, minimizing the number of steps and decisions between the AI’s starting state and the correct information. Fewer transitions -> fewer chances for error to compound.

> 3 days

still seems slow! I’m saying what happens in 2028 when your entire project is 5-10 minutes of total agent runtime - time actually spent writing code and implementing your plan? Trying to parallelize 10m of work with a “town” of agents seems like unnecessary complexity.

Most of the time, the LLM’s framing of my idea is more generic and superficial than what I was actually getting at. It looks good, but when you look closer it often misses the point, on some level.

There is a real danger, to the extent you allow yourself to accept the LLM’s version of your idea, that you will lose the originality and uniqueness that made the idea interesting in the first place.

I think the struggle to frame a complex idea and the frustration that you feel when the right framing eludes you, is actually where most of the value is, and the LLM cheat code to skip past this pain is not really a good thing.

My understanding/experience is that LLM performance in a language scales with how well the language is represented in the training data.

From that assumption, we might expect LLMs to actually do better with an existing language for which more training code is available, even if that language is more complex and seems like it should be “harder” to understand.