I don't understand why almost always the AI doomerism needs to give embodiment the AI. Real life is not a movie needing visual effects.
AI doesn't need a body of itself to became dangerous. A politician could get into ChatGPT induced psychosis, that doesn't require a physical body, and it could happen now or even 3 years ago... I'm sure it has already happen...
Other pointed risks are interesting because they are basically the risk of empires loosing their tributary states. The world has always seen war mongering states trying to preserve their abusive advantages, humans doesn't need AI for that.
Overall, I would be far more concerned about some really dangerous humans than AI itself.
I tried the smallest ome, the only available, and got an error:
```
Could not load.
Error: failed to call OrtRun(). ERROR_CODE: 1, ERROR_MESSAGE: Non-zero status code returned while running GroupQueryAttention node.
Name:'/model/layers.0/attn/GroupQueryAttention' Status Message: Failed to create a WebGPU compute pipeline: A valid external Instance reference no longer exists.
```
But what users prefer? Given this is for marketing, which results produce more conversions? From the examples shown, personally I strongly preferred Claude Opus in all cases.
"Uniqlo x Akamai sells another design of shirt in the same range which is plainly incomplete"
Imagine having to return a t-shirt because that malfunction!
— I don't understand why are you returning this, was the size wrong or you didn't like it?
— No, there is a syntax error at line 37 that makes it impossible to run, and I'm concerned people on the street may think I promote unsafe bash scripting.
That sounds very similar to what we know in self-supervised learning as representation collapse. I wonder if we could copy some of the anti-collapse mechanisms from SSL into GPT... after all, they are ways to increment the differential entropy. However, I'm not sure if it could be useful after all: any pure function cannot produce more entropy than the entropy it receives... and natural language as text has much less entropy than other domains... [edit: typos]
This is interesting. Adam Smith said "People of the same trade seldom meet together, even for merriment and diversion, but the conversation ends in a conspiracy against the public, or in some contrivance to raise prices."
The annoying part is that, as the same Adam Smith says, regulating industries would end up enforcing such assemblies, reinforcing the problem... after all, industries can share information via the market itself...
And proposed solutions end up being controversial: employees ownership, open source, paying taxes over stocks ownership... or just hoping that colluders will be broken by a randomly ocurring incumbent...
Often, but not always. For my thesis, I ended up with a section related to porn. ChatGPT simply refused to spell-check that section. Also, recently I wrote a comment on HN about different subsets of English being easier to learn for native Spanish or German speakers, and the Samsung AI spellchecker refused to review it because it was considered "inappropriate content."
I had that discussion with my high school English teacher. We used USA oriented books and they often introduce "advanced vocabulary" which should have been trivial for Spanish speakers, or any latin language speaker for the matter.
I suppose they evaluate difficulty based on origin of the word. If you already know German or Spanish you may have a head start when learning English, but on a different subset of it.
I can do high level thinking for around 6 hours with just two scrambled eggs and a cup of coffee.
What I need is something to prevent me from context drift. /starts googling how many scrambled eggs are equivalent to the energy consumed by a data center. Google how many chickens are in the world.../
Simply Ruby on Rails. I maintain 3 markdown documents with system design, implementation plan and use cases in repo. Then tell it those files exist and go implement X feature (from implementation plan). These documents, plus AGENTS.md, declare completion criteria, which includes full code coverage both with system and controller tests.
Usually I don't tell it to implement something adhoc, I first implement it in the documents first. LLMs are quite good to keep those documents in sync.
A good part of the implementation plan is that it keeps the LLM on track. With it, the LLM can understand why something must not be done yet, so it includes less unsolicited functionality. My workflow surely can be improved, but it has worked well for me.
In not sure about the actual costs, because I started using the same subscription for document parsing. But even then, I used less than $10 in may.
I'm not sure that is really the case, or relevant in practice. I have been using OpenCode with DeepSeek lately (regular coding). For instance, today I got 120 million input tokens hitting cache, vs just 2.59million missing cache.
I have refused to lean too hard on agentic tooling for developing. I'm aware of the gains, I use it at my daily job. But I cannot afford to loss my brain skills, just in case they do a rug pull.
These week announcements are effectively Google doing a rug pull to its customers. Now simple changes cannot be done anymore within antigravity without it to consume its full quota.
Personally I downgraded my Google One subscription. I cannot justify paying Pro anymore, and thankfully I'm not AI dependent enough to pay Ultra.
No, increasing the offer of something decreases its value, always. Do not necessarily increases its demand. That is basic economic rule. See that I use "value", not "cost". The distinction matters.
Yesterday I went to a bookstore: saw an interesting book cover then I thought "ah, looks like AI"... all excitement went away. There won't be a "new complexity frontier" for artists that used to draw book covers. Or writers, actors, writers, etc.
AI is currently not enabling any use case which previously was "too hard". It is just reducing the value of stuff by increasing the offer and making people delulu about what they can achieve without proper knowledge.
Making good stuff requires paying attention to a lot of details. Even "simple" stuff can become incredible complex once you actually learn about how it must be done. Most of what we humans do is working on that space, not chasing projects Manhattans.
What do we get if population is disconnected of the true complexity of creating stuff? Perceived value decreases and if everything is perceived equally bad people will stop caring about quality. That is why fascism likes uneducated people.
So, that is about the AI contribution to "value" itself.
Now, is it true that AI will allow us to create more complex stuff that is not practical now? I would strongly disagree. The reason is Kolmogorov complexity: it is not possible to find the shortest program that describes a task. Describing it with natural language will not magically give us permission to avoid having to describe that complexity. What is the point of switching from C to English, if I still have to specify every little detail in a much ambiguous and verbose language? Programming languages are not the challenge, they are the solution to the problem of having to specify complex tasks in a reproducible way.
Now gathering everything together: that is why I think that generative AI makes things worthless: value reduction, complexity perception reduction (which reduces value), a population ignorant of the complexity will choose subpar options because "they are all the same garbage" and we will not get any superior engineering capability anyway.
It has always been possible to do it. LLMs are not a particular enabler for that.
The difference is that now it is worthless: there is no learning, no person caring about the result, nothing aspirational for the public to look towards... we used to enjoy those challenges, used to be proud of solving complex problems... now? Yeah, whatever, execute execute commit push, let another LLM "review" and call it a day.