You can access Google's SynthID tool only via Gemini. Which supports your first point!
I think this is trivial to implement for open weights models? The main issue is each open weight provider could choose their own randomisation key and so actually matching the synthID would be finding a needle in a haystack! Intractable at scale and a chore for even one chunk of text.
It doesn't look like the providers are going to publish their randomisation key. The google version is accessible only via the gemini prompt which only actually ran the synthID tool 1 out of 2 times I tried it the other day. So at the moment the best approach would be taking the suspicious test and using claude computer use to feed it into each provider's prompt brower window "manually"...
I had issues on my iphone 13 pro where switching between two apps would lead to one app "forgetting" what screen it was on. Particularly painful on Safari! Hard to know how much of that is memory pressure but its not a problem I have encountered on recent flagship android phones.
Maybe the curl thing is because they are happy to let you do some light scraping. What they want to avoid is bots directly crawling the page interactively. No one seems to be blocking chatGPT when I promot it to use it's web search skill anyway.
Those websites are janky despite having all the (non AI) solutions available already. They suck for reasons independent of the quality of LLMs. I have not worked inside a megacorp but my understanding is they suck because of what are essentially political roadblocks within the organisation.
Ok, let’s assume that’s the case and we’d never see those improve under any circumstances.
Where are all the killer new things? All we see are glorified hobby projects (guilty as charged) and an unimpressive pace of equally unimpressive feature additions to all the same old stuff.
Businesses with resources out there should tackling huge things we all want, that they never could have undertaken. But I’m not seeing it. Everything is just… more or less the same. And this tooling didn’t arrive last Wednesday.
Or did we get magical AI at a time when we’re also completely out of ideas? That seems unlikely.
This shit just isn’t adding up. We’re not seeinganything that isn’t more “here’s some more ai tools and services… they’re basically the same as the ones you saw last month.”
I agree they are over-hyped. Maybe the problem is that all the things LLMs are great for are essentially quality of life improvements. There are a dozen killer apps worth of functionality I get out of LLMs - near instant proofreader, one-shot code bugfixer, excellent cursory research tool, bibtext generator, etc. All of these things extend what I can do a bit but none of them instantly result in me being able to generate category killing apps. They still make stupid mistakes, they don't reduce my workload, but they reduce cognitive load on mundane tasks.
>Where my angst comes, is worrying that no one will ever get that experience anymore.
I am a fairly inexperienced python developer using LLMs to build software and find that I still learn a lot just from prompting and tinkering. Maybe that's less true once you reach a certain level of competence, but at my intermediate level I am still learning a lot even leaning heavily on LLMs.
Hell, $20/month is a pretty good deal for something that can proofread a 100 page report, do some light research work, and write some quick and dirty scripts. It doesn't have to replace the entire job of programming to be really damn useful. The pushback against using it even for these simple tasks seems utterly unreasonable to me.
I find it helpful to think of LLMs as reflections. If you can talk like an expert mathematician at the model it will respond like one. While Terrance's first prompt looks trivial I expect a first year Uni student would be hard pressed to provide something that good.
I guess it is kind of the inverse of the "you are an expert mathematician" prompt engineering of gpt3.5. Since no one ever says that to an expert mathematician when they are doing expert math the model immediately reflects that it is not an expert mathematician.
Isn't LLM doing something like argmax{x} P(x|context) where x is the next word, and context is the words before including your instructions? Thus, the LLM output is heavily depending on your instructions, and if you input an expert context it would produce an expert result. Consequently, domain expertise has a significant bearing on what you can achieve from LLM.
It seems too concise to be AI. My (conscious) heuristic for AI writing is how much context is squeezed into a sentence. LLMs seem to be pretty bad at the kind of elegant compression of meaning humans can do when they have done a lot of writing practice.
If I had to guess, I would say this is the human summarised conversation(s) with a bot.
Sorry if my speculation was bad! When I wrote it I meant it as a compliment. Raw LLM output is awful and really needs a human to "translate" the useful content to something worth reading.
I guess ask the bun people why they translated from zig to rust. I think it was essentially because rust guarantees a set of bugs can't exist so over medium to long term timeframes you end up with less technical debt.
I think this is trivial to implement for open weights models? The main issue is each open weight provider could choose their own randomisation key and so actually matching the synthID would be finding a needle in a haystack! Intractable at scale and a chore for even one chunk of text.
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