CommieBobDole
What is it even supposed to do?
This is sort of like demonstrating a car that softens the asphalt in front of it with lasers to get a better grip; difficult, technically impressive, and zero chance that it will ever be allowed on a public road.
Edit: According to this Motor Trend article, it uses a big carbon-fiber pressure vessel at 5500psi, so basically an enormous fragile bomb. And the one that did all the stunts had two of the pressure vessels which would have filled most of the interior.
https://www.motortrend.com/news/tesla-roadster-thrust-system...
https://earthquake.usgs.gov/earthquakes/eventpage/us6000u18k...
I use cables from it once every few years.
This is probably a difficult-to-solve problem; given that they generate billions of these a day, not even Google can afford to devote enough compute to each query to reliably generate quality results. You can see this by selecting the "AI mode" from the search interface after getting the mediocre summary - the results are much better and generally perfectly usable. Though even that is probably a special minimal-compute version of the lowest tier of Gemini, it's still maybe an order of magnitude more capable than whatever generates the search summaries.
The bigger problem is that these search summaries are the default and by far the most common interaction that the general public has with "AI", and because this experience sucks, they just assume that all LLMs are similarly stupid and mostly useless. In non-technical spaces I frequently see the argument that "AI" is not useful for anything, all it generates is garbage hallucinations, and almost invariably they cite some actual terrible experience with the Google AI search summary. I would argue that the strategy of adding LLM summaries to every search is the worst of both worlds - it makes classic search worse while poisoning users against the idea of actual LLM-assisted search.
Likewise, there's a whole lot of day-to-day writing that mostly exists to communicate "I have written some text about this subject because I am expected or required to" and at most needs to communicate some simple information. LLMs will probably replace a lot of that writing, at least temporarily. As people increasingly use LLMs to consume that information as well, it will probably eventually be replaced by some sort of inter-agent communications protocol.
>"Massgrave, the group behind Microsoft Activation Scripts, has noted that Windows setup sends hardware info to Microsoft and receives identifiers back that are later used for Store access and licensing. Blocking GDID assignment breaks both activation and UWP apps."
I think probably it sends the gdid back to Microsoft as part of telemetry/updates/MS account periodic re-auth, which lets them know the current IP address, and then once the government has Microsoft's logs and the various target websites' logs, they can correlate based on IP address. I don't think it's actually sending the gdid to the web sites. Maybe.
This serves to further illustrate that nobody should be using Windows for anything that involves the need for privacy. And doubly, triply, and morefold so, nobody should ever sign a Windows machine into a MS account for any reason.
Congratulations, person who wrote the prompt that caused a machine to generate this article, you have truly achieved some sort of perfect something.
I run a local Qwen 3.6 27B at a 5-bit quantization on a 3090ti - it's a dedicated server that used to be my old gaming PC/workstation. I mostly use it with a chat interface for deep web searching and simple script generation. Here's what my setup looks like:
LLM host - the aforementioned physical box that contains the GPU and hosts Qwen 3.6 27B and also a CPU-only Qwen 2.5 1.5B that I use for generating chat titles in Librechat.
Librechat host - a VM hosting librechat; this provides the chat interface and interfaces with the search provider.
Search host - a VM with SearXing and Firecrawl.
The flow is:
- Qwen decides it needs to make a search tool call and does so - Librechat receives the call, and feeds it to the SearXing API. It receives search results in JSON format.
- Librechat feeds the result URLS to firecrawl, which scrapes the pages and returns the contents to Librechat.
- Librechat prompts Qwen with the results of the search, and it either formulates an answer or performs additional searches based on the results.
What I don't have yet is a reranker; this is a specialized model that analyzes the scraped documents and returns only the ones (or even just snippets) most relevant to the query. This would be useful, because sometimes the LLM ends up semi-randomly with very irrelevant results and spends multiple turns trying to get better ones and/or gives a bad answer. No specific reason not, I just haven't implemented it yet.
I'm old enough to see this process in action; I used to be young and in possession of esoteric knowledge that made me infinitely in demand and now most of the things that young people have esoteric knowledge about is things that I don't particularly care about, and I'm left with a lot of finely honed skills to solve problems that have mostly been abstracted away.
This is probably just a 'Kroger vs nice supermarket' thing.
Heck, even the 'benchmarks' are mostly somebody's attempt to crystallize their vibes with varying amounts of success.
It still generates every response using the model's pristine state with every new API call; whether the context is provided from the client or from a colocated cache server doesn't really change that.
It's a big file with a bunch of coordinates describing spatial relationships between tokens. When you give it a prompt, it uses those relationships to generate a string of tokens that is a statistically likely response to that prompt, then it stops. It's not changed by the experience. It doesn't remember anything. It doesn't sit around thinking on its own.
Even if the model itself were extremely complex, it's hard to imagine a definition of consciousness that includes something that doesn't remember and can't change.
First and most importantly, it's not really about LLMs, it's about AGI, and the second does not necessarily follow from the first; LLMs in their current state are pretty clearly not AGI, and most of the LLM-world progression in the last few years has been about better tooling/interfaces, refinements in training data and techniques and people learning how to use LLMs effectively rather than the huge leaps in fundamental capability that we saw in earlier years. It seems more likely that at this point, when AGI comes, it will be something entirely new or something that LLMs are only a component of, rather than "we built an LLM with ten trillion parameters and suddenly it became God".
Second, it's not even really about AGI, it's about AGI superintelligence. And more than that, it's about affordable AGI superintelligence, assuming that such a thing won't cost billions a year to operate.