Intel BOT seems to be patches for specific binaries (hence why they didn't see a difference for Geekbench 6.7), unlike BOLT/Propeller which are for arbitrary programs. The second image from their help page [1] showcases this.
Data tagging? 20k tok/s is at the point where I'd consider running an LLM on data from a column of a database, and these <=100 token problems provide the least chance of hallucination or stupidity.
I don't think local as it stands with browsers will take off simply from the lead time (of downloading the model), but a new web API for LLMs could change that. Some standard API to communicate with the user's preferred model, abstracting over local inference (like what Chrome does with Gemini Nano (?)) and remote inference (LM Studio or calling out to a provider). This way, every site that wants a language model just has to ask the browser for it, and they'd share weights on-disk across sites.
> "Open source" to me is sharing the input required [...]
I don't disagree with your sentiment, I am also more interested in human-written projects, but I'm curious about how this works. Would a new sorting network not be open source if found by a closed source searching program, like AlphaDev? Would code written with a closed source LSP (ie. Pylance) not be open source even if openly licenced? Would a program written in a closed source language like Mojo then be closed source, no matter what the author licences it under? The line between input and tool seems arbitrary at best, and I don't see what freedoms are being restricted by only releasing the generated code.
I don't think your ultimatum holds. Even assuming LLMs are capable of learning beyond their training data, that just lead back to the purpose of practice in education. Even if you provide a full, unambiguous language spec to a model, and the model were capable of intelligently understanding it, should you expect its performance with your new language to match the petabytes of Python "practice" a model comes with?
Would this be similar to how Rust handles async? The compiler creates a state machine representing every await point and in-scope variables at that point. Resuming the function passes that state machine into another function that matches on the state and continues the async function, returning either another state or a final value.
If it really is fully autonomous, that first video is insane. I struggle to put those little tags into the slot in the box sometimes, and I'm pretty sure I'm human, but the bot gets it on the first attempt.
I see the idea, but you're competing with Microsoft Word and Overleaf for non-techies, and LaTeX/Typst for techies, and that sounds like a losing battle on both fronts. Non-techies want something familiar that they already know how to use, like Word, just with bib and their university's template. Techies probably don't want a cloud only service for a mostly solved problem. I don't see the value as a techie, and I don't see why I wouldn't just use my University's Word template from a non-techies view.
I wonder if this is a tactic that LLM providers use to coerce the model into doing something.
Gemini will often start responses that use the canvas tool with "Of course", which would force the model into going down a line of tokens that end up with attempting to fulfill the user's request. It happens often enough that it seems like it's not being generated by the model, but instead inserted by the backend. Maybe "you're absolutely right" is used the same way?
https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing