NitpickLawyer
The insistence of naming it open weights as opposed to open source is getting ridiculous, and it's both irrelevant (i.e. no one cares in practice) and factually incorrect.
Weights are source in language models. Apache defines source as ""Source" form shall mean the preferred form for making modifications". That is precisely what's happening here. Everyone is using the preferred form for making modifications to these models (including the model creators themselves). A model is "created" at init time, and then "trained" by modifying the weights.
All these models are open source. What's not open sourced (with qwen et all) is the training code. So open source model, no training code. And that's ok. There are labs that release those as well. Apertus and Olmo series come with open source models, open source training and open datasets. Nemotron comes with open source models, open source training and some open datasets, while others are not published. And that's ok too.
The fact that you see all these models being modified (from AR completion models to "decision models") and re-released should be all the proof you need. That's what a license offers you. The right to inspect, run, modify and re-release a model. A license cannot (and never did) give you any other rights. OpEnWeIgHtS is silly.
A huge reason for that is the knee-jerk reaction of some communities (gaming especially, for some weird reason) at AI usage. So people build for an audience of one (maybe sharing with their closed friend group), and move on with their lives.
IIRC Oppenheimer facilitated / sponsored a meeting there after the Manhattan Project, as a gathering where physicists could talk freely about physics for a change.
Isn't it better if the tool is sandbox agnostic and you as the developer / integrator choose what's best for your use case? There are several levels of sandbxing, with many degrees of "freedom", so it would be really hard/confusing/overly-complex to build something ootb that suits everyone, no?
There is no official qwen 3.8 9b
From the model card:
> Clef-Flash is post-trained from Qwen/Qwen3.5-9B. See Clef for the larger variant.
Nothing listed in this comment is going to lead to "the demise of the starship project". And nothing listed there is original. We've heard it time and time again, when F9 was ready to launch, when F9 was ready to land, when F9 did land (bbbut they have to do it constantly), when F9 managed the first re-use (they need to do it at least 10 times so it's worth it) and so on and so forth.
They've now launched AND landed >600 times already. Let's just say they know what they're doing.
Anyway, for a bit of context: the current first orbital flight, with 26 satellites deploying right now, is adding the same amount of Starlink bandwidth as 10+ F9 launches. Let that sink in and re-do whatever cost/opportunity and "management" spiels you need to do. One flight > 10x F9 flights.
That alone justifies the Starship. The rest is the cherry on top.
Misaligned stuff is the prompter's fault. Those should be caught by the human. they're the types of tasks that are fast/cheap to verify.
You should check the logs, there's probably a bunch of retro gaming forums that got hacked behind the scenes :D
Yeah, it could. The most obvious usage would be to have local fast cheap "feedback" / "control" over a slower more expensive agent (i.e. cc / codex / opencode). Things like "goals" could now be split from a long prompt into "actions" and "verifiers". Where for each action you also produce a verifier. Then after each action you run the verifier w/ this kind of "universal classifier" and decide if the step was done correctly, if it needs follow-up and so on.
Example: implement auth in this repo -> llm_plan() -> for item in plan generate_verifier() -> for item in plan implement() ; verify() ; accept() / followup().
Verifiers could be something like this. take a plan item as input, generate classification questions that might verify the task "is this following project conventions?" | "is this touching files from other tasks?", etc.
You can do that with LLMs, but some things might become cheaper / faster. And you can pretty much use it to check against an ever growing list of conventions. Yours or project specific.
Also, models are now good enough that you can give them chapters from "authoritative" books, and they'll integrate that and come up with better answers even if their "vanilla" answers were average. And they'll tailor stuff to your particular situation. It's funny that the "agentic" stuff is only used in coding mostly, while it can and does work in other fields as well.
As always, you kinda need to check it (at least spot check) but all in all I'd agree it's better than the average stuff you used to find with a quick google search.
citation needed. It has been used as a rubicon for a long time. Ever since Eliza, at least. And there were big headlines and lots of talk around the time LMs became "good enough". I specifically remember when someone had a test done around "a teenager talking in a different language" or somesuch, claiming it was the first time the test was passed.
It is pretty normal that once it was unquestionably "passed", lots of people started claiming it wasn't even that big of a deal. Tesler's theorem and all that.
And even if you think the specific formulation of Turing isn't that important (and I'd somewhat agree), you can still use the concept to look at other things. Imagine asking a mathematician 5 years ago the chances of a Erdos problem being solved by a computer end to end. Or a millennium prize. Or ask a swe if a repo could be generated by a computer from the input "write a mario style game", or any other examples of proven expertise.
I don't get this. How are people "betting" on something that is "known information" for other people? What's the point? I get betting on stuff that no one can know, but who's taking "the other side" on these bets? Why would you put money into something knowing that there are people who already have the absolute answer?
That's an interesting choice. One question I had when looking at the jev copy on their blog is if one "line" in their output looks / attends to other lines. I think not, since they say it's parallel and not autoregressive. In that regard, it would be interesting to play with diffusion, and see if you'd get better results by playing with types, locking some, and so on.
There's also a cheeky "one of the models hallucinated a link" in the wiki jump example that most likely could have been avoided by properly using grammars. You can setup constrained gen so that only valid options (say from a list) can be outputted. Their own inference lib likely does that. So comparing to one that doesn't is a bit cheeky.
That being said, after a brief look at the site I could see this working. Especially if this can be ran locally, the speed and cost can enable some workflows where you have this as an "overseer" layer over say a cli agent. After each step you run through a list of "questions" ("is the task completed?" -> yes -> "does the edit touch files it shouldn't" / "does the edit follow our code writing policies") etc.
edit: extra points if the "question" rubric is also generated by a higher abstraction model. Say "/goal Build out auth" -> generate_rubrics(goal) -> "Is auth implemented on all endpoints" / "Has code touched anything else than auth" / "is this following the best practices" / ...
People have been saying this for at least 2 years now.
> token prices are skyrocketing
Today's SotA (fable and astra @ 50$ /Mtok output) are cheaper than o1-preview (sept '24, 60$ /Mtok output).
And other models are workhorses, with much better capabilities, are at least 1 oom cheaper today than o1-preview. (I'm using this model, since it was the first "thinking" model)
> it feels impossible for this approach to do something like
The models have just provided lean proofs for FLT (a ~1M$ project that was expected to take a human expert ~5 years to complete) and a Millennium prize problem. These are current, relevant, and previously unsolved problems. The obsession people have with "proving relativity from stone-age data" is just moving the goalposts.
> how poorly the cutting edge models do with being concise
LLMs solve a Millennium prize problem. People complain the proof is too long, within a week. What a time to be alive!
I've used this example before, but consider the purposeful downgrading on AI engineering in SotA models. Imagine MS being able to detect and deny you working on competing software, using Windows / VisualStudio. We would be up in arms, and they'd be split in a second. But top labs doing it is somehow good?
People have been saying this for 3 years now. Eppur si muove...
There are a few yt content creators that cover great engine games, if you're curious.