491–500 of 513 posts
This is kind of trivially satisfied and they make this sound more extraordinary than it is. Also I suppose it can still hallucinate in the sense that for out-of-distribution data it will give miscalibrated probabilities.
Anyway a great step in the direction of calibrated AGI
Spent a lot of time - but this makes zero sense to me. It can, maybe, return type safe outputs faster than larger llms - but there is little reason to believe that it will be more accurate. It does absolutely hallucinate - and seems to me that the claim is largely misleading. You architect your systems with typesafe - because it is marginally faster, but inaccurate - to do what ? You can just wait for the next versio…
Wasn't really till seeing this home assistant demo they have ( https://www.loom.com/share/18c4dbcf8db546dfb2d7f2ef018e78e4 ) that the value really clicked for me. Seems really cool.
This is very cool. However I don’t really want to bounce all my home automation commands to the cloud. I hope there will be an open weights approach one day. I’ve spent a lot of time setting up my local only home automation system, it would suck if it didn’t work during an internet outage, and also there are obvious privacy problems.
I feel like this whole scenario could be solved by a very ugly switch case generated by deepseek for 0.02ct.
But is it just me or other folks feel uncanny seeing "a machine" playing Doom with low-latency as the first demo.
While I'm optimistic that humanity is good (but sometimes makes bad decisions), My first thought was seeing such models used by armies.
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The inputs aren't natural language. https://docs.typesafe.ai/primitives
The inputs are natural language, they're just also structured into a tree. The first example on that very page shows natural language instructions: questions = { "refund_requested": Noul( instructions="Does the customer request a refund?", ), }
First, congrats to the team on launching something genuinely interesting and new. Seems like a more accurate title would be "Jev: Trading general purpose generation for fast typed inference" or something like that. This is interesting, but the speed comparison seems misleading? A generative model that can output code in a Turing-complete language can do anything a computer can do. Jev can only generate structured out…
I don’t think it’s misleading if you compare on the use cases they suggested. It’s faster and cheaper (no idea if higher quality), so it’s immediately interesting for certain things. And if you buy their RLCD claims, this might be even better than huge models that know a bunch of irrelevant things.
It's therefore misleading because they make people believe they're as general purpose as LLMs.
My guess is that due to its design there is no support for prompt caching, as there would normally be no reason for it. So the performance of my idea would probably be appalling as every step in the loop would reevaluate every input and output token. But it would be interesting to see the outcome.
I wonder how many choices you can give this thing in multiple choice response mode. I'm guessing you could give it enough choices to produce Turing complete programs one symbol at a time when running in a loop, or to hold a conversation when given a vocabulary as the choices. It would be particularly hilarious to just let it choose the next ASCII character of output in a loop. My guess is that due to its design there…
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When they say "can't hallucinate" they mean they produce a confidence value for every result, so you could see for example it has 0.1 confidence, and you can disregard the result - that'd be different from hallucinating where it believes it's correct
that's right, but because these models are probabilistic, it's also possible to be confidently wrong (and all future models will be smarter still and still have that possibility)
"What are the odds a thunder will strike in Paris at 1pm UTC of 2026-09-16" - that could be a 0.001 chance going from blind historical measurements; 0.01 if it's raining; or 1 or 1 after the date has passed.