The post hits it spot on with unequal access to the models in terms of security. I'm developing OSS where security is important for the user ... but the frontier models like GPT 5.6 and Fable flake out and state that I cannot get the info/access.
This is extremely lopsided I'll have to resort to GLM 5.2/K3 to ensure that those security issues (hopefully) are resolved properly.
For OSS, this is one of the most counterintuitive experiences I have ever had. More than ever I'm convinced that open weight and open pipelines models are 100% critical for progress on the AI and societal fronts.
It's the right direction, but control flow introduces limitations within a system that is quite adaptable to dynamic situations. The more control flow you try to do, the more buggy edge cases that pop up if done poorly.
Still have yet to see a universal treatment that tackles this well.
John is not only smart and knowledgeable, but an incredibly great person to know in general. I worked with him on a project briefly back in 2012 and he stood out as a champion for science, coding, and education. His posts clearly reflect him well.
Absolutely love these type of keyboards. But ... with how much security I work with for logins, etc, the fingerprint button on my Mac keyboards are amazing time savers that I don't want to live without. Has anyone found a workaround?
What I find odd is that definition of "investor" is not that clear. When you click through the links you get blocked at the data provider with no context. There's also a link to another post by the same news provider. When clicking through reference to the data source, the link doesn't work.
It would need to be more than that. A prompt for one model can have different results vs another. Even when the model has different treatment for inference, eg quantization, the same prompt for the unquantized and quantized model could differ.
Regarding the energy of lifting a 10KG weight 2 KM high is not a 1:1 comparison with the tech here. It's using an LED which much more efficient than a Kerosene powered flame, since most of the energy in the flame is spent in infrared.
Seaborn is my favorite statistical plotting package in Python. I wrote an astro plotting package that digs deep into the Matplotlib internals and it was not easy. Big props to the developer behind Seaborn and the great aesthetics he imbued it with.
You're correct, the likelihood of stars colliding is near zero, but the gas that forms stars in both galaxies will collide and that will create quite a spectacular view. It will likely resemble something like this: http://hugepic.io/bfc195a2b/4.00/2.02/-77.61
Your question rang with mine. Why is this on HN? Is there a policy about the content that posts should contain on HN? In the past 6 months it seems like much more non-code/hack/start up material is posted on HN and that's a bit concerning.
My dad was part of a company in Boston that only had employees with an IQ of 140 and up. I asked him how it went and he laughed and said it naturally fell into ruins. Key point: there's a lot more to employees than just quantifiable numbers like GPA, IQ and such.
This depends on which field of study you're in. If you want to go to grad school for astrophysics, getting that high GPA score is important. But, I do agree that halving your time to get a B+ versus an A is good. This type of decision making shows that you're good at managing your time and setting your priorities right.
[Authorea consultant here] This feature is quite exciting. Using JS based plotting capabilities like D3.js will allow users to do two things at once.
(1) Provide dynamic figures to represent data that is normally ineffective in static form and (2) provide the code and data that was used to create it. This helps others reproduce the results from the data to figure form, which is a feature that is definitely missing in PDF publications.
Awesome! The only issue is that the visualization makes it look like we are in a cluster of stars. That is not correct. We're part of the diffuse field star population in the Milky Way.