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Discussion (17 Comments)Read Original on HackerNews
Nebari is officially listed as a JATIC product as part of the next-gen toolchain supporting DoD AI development.
Are we officially ~one degree of Kevin Bacon from the DoD endorsing running Chinese OSS models because they're self-hosted and we're all too dumb to tell the difference?
https://openteams.com/open-source-isnt-the-real-risk-in-nati...
For my style of coding (quick back-and-forths and corrections) it makes a big difference if a model comes back in 1-2 minutes compared to 5-10, and I am happy to pay a bit extra for that.
there's a fun Excel artifact where it auto-selects the 'relevant' range with no adjustment for how proportionally close to 0 the values are - a professional researcher publishing to a journal should know better (and should be ridiculed for not incorporating best practices) but for a personal blog by an SWE this really isn't the worst sin
[0] https://digitalblog.ons.gov.uk/2016/06/27/does-the-axis-have...
Just look at the first chart: the distance between Fable 5.1 and Sol is <5%, but it looks like 25 or 30%.
If you own a graphics card you bought for gaming or a laptop you bought for doing schoolwork there is $0 in cost of local AI tokens, because 100% of the cost was assigned to doing other things.
> The first issue I have with it is that it uses a logarithmic scale on the cost axis. Using a log scale is the only way to make you spot the difference between a model that costs $0.015 per task and one that costs $0.032, while the same plot contains a model that costs $3.69 — almost 250 times as expensive. However, the net result is that the viewers can no longer appreciate the immensity of the price difference between the cheap models and the heavy ones; nor can they realize how inconsequential the price differences are between the cheap models.
This is an asinine complaint, and nobody can seriously tell me that the last plot on their page [0] is more readable than the AA one [1]. If I'm using a model at the lower range of the cost scale for whatever list of tasks, and i switch to another model at the lower end of the cost scale, my spending might double anyways! This should be reflected in the plot, and linear scale doesn't do it justice.
It's also much easier to see the mentioned pareto frontier in the log plot than in the linear one.
I can see why they disagree with the pricing determination for open/local models, but I don't think there is one clear right way to do it. So how do they do it instead?
>Hardware is priced at zero, on the basis that both an RTX 3090 PC and a 64GB Strix Halo are desirable gaming/work machines anyways.
...oh
Would have been nice to mention explicitly how the pareto frontier changes with those new calculations.
[0] https://openteams.com/wp-content/uploads/2026/09/all_models-... [1] https://artificialanalysis.ai/#intelligence-comparison-tabs
What will happen is that this will be the third consultancy with a lofty narrative after Enthought and Anaconda that Oliphant established. It is always bait-and-switch.
Another thing is if you're using the subscriptions with OpenAI or Anthropic you get an order of magnitude discount relative to the per-token price. So you need to move their models ~10x to the left on the plots to get a fair comparison.