The best way is to measure temps before (ideally over at least 1-2 hours), then to measure temps the same amount of time after and compare.
In the case of this Rb oscillator, it is temperature sensitive, but also built for pretty extreme environments.
I'm not too worried about this particular application, because the clock will be in an air conditioned environment, and I'm monitoring the clock accuracy to +/- 2ns from another GPSDO / rubidium clock. The performance before and after the fan swap is within my measurement noise for 24H, though I haven't done 30+ day measurements and ADEV comparison. Still working on a consistent test setup.
The GRClok datasheet[0] gives its operating range as -10 to 55 °C (14-131 °F). The integrated Calnex unit only says it should be kept at +/- 1 °C for rated stability, but otherwise it just mentions 25 °C.[1]
Considering the new airflow rating is 9.4 m^3/h (vs 16.8[2]), yes it sounds like plenty of headroom.
> You can just look at the airflow charts and buy accordingly?
Not with Noctua and the likes, not always at least. These fans are quiet, but they are not made of magic fairy dust. They simply max out at lower flow rates than some of the the noisy, high-RPM fans people replace with them. Those 40 mm Noctuas, apart from voltage options, don't really come in many varieties.
It's a trade-off - you definitely lose the upper end of the device's original temperature range, but if it's stable under your conditions - great. How do you find out? Well, you replace the fans and check if it sizzles or not.
And funnily enough, a LOT of equipment these days isn't made with formal thermal design, they simply throw in high-cfm fans and... check if it sizzles. It usually doesn't because there's plenty of headroom. It's loud, but who cares since it's not meant to sit on your bench.
Source: replaced PSU and chassis fans in countless pieces of network and test & measurement equipment.
It is, and always has been, data contracts. Which is why the hyperscalars are a more likely candidate to take American frontier business than Chinese vendors.
I can click a button in Azure foundry and run open weight models that are approved through my compliance and procurement teams.
I don’t know how open sourcing this stuff helps deepseek but it means I’m getting ever better and cheaper models so I’m fine with it.
Well, whatever real or perceived moat Openai/anthropic had. OP claims this open source inference engine reduces their chances of high valuation, I'm assuming this is due to their in house engine being something they could use as differentiation before?
That is probably what has been the most fascinating part of this AI/LLM boom, the moat was apparently never the technology. It has been very much like nuclear energi (and weapons), once you know how to do it, it's not that difficult. It's still fairly hard, but you can do it. The moat has been the funding for the infrastructure and if that dries up you're in trouble.
I've been trying to find an answer to that, too. The best I've got right now is this:
Moat-like things going for them:
- access to the US government (for huge inflated government contracts as a source of free capital, having the competition banned, having their products legally mandated, getting other preferential treatment, having foreign governments bullied into granting similar advantages)
Too unsustainable, too fragile, too small:
- Strong brands: Brand loyalty seems like a pretty insignificant factor in AI right now
- Semi-exclusive hardware access, RAM buyouts: Unsustainable even in the short term
- Lock-in effects: Their models aren't very stable across versions, or very unique, and switching providers is about as easy as it can be in a highly competitive market.
- Exclusive technology that is important, advanced and hard to replicate to a degree that would materially set them apart and justify potentially much higher cost. Maybe I'm missing something here, but a lot of the claims they make to that end fall apart even on light scrutiny (due to things like test sets that are small, hand-picked and clearly overfitted on, or broad general claims that cannot be substantiated). I'm getting the feeling they don't have anything that materially sets them apart at all, but that's seems impossible to measure in any meaningful way, so who knows, they might have something here.
I think the early models distorted the perception of what artists actually need. It's not about workflows. Image creation is inherently iterative. None of the platforms seem to understand that, while AI artists have somehow fallen in love with the unnecessary complexity of node-based tools. Apparently, 400 tangled noodles look impressive when the boss peers over your shoulder.
The big studios and agencies are all building custom tooling as they always have but now a lot faster.
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