Of the changes made last week to the license, this one stands out:
- None of the Work may be used in any form as part, or whole, of an integration, plugin or app that integrates with Atlassian’s Confluence or Jira products.
That is a weird carve-out, so I’d guess the license revision (and technically the reason it’s no longer open source) somehow has to do with Atlassian or their plugin marketplace?
Feel like the (totally impractical) fediverse end-game would be for each individual to have their own activitypub service, and federation happening on a person-by-person basis. So you retain some control over anything you publish, and your history is yours to keep.
As others have said, changing UPS batteries is required maintenance, and I agree 18-24 months is the typical service life for even high-end UPSs. However, you may want to look into LiFePO4 based UPSs, which can handle many more charge-discharge cycles and often have 5-year warranties. More expensive and potentially not as recyclable as lead acid batteries, but maybe appropriate for your use case.
Between this and the Bulletproof craze a few years ago we can almost get a whole breakfast in one cup!
Cat software running on dog hardware.
(As opposed to a cheetah, which is dog software running on cat hardware)
I heard someone won an award for the fjords…
This is so terrible it physically pains me.
Are you looking for a tool that can diff legal documents line by line or clause by clause? If the latter I’d bet an LLM with a large context size could do a pretty good job, especially if you used a script (or another pass through the LLM) to break them down into like sections so that could just compare e.g. all Controlling Law sections with each other and all IP Indemnification sections with each other.
Now that I think about it, tuning the prompt (and keeping the temperature very low, like 0) you could probably get it to return everything from proper diffs to summaries of conceptual differences. And it could definitely do multiples at once if you were to break them into like pieces ahead of time.
“Aerospace chocolatiers” would be a great name for an experimental, new-age music troupe.
The interesting part of the article:
The new flexible polymer and carbon composite boom is coupled with a twelve-unit (12U) CubeSat built by NanoAvionics. After the mission launches atop a Rocket Lab Electron rocket from the company’s Launch Complex 1 in Māhia, New Zealand, the spacecraft will go into a Sun-synchronous orbit at an altitude of about 600 miles (~1,000 km) and the sail will deploy in about 25 minutes to cover an area of 860 ft² (80 m²) with the boom unfolding from the size of a hand to 23 ft (7 m) long. Once deployed, the sail will adjust the vehicle’s orbit by angling itself in relation to the solar wind.
Wow. How do they fit so much smart into such a tiny space?
Ugh, I forgot what April fools is like on the internet.
It sounds like a cortado, though that is usually an equal volume of steamed milk, not frothed. Those have been popular in Spain for a long time, though that doesn’t really answer your question about Buenos Aires.
I think you forgot to add the link: Here’s a no-paywall version.
Who is your PM or senior assigning the tasks? You need to take this up with them – everyone always needs a couple of quick hits in their back pocket. When you stall out grinding on task after impossible task it kills your motivation and productivity, and that’s your boss’s job to fix.
One thing I never thought about is how the longer a scientist is out of work (due to war, political instability, etc) the more likely it is that they’ll be lost to science (in other words, the less likely they are to return to scientific work when they can). That could potentially be like a chilling effect on steroids.
Fair enough. ML ⊆ AI then. But these days when everyone talking breathlessly about AI taking away jobs they’re almost always taking about LLMs. This article is about ML in particular which is a different discipline with different applications.
ML =/= AI. There are legit uses for ML that don’t have anything to do with LLMs and the cloud. I worked on an ML project 3 or 4 years ago to listen for fan noise that might indicate that it was about to fail soon. We trained a tiny GAN on good and bad noises. It runs on a tiny CPU, locally. Highly specialized work, and I have to imagine there are and will continue to be lots of similar opportunities to bring efficiencies by getting computers to make good observations and decisions - even if only about “simple” things like “does this thing seem like it’s about to break?”
The duck’s name was also the inspiration for the blaster’s iconic sound