Easy to do, easy to implement but hard to bypass.
Also it tells me something about the network that is not vying for a slice of the attention economy and isn't going to do everything it can to keep me on the site.
Don't underestimate the stubbornness of "get rich easy" people when it comes down to cheating etc. Even if it's not easy or cost effective, if this was going to be actually viral, they would tap real phones in click-farms to game the system. And do it once a year.
It's true that there are people who pay a premium for thinking they got one up on you, and will waste $1000 of effort to get $100.
But it wouldn't actually work well. It doesn't even need physical invites, keeping track of the invite graph is a great way to kick scammers out. It works. It's been demonstrated to work well since at least 2004.
The reason social media sites don't do it is not that it doesn't work - it's that growth trumps those concerns. Making onboarding as easy as possible is more important than keeping scammers out.
Why "hard to bypass" would be a sufficient thing?
It depends on the technology used to connect the two phones. Bypassing this process can range from "easy" to "quite complicated", but it remains possible. Once the security is compromised, the entire network loses its core value since a single interaction is enough to establish a permanent connection.
This comment got me thinking that it might be worth using their second-to-last location to try and derive some vector. Obviously that's super informative as you already know the edge of the map they left, but maybe it's useful?
It's a good question, but in a closed system (like you have in space) the heat from the turbine loop has to go somewhere in order to make it useful. Let's say you have a coolant loop for the gpus (maybe glycol). You take the hot glycol, run it through your heat exchanger and heat up your cool, pressurized ammonia. The ammonia gets hot (and now the glycol is cool, send it back). You then take the ammonia and send it through the turbine and it evaporates as it expands and loses pressure to spin the turbine. But now what? You have warm, vaporized, low pressure ammonia, and now you need to cool it down to start over. Once it's cool you can pressurize it again so you can heat it up to use again, but you have to cool it, and that's the crux of the issue.
The problem is essentially that everything you do releases waste heat, so you either reject it, or everything continues to heat up until something breaks. Developing useful work from that heat only helps if it helps reject it, but it's more efficient to reject it immediately.
A better, more direct way to think about this might be to look at the Seebeck effect. If you have a giant radiator, you could put a Peltier module between it and you GPU cooling loop and generate a little electricity, but that would necessarily also create some waste heat, so you're better off cooling the GPU directly.
I think I get it. If we could convert 100% of the waste heat into useful power, then all good. And that would get interesting because it would effectively become "free" compute - you'd put enough power into the system to start it, and then it could continue running on its own waste heat. A perpetual motion machine but for computing.
But we can't do that, because physics. Everything we could do to generate useful energy from waste heat also generates some waste heat that cannot be captured by that same process. So there will always be some waste heat that can't be converted to useful energy, which needs to be ejected or it accumulates and everything melts.
So, in doing a bit of research from a link in one of the other comments, this is lcos, levelized cost of storage. I understand that to be roughly equivalent to the marginal cost of using it, including the capex divided over the unit volume. That same article uses $125/kwh as the capex, which is in line with your (and my) expectations of the cost to install.
$65/mwh works out to $0.065/kwh, so that makes sense. Effectively you can read this as "it costs $65/mwh to store and then consume electricity using these batteries"
You’re right, upon further review you can get budget Lifepo4 batteries shipped to your door from Amazon for as low as $75/kwh, which includes cables, a BMS, and various Bluetooth connectivity. So $65/kwh seems fairly reasonable for raw battery capacity in very large quantities.
But now it’s time to better understand why a Powerwall or other wall-mounted units are so much more expensive. I understand UL-listing costs, marketing, warranty, and other things are thrown in, but it’s $75/kwh versus $1000/kwh, a 13x difference.
If even at a $100/kwh price point all homeowners need to get 10-20kwh in batteries just to help peak shave the grid and save tons of money since batteries will be a fraction of the cost of grid power.
Oh man, I've been playing with GCP's vertex AI endpoints, and this is so representative of my experience. It's actually bananas how difficult it is, even compared to other GCP endpoints
If it matters to you, Reolink is a Chinese owned company. Not passing judgement one way or another, but if avoiding Unifi over the remote incident matters, I could see this factoring in as well.
Separate vlans for iot devices with strict firewall rules is generally enough to mitigate the threat of most iot devices phoning home i think. We’re already in the territory of hobbyists who should be able to manage that with these suggestions like ubiquity and frigate.
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Kharon | Individual Contributor and Data Engineering Leadership Opportunities | HYBRID in London or Madrid or On-site in Denver, CO | Full Time
Kharon is on a mission to revolutionize the current global security landscape. When you look at any major global crisis event, we’re providing intelligence that’s at the heart of those circumstances. We connect the dots in a way that’s meaningful, and we're currently growing our Data Engineering vertical.
Operating at the intersection of global security + international commerce
Engineering team tackling massive open-source data at scale
Stack: Python (Pandas, NumPy, FastAPI), SQL, Spark, Databricks, Neo4j, Elasticsearch, AWS, Docker, K8s
Nitpicking on the mechanic point, but this is pretty common, just not at the same level of detail as medicine. Certain brands, models, and parts are more likely to fail in certain ways, so if a model comes in with symptoms of a known, high frequency problem, many times that work will be done first rather than taking more of the car apart to inspect individual parts.
Certainly I didn't think there's huge bodies of work on those statistics the same way there is for medicine, but any car repair forum online will give you some sense of this
Easy to do, easy to implement but hard to bypass. Also it tells me something about the network that is not vying for a slice of the attention economy and isn't going to do everything it can to keep me on the site.
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