Mordikan

I use VLANs for this as others have mentioned. You can create VLANs for the logical grouping of devices (ex. Home, Work, IoT, etc), then you create firewall rules to block specific traffic sourced from the IoT VLAN to the other VLANs or out the WAN interface. This still allows traffic originating from the Home/Work VLANs to talk to the printer but doesn’t let devices from that VLAN scan the network.

Their build command references INSTALL.md which is directions for an LLM to install dependencies and setup the environment.

Can you add the [AIP] tag to this?

I’m a vegan. Just thought I’d let you know.

Those 4 points for AI code writing are extremely well thought out. I can really respect that.

“It starts with people hold their governments and representatives accountable”.

That is basically a non-answer and doesn’t mean anything. The reality is AI is classified a national security priority and therefore has the US govt to prevent it from running out of money.

You could argue that the US has already lost the AI race to China, though. The race isn’t won by building the gigantic LLMs. It isn’t about 1 million token context windows or 1 trillion parameter models. It’s about sentiment. Deepseek and the other distilled models take a novel approach with MoE and strict use case training, but ultimately its the willingness of people/companies to use the AI that results in the win.

US adoption is still the highest world-wide, but China is starting to take the lead on that and have they have stronger willingness to use it.

AI isn’t anywhere close to being a pump and dump scheme as you speculate.

The reality is it would be closer to the dot com bubble (if anything at all). Most of the damage would be in physical infrastructure like datacenters which would be an asset write-off. However, there is tangible profitability in AI which we didn’t see in dot com. That would imply if the demand does not exist (which we know it does looking at DevOps, BI, etc) that should things go south, it would be a market correction and not a market collapse.

Again, the weak point and therefore the most hurt would be overvalued segments which is basically the datacenter infrastructure, but that doesn’t imply a collapse/pop only a downturn and correction.

There is so much AI hype and so much anti-AI hype, that I doubt it really will.

For the last 4 years, we have perpetually been 90 days from both the AI market crashing and AI stealing all our jobs at the same time.

My thought is the AI market won’t crash because especially in data engineering and devops there is an inherent worth. I think there will be a potential downturn in the volume of cash flowing into perhaps after the big players like OpenAI and Anthropic go to IPO, but the market itself will remain stable. Similar to how the housing bubble crash didn’t actually wipe out the housing market (with or without the US govt saving the big players - it still would have survived on its own even if those players did not).

Multiple people mentioned there are on the Epstein list. They probably misheard “kitty exploit” as “kiddy exploit”.

You already ruled out Tailscale and the internal network and potentially the route taken to each your router. Does your router run any services that perform IDS/IPS maybe? Any sort of packet filtering on the external interface?

Saw it was already commented about CO2, so I thought I’d counter-point your environment claim regarding water usage (since that is something I’ve seen a lot of too).

The ISSA had a call to action due to the AI water use “crisis”: https://www.issa.com/industry-news/ai-data-center-water-consumption-is-creating-an-unprecedented-crisis-in-the-united-states/

68 billion gallons of water by 2028! That’s a lot…right? Well, what I found is that this is somewhat of a bad faith argument. 68 billion gallons annually is a lot for one town, but those are numbers from a national level and it isn’t compared to usage from anything else. So, lets look at US agriculture (that’s something that’s tracked very well by the USDA): https://www.nass.usda.gov/Publications/Highlights/2024/Census22_HL_Irrigation_4.pdf

That’s 26.4 trillion gallons of water annually. So, AI datacenter represents 0.26% of agriculture consumption. If AI datacenter consumption is a crisis, why is agriculture consumption not a crisis? You could argue that agriculture produces “something useful”, but usefulness doesn’t factor into the scarcity of a resource. So, either its not a crisis, or you are cherry picking something that has no meaningful outcome to solving the problem.