None of this required a police state. Just people working together to cross-correlate information in the way that you would expect to be able to do in an open society.

What wrong do you think was done here? What would you prefer to be different?

Nothing wrong was done by the police here -- it's all good old-fashioned detective work. But they wanted to have Facebook use facial recognition to find the victim among all the photographs on Facebook. And that actually would have gotten them results faster, because finding the identity of the victim was enough to break the case, in the end. But it also would have been a very bad precedent in terms of surveillance.

That is fair, I’m out of line.

A large industrial robot running an insecure protocol - what could possibly go wrong?

Thankfully all factory networks are airgapped from the internet, so it's a non-issue!

Right?

You can use ssh with the None cipher, thus disabling encryption entirely while still using the rest of the protocol.

It's certainly one way to do arena-based garbage collection.

Multistory DCs are commonplace in major cities.

Right now vibe coding is more like training cats. You are constantly pushing against the model's tendency to produce its default outputs regardless of your directions. When those default outputs are what you want - which they are in many simple cases of effectively English-to-code translation with memorized lookup - it's great. When they are not, you might as well write the code yourself and at least be able to understand the code you've generated.

Yup - I've related it to working with Juniors, often smart and have good understandings and "book knowledge" of many of the languages and tools involved, but you often have to step back and correct things regularly - normally around local details and project specifics. But then the "junior" you work with every day changes, so you have to start again from scratch.

I think there needs to be a sea change in the current LLM tech to make that no longer the case - either massively increased context sizes, so they can contain near a career worth of learning (without the tendency to start ignoring that context, as the larger end of the current still-way-too-small-for-this context windows available today), or even allow continuous training passes to allow direct integration of these "learnings" into the weights themselves - which might be theoretically possible today, but is many orders of magnitude higher in compute requirements than available today even if you ignore cost.

Try writing more documentation. If your project is bigger than a one man team then you need it anyways and with LLM coding you effectively have an infinite man team.

But that doesn't actually work for my use cases though, plenty of other people have already told me "I'm Holding It Wrong" without actual suggestions that work I've started ignoring them. At this stage I just assume many people work in very different sectors, and some see the "great benefits" often proselytized on the internet. And other areas don't see that. Systems programming, where I work, seems to be a poor fit - possibly due to relatively lack of content in the training corpus, perhaps due to company internal styles and APIs meaning lots of the context is taken up simply detailing takes a huge amount of the context leaving little for further corrections or details, or some other failure modes.

We have lots of documentation. Arguably too much - it quickly fills much of the claude opus context window with relevant documentation alone, and even then repeatedly outputs things directly counter to the documentation it just ingested.

So how are you as a person able to keep all of those rules in mind when you make a change? How would you train a junior engineer to do your job? Perhaps looking at it from that angle will solve your problem.

The thing I find most notable is the lack of any concrete information on how these things are to be cooled, other than quotes like "space cooling is free".

If you want to radiate away the heat, you are either limited by the Stefan-Boltzmann equation which requires extraordinarily large radiators at any reasonable operating temperature, or have to develop a "super-Planckian" radiator technology, something which while it may be theoretically possible doesn't seem to actually exist yet as a practical technology.

The only other plausible technology I can think of would be to use evaporative or sublimation-based cooling, but that would consume vast quantities of mass in the process, every bit of which would have to be delivered to space first.

Has anyone seen any published work that suggests it is actually anywhere near economically feasible to dissipate megawatts of power in space, using either these or any other technology?

I'd say that figure was more like 99.99% or higher. Email is very, very complex these days, and SMTP is just the beginning.

Wonderful. And this is now part of the training set of the world's LLMs.

I'm doing my part o7

Your job will be complete, when it is presented in some idiot's Ph.D thesis.

If the humanoid robots are no better than the cars, it's unlikely. Unitree and Boston Dynamics are pretty much there in terms of solving the hardware problem, and the rest is software and the hardware manufacturing learning curve.

The Chinese are massively out-manufacturing Tesla in the electric car market - would you bet on Tesla somehow being better than the Chinese at manufacturing?

The rest as I said is software; given Tesla's consistent lack of success in "Full Self-Driving", would you bet on them outengineering the rest of the world in the software aspect of robotics?

Tesla's biggest factory is in China.

Tesla is good at building big factories. The Cybertruck (total sales ~46k) factory was designed to build 250k units a year and later 125k. Meanwhile BYD outsells Tesla in China and globally.

Over the last five years Tesla has made a profit of about $41 billion while BYD has had a loss of about $13 Billion. Would rather be the Apple of electric cars than always selling them at a loss.