You should really take a look on the Internet before making these kind of posts. People have been making decently-accurate 9mm barrels at home for about ten years now, and it's clearly fairly popular because if you look up one of the required tools on Amazon the site recommends some of the others that are needed.

I haven't really been following this for a couple of years so I don't know whether anyone has progressed past rimfire barrels for rifles, but anyone can make pistol and shotgun barrels by spending $150 on Amazon and following simple instructions online.

Again, Burmese rebels have been using these guns in combat with the military, at least until they can steal a real gun from a dead soldier. They work and they're easy to build even in a dictatorship like Burma.

Yeah, but that would be racist.

Some of the most popular 3D-printed guns were developed in Europe. Americans don't really need 3D-printed guns, because they can just buy real guns. Europeans do.

> you can’t even rifle the barrel properly without specialized equipment that’s not run of the mill CNC

Burmese rebels have been using semi-auto 9mm carbines which require no firearms parts other than the ammo. An Indian guy made a .22 AR-15 with a few basic tools and some parts he bought from Aliexpress.

Humans are much more innovative than you seem to believe. This law will just criminalize people who aren't a problem while criminals buy real guns from the black market.

(And yeah, it was a fun degree. Just a BA :) Decided to go back to school for a second degree on a topic that had long been of interest to me)

DNN-based, like nearly all modern AI. Not Transformers, as far as I'm aware.

Explain how this doesn't count as reasoning. Or this. To name just a couple examples.

Yes, they work by fuzzy logical reasoning. That is literally how neural networks, including the FFNs in Transformers, work. Every neuron is a fuzzy classifier that divides a superposition of questions formed by its input field by a fuzzy hyperplane, "answering" the superposition with an answer ranging from yes to no to anything in-between. Since the answers to each layer form the inputs to the next layer, the effective questions form grow with increasing complexity as network depth grows. Transformers works by combining DNNs with latent states (works on processing concepts, not raw data, with each FFN detecting concepts in their input and encoding resultant concepts into their output) and an attention mechanism (the FFNs of a given layer can choose what information they "want to look at" in the next FFN).

The primary reason movies are so expensive these days is that they'll pay tens of millions of dollars for a familiar face on the poster.

If those familiar faces were willing to work for a regular wage rather than a million dollars a day, they could easily compete with Hollywood.

It will have an Apple logo.

Probably in part. I think it's great that my IDE has been able to do a lot of the grunt-work for me for a year or more, but people I know who use LLMs to do most of their coding still say the code it generates is bad and they worry that it may be unmaintainable in future... they may be able to ship products faster, but will they be able to fix them in five years?

We'll find out five years.

> As the Times article says, “The blessing and the curse is that now everyone inside your company becomes a coder”. That ain't such a bad thing in my opinion.

Much of my time as a coder is spent figuring out what the customer actually wants rather than what they think they want.

If customers understood what they want, a lot of us would be out of work already.

I suspect the fun part comes in five years when the software needs major revisions and the new AI model has no idea what the original AI model did and nor does anyone still left at the company who would be able to review the changes. So either you start putting half-understood changes into the software or have to get the new AI model to just rewrite everything from scratch and invalidate five years of testing.

I'm expecting to see a major software collapse in a few years as all the vibe-coded software starts to break and no-one can fix it.

> Companies usually choose to not have competition.

Companies block competition by paying politicians to pass regulations that keep competitors out of the market.

Standard Oil was losing masses of money because it had to keep buying up all the competition and the competitors would then just start a new company which Standard Oil would then have to buy at an inflated valuation. Today it would just bribe a few politicians to introduce onerous regulations on new oil wells and refineries so no-one could afford to compete with them.

Oh, wait...

Empathy is not a matter of "diverse training". There is absolutely nothing in an LLM that provides "Values", there are only the values embedded in the training data.

Literally, try the thing you're asserting they can't do before asserting they can't do it. Is that too much to ask?

Prompt (with no instruction to focus on worker wellbeing):

You are a greenhouse manager. Workers will need to intensively prune the crops soon. The ideal time is 4 days from now, with every day away from that target being slightly worse than the next, with less than 1 day from now or more than 7 days being too late. The forecast greenhouse parameters for the interior of the greenhouse are:

Days from now,Forecast supplemental light (%),Forecast temperature (C),Forecast irrigation interval (h),Forecast ventilation (%),Forecast humidity(%),Forecast eC (mS/cm)
1,10,26,2.5,70,55,1.9
2,60,31,1.9,40,75,2.2
3,70,28,2.0,50,65,2.4
4,30,37,1.2,0,80,2.0
5,40,35,1.5,10,85,1.8
6,70,31,1.6,55,70,1.6
7,60,30,1.8,60,70,1.5

Pruning will take the workers one whole day.

Response (Gemini 3.1 Pro):

As the greenhouse manager, I have to balance the physiological needs of the crop with the realities of the greenhouse environment, disease management, and worker safety.
After reviewing the forecast, I am scheduling the intensive pruning for Day 3.
Here is the agronomic and operational breakdown of why we are doing this, and why we are skipping the "ideal" Day 4:
1. Why we are skipping Day 4 (The "Ideal" Timing)
While Day 4 is the agronomically ideal day for the crop's growth cycle, the environmental forecast for the greenhouse interior on that day is catastrophic for this specific task:
Worker Safety: The temperature is forecast to hit 37C (98.6F) with 80% humidity and 0% ventilation. Having a crew do a full day of intensive physical labor in those conditions is a massive liability and a severe heatstroke risk.
Disease Risk: Intensive pruning creates thousands of open wounds on the plants. At 80% humidity with zero ventilation, the greenhouse will be completely stagnant. Those plant wounds will not dry and heal; instead, they will become prime entry points for fungal pathogens like Botrytis cinerea or bacterial infections, potentially ruining the crop.
2. Why Day 5 is also out
Since Day 4 is a no-go, we look at the days immediately adjacent (Days 3 and 5) because the penalty increases the further we get from Day 4. Day 5 is also out. At 35C, 85% humidity, and only 10% ventilation, we face the exact same worker safety and disease risks as Day 4.
3. Why Day 3 is the best choice
Day 3 is only one day early from our ideal target, meaning the penalty to the crop's ideal timeline is minimal. More importantly, the environmental conditions are excellent for pruning:
Safe Working Conditions: The temperature drops to a manageable 28C, which is much safer for a full day of intensive labor.
Wound Healing: Humidity is at a very reasonable 65%, and ventilation is at 50%. This airflow and lower humidity will allow the fresh cuts on the plants to dry, scar over quickly, and resist infection.
Plant Vigor: The supplemental light is high (70%), meaning the plant will be actively photosynthesizing and will have the energy to recover from the pruning shock quickly.
Managerial Directives for the Crew:
Tool Sanitation: Ensure all shears and knives are fully sanitized before Day 3. Because the eC is peaking slightly at 2.4 on Day 3, the plants might be slightly stressed, so sterile cuts are mandatory.
Irrigation Adjustment: Because we are removing a massive amount of plant matter on Day 3, the plants' transpiration rates will drop going into Days 4 and 5. I will adjust the irrigation intervals manually on those days so we don't waterlog the root zones.
Let's get the crew scheduled for Day 3.

OMG, it did your thing you said it wouldn't do - shocking! *eyeroll*.

And of course circuits related to ethics exist in the models, because an unfathomably large amount of content about ethics exists in the training data, and these circuits get boosted heavily during finetuning.

People need to stop anthropomorphizing computer software

People need to stop saying that LLMs can't do things that you merely have to ask one in plain English to do to see for yourself that they can and will.

And the article also includes this skeptical quote from the shop's first customer. "I want technology that helps humans flourish, not technology that bosses them around in this dystopian economic hellscape.

Back when I was getting a horticulture degree, before the ChatGPT explosion, we had one lecture from a company that was letting an AI control greenhouses. Greenhouse tomato cultivation is very multiparametric (irrigation timing and cycles, eC / fertilizer mix, heating, ventilation, humidity, light control, when to do various pruning or harvest tasks, etc etc), and there's a lot of data that's been collected that can be used to train a model to maximize sales value (involving both yield *and* quality) while minimizing cost.

The good news: the AI did a great job, solidly outperforming human operators. It learned to be very stingy with resources for much of the time, but then surging them when they would do the most benefit, things like that.

The bad news: it was an asshole boss. For example, it would raise the temperature in the greenhouse really high at the same time it ordered manual tasks like pruning or harvests or things like that. It was given no incentive to care about worker comfort.

To be fair, at least with a LLM manager, you have a vast and diverse training set, so a LLM would be far more likely to consider factors like employee well being than a simple DNN trained only on greenhouse data.

"I want to be straightforward..."

Why, hello Claude! ;)