Nope. No such thing when I paid to have that button first time around.

I don't use companies that try to monetise my use of them, especially if I'm paying for their service.

On that note...

Why the fuck am I seeing huge ads on Slashdot now for "bolt.new" and other shite when I paid many years ago to "Disable Advertising"?

Depends. Configurable.
Decoding strategies vary from stochastic to greedy (non).
Configurable per generated token (though usually just per prompt)

Skills have a meaning in the LLMiverse- and it does mean that prompt. So this skill is a skill.
What you are calling a skill is a script produced by an LLM (possibly a skill fed to an LLM), but that is not a skill.
Saving scripts is less useful than you think. The LLM will not, unless asked, try to produce something that works generically. It will create very fine-tuned things to solve the problem based on the example dataset it's got in front of it.
If you want to save off and run scripts, you want plugins.

Yup. Prompt injection is basically an un-fixable problem.
Hope they've got they've got their LLM tools well-sandboxed off from the rest of the browser.

Now all you have to do is figure out how to make solder joints that work at that temperature.

Yeah I know there are metals that would be candidates for that, but the fabrication process will be exotic, to say the least.

(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).

Largely nonsense.

They are defaulting to PREEMPT_NONE but nothing to stop you changing that.

Newer versions of PostgreSQL already avoid certain spinlocks that hit this performance issue which is, as stated, doing something dumb in PostgreSQL which kernels can't have insight into, and which the new kernel defaults now penalise.

And nobody, with a brain, would just be running anything critical on the very latest kernel without testing it first.

By the time 7.0.1 comes out, PostgreSQL will have sorted it and nobody will ever remember it was ever a problem.

If he thinks he can, then he should.

Because then we can just ignore him, put an unpaid AI in his place.

Let's do this to ALL the billionaires.

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! ;)

There is no shortage of fabs, no shortage of RAM. There is a shortage of RAM available for purchase at reasonable prices. That's a very different thing.

All it requires is 3 manufacturers deciding, jointly, that the price should rise. They have a documented history of doing this for no reason. But when you give them an excuse - like OpenAI has - to expect anything else would be very strange.

I would prefer an honest Web 1.0 site, but it's not that either.