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It's more "you need to learn how a tool really works in order to use it most effectively" Talking to a chat bot like you'd talk to a human is a quick way to be disappointed. They don't work that way, despite sometimes sounding like they do.
But don't forget, "we don't know how it really works".
But the end result is the same.
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People are stupid, always have been - took thousands of years to accept brain as the seat of thought because “heart beat faster when excited, means heart is source of excitement”. Heck, people literally used to think eyes are the source of light since everything is dark when you close them. People are immensely, incredibly, unimaginably stupid. It has taken a lot of miracles put together to get us where we are now…bu…
> took thousands of years to accept brain as the seat of thought because “heart beat faster when excited, means heart is source of excitement” So what you are saying is that beings without a central nervous system cannot experience "excitement"? or perhaps the meaning of too many words has changed, and their context. When Hippocrates claimed that the brain was an organ to cool the blood, perhaps he meant that we use…
The belief that the heart was the seat of thought and emotion was shared by numerous cultures[0], and was based on their naive interpretation of physiology and biology and cannot be dismissed as a modern misinterpretation of a single vague aphorism by a single person due to the preponderance of documentary evidence to the contrary from contemporary sources. Also, you're probably talking about Aristotle, not Hippocrates.
>Maybe because we stopped talking about "excitable" people as being "hot-blooded"
Also people still say "hot blooded" all the time.
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No offense but your experience with AI is fairly primitive if that's where you're at.
Please elaborate. Share what you built and how you prompted and what you are making from it, and how many tokens you paid to rummage through.
I'm trying to build a software stack that can eventually take something like a PyTorch model, and unwind everything, resulting in a directed acyclic graph of individual bit-level operations (OR, AND, XOR). That graph will then be compiled into a bitstream suitable for an FPGA-like substrate that eliminates the memory/compute divide, the BitGrid[1].
FPGA routing is a non-trivial problem, I'm hoping to get it down to seconds. I'm currently trying to build the software stack to make it usable.
The goal is to answer questions about BitGrid:
How efficiently can I pack a program into the hardware?
Is the model I've chosen for a cell optimal?
How many femtojoules per operation would a cell actually take?
This isn't just some CRUD application with a web front end. There are a number of layers of abstraction at play, and the LLMs seem to handle it well if you limit the depth under consideration.
[1] BitGrid eliminates the traditional memory/compute divide that causes most of the energy consumption of CPUs, GPUs, and other accelerators. Even FPGA systems tend to focus on emulation of these models, and routing fabric for minimum latency, instead of maximum performance. Because all the active lines only reach nearest neighbors, power consumption for a given operation can be far lower than the traditional approach.
PS: I pay $10/month for GitHub CoPilot, which apparently now includes ChatGPT5
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Could you elaborate your argument? I'd say AI is far from replacing any human contributor at this point. Context doesn't work the same way as memory + experience in humans. While humans have an impression and a flexible mental model of any single domain, AI needs hard data, which is hard to manage with context and can't really be worked around by fine-tuning in practice, lest you have to retrain the model on each and…
Sure. My point is that it's not unusual for the quality of work of a junior coder to be fairly low, and that work typically needs to be double checked by someone more senior before it can be used in production. Often, an LLM will produce a higher quality output than that junior developer in which case, given that the work has to be checked by a senior coder no matter what, the LLM can replace the junior developer. Al…
This pattern I see play out time and time again. Senior builds the integration to various team services, then juniors tweak & mimic those to do more.
> let's say that a senior developer could produce N units of work per Y units of time.
So, it's more the case that senior developers are building full production assembly lines, that can then be replicated to do similar things relatively easily. The cost of the units of work are not at all equal.
How does this apply to AI? Good luck getting AI to do something novel, by definition it can't. OTOH, extending an existing pattern is trivial for a senior developer to do after they have built that pattern; juniors spend a lot of time at it cause they have to learn everything.
So, who is the AI going to replace? If the junior, then you have a senior developer using an AI to do something that is already trivial for them to do. No way AI is replacing the senior.. So who? Who is getting replaced by the AI and who is using it?
I'm tired of the anthropomorphization marketing behind AI driving this kind of discussion. In a few years, all this talk will sound as dumb as stating "MS Word spell checker will replace writers" or "Photoshop will replace designers". We'll reap the productivity benefits from this new tool, create more work for ourselves, output will stabilize at a new level and salaries will stagnate again, as it always happens.
People are stupid, always have been - took thousands of years to accept brain as the seat of thought because “heart beat faster when excited, means heart is source of excitement”. Heck, people literally used to think eyes are the source of light since everything is dark when you close them. People are immensely, incredibly, unimaginably stupid. It has taken a lot of miracles put together to get us where we are now…bu…
Stupid is people in 2025 believing the world is flat and germ theory is a hoax. Ignorance becomes stupidity when our species stands on the shoulders of giants but some people simply refuse to open their eyes.
I'm tired of the anthropomorphization marketing behind AI driving this kind of discussion. In a few years, all this talk will sound as dumb as stating "MS Word spell checker will replace writers" or "Photoshop will replace designers". We'll reap the productivity benefits from this new tool, create more work for ourselves, output will stabilize at a new level and salaries will stagnate again, as it always happens.
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The simplest system that acts entirely like a human is a human. An LLM base model isn't trained for abstract thinking, but it still ends up developing abstract thinking internally - because that's the easiest way for it to mimic the breadth and depth of the training data. All LLMs operate in abstracts, using the same manner of informal reasoning as humans do. Even the mistakes they make are amusingly humanlike. There…
> The simplest system that acts entirely like a human is a human. LLM's do not act entirely like a human. If they did, we'd be celebrating AGI!
Don't expect them to show mastery of spatial reasoning or agentic behavior or physical dexterity out of the box.
They still capture enough humanlike behavior to yield the most general AI systems ever built.
I'm tired of the anthropomorphization marketing behind AI driving this kind of discussion. In a few years, all this talk will sound as dumb as stating "MS Word spell checker will replace writers" or "Photoshop will replace designers". We'll reap the productivity benefits from this new tool, create more work for ourselves, output will stabilize at a new level and salaries will stagnate again, as it always happens.