Thomas Aylesworth

Just another computer-programming, bicycle-riding, coffee-drinking, video-gaming geek.

[Thomas Aylesworth @SwampThingTom]

@Dimillian Love following them! Very cool seeing what you are able to do.

[Thomas Aylesworth @SwampThingTom]

@msimoni It’s a skill issue on your part.

[Thomas Aylesworth @SwampThingTom]

@rakeingrass The first one (left).

[Santiago @svpino]

In 1994, software was dead. People told me programming was for nerds and that I should become a doctor or a lawyer instead.

In 2004, software was dead. People told me that someone from India would take my job for $5/hour.

In 2014, software was dead. People told me that no-code was going to kill every job and doom my career.

In 2024, software was dead with Devin, Windsurf, Copilot, and OpenAI o1.

We kill software every 10 years, and yet the demand for good Software Engineers has never been higher.

[Thomas Aylesworth @SwampThingTom]

@kasthomas I buy novels all the time.

[Thomas Aylesworth @SwampThingTom]

@mrmikeMTL Yes

[Thomas Aylesworth @SwampThingTom]

@SketchesbyBoze Yep.

[Thomas Aylesworth @SwampThingTom]

@SketchesbyBoze Still have that edition and reread it semi-regularly. Due for another reread now that I think about it.

[Thomas Aylesworth @SwampThingTom]

@davepl1968 That is actually very cool. I need to get myself a PDP-11 so I can try it out and see how much better it is than my college experience. 😂

[Thomas Aylesworth @SwampThingTom]

@exQUIZitely Curious. Akalabeth and Ultima were huge parts of my videogaming in high school. Revisit occasionally. Not going to get my hopes up too high for what he puts out next but will definitely check it out.

[Thomas Aylesworth @SwampThingTom]

@exQUIZitely Creative Computing and Byte were my go-tos. Sadly did not keep them. Would be fun to revisit.

[The New York Times @nytimes]

Margaret Hamilton, who led the 100-person team that wrote the onboard software for the Apollo missions, has died. https://t.co/vhkTLe9J4Z

[Thomas Aylesworth @SwampThingTom]

This.

[Gregory M. Kurtzer @gmkurtzer]

This argument is getting old.

I’ve participated in it far more than I ever thought I would, but here we go again because I’m increasingly convinced that many of the people arguing that LLMs are conscious, or even open to the possibility, just don’t understand how LLMs actually work, so here is context.

This is not a debate about understanding what consciousness is or comparing observations to human experience. It’s about understanding the capabilities and mechanics of the LLM, nothing further.

First off, how LLMs work is understood. There isn’t magic happening inside the model. The calculations an LLM performs are known mathematical operations. Matrix multiplication, attention, normalization, nonlinear functions, probability calculations, etc.

Every operation is defined, implemented, and understood. In theory, you could work through an inference by hand if you were sufficiently masochistic. :-)

But what makes it difficult isn’t some unknown computation happening inside the model. It’s the absolutely enormous scale of computations operating across billions of learned parameters.

But scale does not magically transform computer software into a self-aware consciousness, but it does get more convincing at emulating it.

LLMs learn what they know during a training process. After training their “brain” is effectively locked. During normal inference (using the AI), the model does not continue learning, grow, or modify its weights based on its experiences or conversations.

When you start a new interaction with a model, the model itself has no inherent knowledge of your previous conversations with it. None. It doesn’t remember talking to you yesterday because that conversation wasn’t learned into its weights. The model itself is static.

The apparent continuity was engineered in the surrounding system. This includes previous conversations, stored memories, accessed summaries, preferences, tool results, and other information can be assembled into context and supplied to the model along with your current input.

In other words, the model isn’t remembering the previous conversation. The system is telling the model about the previous conversation again, every time, like it’s the first time.

That’s what might sorta resemble short-term memory. The continuity exists in the context supplied during inference, not as new knowledge learned by the model. Long-term memory is also emulated via tool calls and vector databases outside of the model, and then injected into the context.

It has to be done this way because the model (its “brain”) is completely static, it can’t change.

And there’s a limit. When there’s too much information, some of that context has to be discarded, summarized, compressed, or selectively retrieved to fit within the available context window.

Note: A later version of the model can be trained or fine-tuned using data that includes previous interactions, but that’s a separate training process that produces a new version of the model’s weights. The deployed model did not learn that interaction during inference.

And between inference calls, the model stops computing entirely. There is no thinking, no ongoing inference, contemplation, consideration, or processing of the passage of time. Its weights simply sit there unchanged until the model is invoked again.

Yet somehow, some people decided this is analogous to human consciousness and self-awareness?

Maybe their own weights are frozen and they ran out of context window! ;-)

And if you think anything I’ve said here is technically incorrect, go research it yourself. The tooling necessary to build the model architecture, train it, and run inference is all available as open source on GitHub.

If you want to debate anything I said, go review the source code and point me to the lines where you think the consciousness is.

[Thomas Aylesworth @SwampThingTom]

@exQUIZitely Certainly played a lot of Civ. But I think the ones that hold up best in my memory are Baldur’s Gate, Planescape: Torment, and Thief: The Dark Project.

[Thomas Aylesworth @SwampThingTom]

@nivedtwt So don’t buy it. 🤷‍♂️

[Thomas Aylesworth @SwampThingTom]

@IRIXpilled Slop? TMOG is great. And Dave is 1,000x the developer you’ll ever be.

[Thomas Aylesworth @SwampThingTom]

@esrtweet @planefag It is useful, free, and gets the job done. But the UX is really terrible.

[Thomas Aylesworth @SwampThingTom]

@exQUIZitely Turn-based

[Thomas Aylesworth @SwampThingTom]

@exQUIZitely Bard’s Tale, although I enjoyed them all. And don’t forget Akalabeth.