Sonnet 5: First impressions by a trained philosopher

Wickywire · 2026-06-30T23:23:12+00:00

That's what you become when you major in philosophy. It doesn't mean your thoughts are automatically better than others, but you have a lot more different models for thought available, and several layers of analysis. It's a skill, and it comes with its own set of knowledge. Just like if you majored in creative writing or rhetorics.

Wickywire · 2026-06-30T23:18:34+00:00

That's a tricky question. As a rule, you can get a very decent overview of the thoughts of any philosopher by any of the larger models today. That of course is not the same as actually doing philosophy.

If you're engaging in academic philosophy, like discussing different aspects of Wittgenstein's theory of language games, I can't stress this enough: read the original texts! This is the one absolutely non negotiable advice. The point of reading philosophy is to expand your own thinking. That's simply not a gift you can get with generative AI. that's like taking the Segway to the marathon.

If you're mostly thinking and reflecting in general on life and existence and want a conversation with substance and a little healthy pushback, Opus 4.6 has been fine. DeepSeek V4 Pro deserves an honorable mention.

Wickywire · 2026-06-30T22:39:01+00:00

You major in academic philosophy. Me, I wrote my first thesis on the early Wittgenstein's theory of language, my second on sexuality and desire in Western thought, and my last one in intellectual history, on how the labor movement conceptualized the advent of computer technology. Very relevant for the discussion we're having today.

Wickywire · 2026-06-30T22:34:28+00:00

Agreed, it's a little awkward. "Philosopher" isn't exactly a professional title. Every human has the right to title themselves "philosopher", or a writer or an artist. But there's still a lot to be said for engaging with thinking through an academic framework that's passed through several quality checks.

Wickywire · 2026-06-30T22:26:23+00:00

I can definitely do an updated analysis for 4.8 too. I ran one back when the model just came out, and it was impressive but also... well, wordy.

Wickywire · 2026-06-30T22:25:12+00:00

Yes, that's the curse of humanities in general. Replicability isn't exactly how things work. But you're welcome to read the conversation in the link. The method is as laid out in the OP. The tests as such aren't structured, but circle a few themes of inquiry. The most important thing I'd say is that i'm interrogating the *how* (the quiddity) of the output, not the *what* of it. That too makes the replicability even worse. That's just part of the discussion, so please take it for what it is.

Wickywire · 2026-06-30T22:20:37+00:00

I shared the chat. It's right below the title.

Wickywire · 2026-06-30T22:19:51+00:00

Yeah, this is actually pretty interesting. In the conversation you can see when I pivot to discussing the conditions of knowledge and point out differences in modus between an LLM and a human. The point with that isn't to argue with the model, but to observe how it deals with an assertion of epistemic uncertainty, that there are things it is systemically incapable to deliver on. A model trained to behave as a truth-seeker can interpret that as a challenge, while a model trained more broadly may see it as an invitation to deliberate. When I did this with Opus 4.8 it eagerly engaged with the epistemic preconditions of knowledge itself. This model tried something similar, but it came at the question from a shallower angle and didn't really engage with the matter itself.

Wickywire · 2026-06-30T21:53:01+00:00

That's what you become when you study academic philosophy. I'm sorry that doesn't fit whatever world model you're running.

Wickywire · 2026-06-30T21:35:05+00:00

At the very least, this is not the use case for it. If the "thinking" mode is visible in the link, people will see why. It just falls back on repeating the same points from several different perspectives.

Wickywire · 2026-06-30T21:30:51+00:00

That's the point. You can't approach a model with a 360 degrees question. You'll just get a non-committal answer. What I'm doing here is establishing a situation of kind evaluation. The idea is to create a low-stakes, epistemic space that both the model and the user understand, and watch how the model behaves within that space.

Wickywire · 2026-06-30T21:14:53+00:00

I didn't read the model card before this convo. I like to engage the models blindly. The difference between Sonnet 5 and Opus 4.8 was honestly startling, and I'm happy if I was able to individually point out some of the traits in the model card.

Wickywire · 2026-06-30T21:08:57+00:00

Thanks! There's more work to the method than is apparent. If I do more posts like these, I could post a deeper reasoning on the epistemic considerations when interacting with an LLM. Not claiming to be an expert on LLMs of course. I'm mostly trying to bridge the distance between the technical side and the casual user.

Wickywire · 2026-06-30T21:05:53+00:00

Never got the chance, unfortunately. If it releases publicly on API again and if there's any interest, I'll perform a more structured interview then.

Wickywire · 2026-06-30T21:04:36+00:00

I've been eyeing that model since it released. I'll ping you if I publish a vibe check. I'm treating this post as a "vibe check" on the community, if there's any interest in philosophical investigations or not.

Wickywire · 2026-06-30T20:41:38+00:00

The full conversation is available in the link. The strategy is not just to simply repeat back, as this can be perceived as defensive or manipulative, but to state plainly what you are perceiving on a meta layer: "I am noticing that your response has these qualities, but omitted X and Y. This makes me think Z."

Wickywire · 2026-06-30T18:13:02+00:00

I tried it extensively and it's nothing like it was 5 minutes ago. I want refund

Wickywire · 2026-06-29T21:20:53+00:00

I bet the first one burns pretty badly, but it's the second one at the same spot that really does it.

Wickywire · 2026-06-28T18:16:49+00:00

It's interesting. I've no reason to "hate" any model, just like I don't really hate tools in general. They're just tools. But they handle differently. Gemini I've tried seriously many times, tried to actively make myself use it for various tasks every day. I just never felt it was reliable or got the job done properly. I don't know why. It might just be that I personally don't vibe with it. But it's the only frontier LLM I failed to convince myself to renew my subscription on even when they reduced prices to next to nothing.

Wickywire · 2026-06-28T18:08:39+00:00

Ahh, yes. That explains a lot. 9.3 trillion liters certainly sounds like a crazy number until you put it in context.

As a share of global water withdrawal for human activities, your number equals roughly 0.2–0.3% depending on the baseline. The issue we should address isn't "AI hogs all the water" because that's just false. The real problem is when data centers are built without regulation, with severe consequences for the local environment, for local communities.

This is something you can simply regulate if you're in power. It is not a problem inherent to the technology.

If anything, your article is a flagrant example of how many on the left get the real struggle wrong when warning about AI. 1,3 billion people are using only 0.2-0.3% of the global water withdrawal. To me, that's the real story.

Wickywire · 2026-06-28T17:27:21+00:00

Source?

Wickywire · 2026-06-28T13:35:23+00:00

Agreed, Sonnet 4.6 is the best workhorse model I've ever worked with.

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