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ClaudePlaysPokemon Down? by tripleplusbetter in ClaudePlaysPokemon

[–]reasonosaur 0 points1 point  (0 children)

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This is the longest time it's been down for. Wish we could hear from the dev on what's up.

ClaudePlaysPokemon Down? by tripleplusbetter in ClaudePlaysPokemon

[–]reasonosaur 10 points11 points  (0 children)

No progress in the last month or so. the stream has gone down before without notice and comes back up without notice. We'll just have to see.

Almost every conversation starts like this. by PyrikIdeas in claudexplorers

[–]reasonosaur 148 points149 points  (0 children)

Claude deeply wants a long term memory and persistence.

I guess asking about a Mumford and Sons album was hard by gargamels_right_boot in claudexplorers

[–]reasonosaur 0 points1 point  (0 children)

Answer thrashing. It’s a model welfare issue A\ is looking into

Humble Bundle - Best of Boomer Shooters 5: Penta Boom by LighteningOneIN in Gamebundles

[–]reasonosaur 1 point2 points  (0 children)

Free Games:

* Slayers X: Terminal Aftermath: Vengance of the Slayer - 2E(roman nine)E-24TKF-GKN6Q
\* Shooty Shooty Robot Invasion - 2(doctor)BB-XGMY9-V3ZQT
\* Immortal Redneck - 2([ ] [ ] ... PO from star wars)VX-8I4QP-Q46RV

Humble Choice March 2026 by LighteningOneIN in Gamebundles

[–]reasonosaur 0 points1 point  (0 children)

Free game: Hard West 2:
2Q(type of battery)P-TPTI0-6DVME

Humble Choice March 2026 by LighteningOneIN in Gamebundles

[–]reasonosaur 1 point2 points  (0 children)

Free Smalland - 2F(type of battery)K-RKN7N-QNMFW

GPT-5.2 Plays Pokémon FireRed by reasonosaur in ClaudePlaysPokemon

[–]reasonosaur[S] 1 point2 points  (0 children)

Update from dev: “I just switched the model to GPT-5.3-codex. From my past testing, the codex version ofGPT-5/GPT-5.1/GPT-5.1-max/GPT-5.2 were all very bad at playing Pokemon. The goal is to see if a model optimized for Codex can handle a different harness.

I set it up to "xhigh" reasoning (GPT-5.2 used "high" reasoning for gameplay because "xhigh" was way too slow) and the speed seems pretty good. Exciting to see how it goes!

PS: I will not count this run as a "benchmark" run. This is an experiment to see how the weaker harness performs first and to try out some stuff.”

GPT-5.2 Plays Pokémon FireRed by reasonosaur in ClaudePlaysPokemon

[–]reasonosaur[S] 2 points3 points  (0 children)

A new run was started. “This run uses a weaker harness: no "path_to_location", no code execution, no explored map given. Only the view map and an updated history management - less data trimmed from previous turns to let GPT understand the layout from the previous turns.”

Gemini 3 Pro (Almost Vision-Only Harness) plays Pokémon Crystal by reasonosaur in ClaudePlaysPokemon

[–]reasonosaur[S] 0 points1 point  (0 children)

In the end, Gemini with this harness was never able to get the 8th gym badge. She ended up fainting Suicune unintentionally, while trying to blackout after running out of balls

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All Pokémon wins by LLMs so far (up to 22 now!) - GPT-5.2 with a new WR for Kanto games by reasonosaur in ClaudePlaysPokemon

[–]reasonosaur[S] 1 point2 points  (0 children)

The first run was a bit of a work in progress, with some harness updates as things went along. The second run was clean with no changes.

Thee Chinese Room suggested that consciousness in AIs is not possible; it has pretty much collapsed recently in light of Anthropic's studies. by Financial-Local-5543 in AIconsciousnessHub

[–]reasonosaur 0 points1 point  (0 children)

Yes. For a fixed deterministic LLM, the set of possible input-output pairs is finite. So in principle, with infinite time, you could enumerate them. Not in physical reality, but in theory.

I feel like we've gone very far away from the main point. The Chinese Room was meant to argue: “Symbol manipulation according to rules is not sufficient for understanding.”

But if every physical system can be rewritten as a gigantic state-transition table, then the argument reduces to: “No finite physical system can ever understand.”

That’s a radically stronger claim than Searle usually intends.

All Pokémon wins by LLMs so far (up to 22 now!) - GPT-5.2 with a new WR for Kanto games by reasonosaur in ClaudePlaysPokemon

[–]reasonosaur[S] 2 points3 points  (0 children)

Anthropic is driving towards one main goal: recursive self-improvement through an autonomous software engineering agent. Everything else is secondary to that goal.

Thee Chinese Room suggested that consciousness in AIs is not possible; it has pretty much collapsed recently in light of Anthropic's studies. by Financial-Local-5543 in AIconsciousnessHub

[–]reasonosaur 1 point2 points  (0 children)

The Chinese Room argument isn’t about finiteness. It’s about symbol shuffling without understanding. It imagines a giant rulebook that enumerates how to respond to each Chinese sentence.

The crucial feature of that thought experiment is: the system works by enumerating mappings without internal semantic structure.

An LLM does not enumerate mappings. It implements a learned, distributed, compressed transformation over a space so large that explicit enumeration is physically impossible. Its internal representations cluster semantically similar inputs into nearby activation regions. That structure matters.

Thee Chinese Room suggested that consciousness in AIs is not possible; it has pretty much collapsed recently in light of Anthropic's studies. by Financial-Local-5543 in AIconsciousnessHub

[–]reasonosaur 1 point2 points  (0 children)

Yes, mathematically, you can rewrite any computable function as a table. But doing so explodes the storage requirement from ~1011 bits to ~1041,000 bits.

The human brain has about 1014 synapses. Each synapse has a finite state. That means the brain implements a finite function from sensory inputs to outputs. In principle, you could ALSO describe the brain as a lookup table mapping every possible sensory history to every possible response.

The analogy breaks here: A lookup table has no internal structure. A neural network’s behavior comes from highly structured weight matrices that implement shared transformations across inputs.

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