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While I love Meta Quest hardware, the store and developer ecosystem is horrific. I can't even find my own game on the Meta Store and it's so slow. No wonder we only have 1k downloads after 2 weeks Meanwhile on Steam we had 10K+ downloads alone on week 1...

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PLECTRUM SOFT | Wishlist PROJECT MIX on Steam!

18,138 subscribers

23,034 Aufrufe • vor 11 Monaten •via X (Twitter)

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The Unity Cube is one of the first (if not the first) apps on the #MetaQuest Store to RUN AT 207 Hz! And it also works pretty well with the experimental 240 Hz mode! I made this update so that every one of you can try for free the crazy smoothness of the new Quest framerates by just getting a little app from the store. I had this idea while I was reading about the new framerates on Quest. I thought: hey, I would like to try 207Hz, but only a very simple app could run at 207Hz in #VirtualReality. Then I realized that actually I'm the creator of such an app: The Unity Cube, the app that is just a cube. If that doesn't go at 207 Hz, no other apps can... So I worked on porting the cube to the latest SDKs, I added hand tracking, and then I implemented the 207 Hz support for the Meta Quest 3. And the result is incredibly smooth. It is like enjoying VR on steroids. There is also a strange halo effect appearing, but the experience is still enjoyable. Read about how to try 207 Hz with the Cube, how to implement 207 Hz in your Unity application, and what my impressions are about all of this in my latest article: #technology #unity3d #gamedev

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Everybody is talking about recursive self-improvement (RSI) and meta learning. Here is my old 2020 talk about this [1]. It has aged well. Example: humans still define the starts & ends of trials of many modern meta learners. My RSI systems since 1994 LEARN to (re)define them [2]! [1] Meta Learning Machines in a Single Lifelong Trial (talk for workshops at ICML 2020 and NeurIPS 2021, based on earlier talks since 1994). Abstract: the most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987 [2]. Here I summarize our work on meta reinforcement learning with self-modifying policies in a single lifelong trial (since 1994), and mathematically optimal meta-learning through the self-referential Gödel Machine (since 2003). Many additional publications on meta-learning since 1987 can be found in the RSI overview [2]. [2] J. Schmidhuber (AI Blog, 2020-2025). 1/3 century anniversary of first publication on recursive self-improvement (RSI) and meta learning machines that learn to learn (1987). For its cover I drew a robot that bootstraps itself. 1992-: gradient descent-based neural meta learning. 1994-: meta reinforcement learning with self-modifying policies. 1997: meta RL plus artificial curiosity and intrinsic motivation. 2002-: asymptotically optimal meta learning for curriculum learning. 2003-: mathematically optimal Gödel Machine. 2020-: new stuff!

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Sensitive content

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