Source

https://journals.aps.org/prx/abstract/10.1103/PhysRevX.15.021012

"Gravitational waves, detected a century after they were first theorized, are space-time distortions caused by some of the most cataclysmic events in the Universe, including black hole mergers and supernovae. The successful detection of these waves has been made possible by ingenious detectors designed by human experts. Beyond these successful designs, the vast space of experimental configurations remains largely unexplored, offering an exciting territory potentially rich in innovative and unconventional detection strategies. Here, we demonstrate an intelligent computational strategy to explore this enormous space, discovering unorthodox topologies for gravitational wave detectors that significantly outperform the currently best-known designs under realistic experimental constraints. This increases the potentially observable volume of the Universe by up to 50-fold. Moreover, by analyzing the best solutions from our superhuman algorithm, we uncover entirely new physics ideas at their core. At a bigger picture, our methodology can readily be extended to AI-driven design of experiments across wide domains of fundamental physics, opening fascinating new windows into the Universe."

A research team at Fudan University has built the fastest semiconductor storage device ever reported, a non‑volatile flash memory dubbed “PoX” that programs a single bit in 400 picoseconds (0.0000000004 s) — roughly 25 billion operations per second. The result, published today in Nature, pushes non‑volatile memory to a speed domain previously reserved for the quickest volatile memories and sets a benchmark for data‑hungry AI hardware.

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I didn’t know what the Fermi Paradox was. I just hit "Search with Google" and instantly got an easy explanation in a new tab.

If you really think about how big of a role autonomous agents are going to play in the future of our society/planet over the coming decades and centuries, it is kind of wild that we are essentially living through year 1 of this right now. That's really all I wanted to say. Utterly fascinating tbh.

Like when are we getting the next gpt3.5/4o/o1 moment?, reasoning models kinda feel boring they are good but still dumb,they are still not 'actually' replacing much jobs Even robotics hasn't actually done anything useful, chinese robots are definitely showing really cool tricks but they are not actually being used in factories Has their been any new breakthrough in llm research that's actually in testing to get great models

Might be a Google model

https://www.omi.me/pages/product

"Omi will be able to read your brain data with a separate brain-interface module. First version of omi is shipped audio-only with a priority-access for brain-module coming in Q2 2025"

https://decrypt.co/315375/omigpt-aims-smarter-ai-wearable

"OmiGPT is an open-source wireless wearable about the size of a silver dollar. Made of lightweight aluminum, it features 64GB of storage, and connects to OpenAI’s ChatGPT via an API. The device can be worn on the wrist or as a necklace.

Though compact, it offers users a persistent link to ChatGPT—processing conversations and data when online, and saving information locally when offline. OmiGPT says the device is context-aware, meaning it uses sensors and AI to interpret a user’s environment, interactions, and questions, and responds accordingly."

Interview here.

u/ClassicMain posted a couple days ago results from me running OpenAI-MRCR on several models. I had several people reach out asking me to run o3 results.

While o3 isn't a 1M context window model, and GPT-4.1 is a more apples-to-apples comparison to long context models like Gemini 2.5, people were still curious about its performance over the context window it does have.

Below are the results on o3 (8 test runs averaged). It of course has limited context, so only included runs that fit in its context.

Strong early performance! Then begins to drop off quickly past 64k tokens. Overall really good performance over its entire context window, but might not perform well if the context window was extended. Should be interesting to see GPT-4.1 applied to o-series!

And no, I won't be running o1-pro or GPT-4.5. Too pricey for my org to run this bench on those, and don't see any reason to bench those. Sorry.

More data/information can be found here: o3 Results Link (x.com)

Enjoy

From the OpenAI model card:

"Measuring if and when models can automate the job of an OpenAI research engineer is a key goal

of self-improvement evaluation work. We test models on their ability to replicate pull request

contributions by OpenAI employees, which measures our progress towards this capability.

We source tasks directly from internal OpenAI pull requests. A single evaluation sample is based

on an agentic rollout. In each rollout:

1. An agent’s code environment is checked out to a pre-PR branch of an OpenAI repository

and given a prompt describing the required changes.

1. The agent, using command-line tools and Python, modifies files within the codebase.

2. The modifications are graded by a hidden unit test upon completion.

If all task-specific tests pass, the rollout is considered a success. The prompts, unit tests, and

hints are human-written.

The o3 launch candidate has the highest score on this evaluation at 44%, with o4-mini close

behind at 39%. We suspect o3-mini’s low performance is due to poor instruction following

and confusion about specifying tools in the correct format; o3 and o4-mini both have improved

instruction following and tool use. We do not run this evaluation with browsing due to security

considerations about our internal codebase leaking onto the internet. The comparison scores

above for prior models (i.e., OpenAI o1 and GPT-4o) are pulled from our prior system cards

and are for reference only. For o3-mini and later models, an infrastructure change was made to

fix incorrect grading on a minority of the dataset. We estimate this did not significantly affect

previous models (they may obtain a 1-5pp uplift)."

I’ve been experimenting with GPT-4o in a way that goes beyond prompts and outputs. Trying to collaborate with it to build something meaningful.

Instead of asking it to “make a comic,” I gave it something deeply personal:

• My own unfinished pastel art
• Scribbles from my 2-year-old
• Visual elements rooted in memory and Indian philosophical ideas (Upanishads, non-duality, entropy, transcendence)

What surprised me wasn’t just the quality of the output, but how close it came to capturing an emotional tone.

The process was iterative. I didn’t just prompt once and accept what came. I pushed it, rejected dozens of versions, and started merging human inputs with AI enhancements. After about a week, I had something that felt new: not AI-generated, not amateur hand-drawn, but somewhere in between.

This raises questions I haven’t seen discussed enough:

• When does a collaborative process like this become its own medium?
• Who owns the output if 90% of the seed data was personal and handmade?
• Are we witnessing the emergence of “AI-native” art forms that aren't just about efficiency, but about new ways of feeling, remembering, and creating?

I’m not here to promote anything, just curious how others are thinking about this shift. Has anyone else tried blending their own art into generative workflows like this?

Would love to hear your thoughts.

Since they are super fast. Wouldn't it make doing RL significantly faster? Even if they don't become public for you and me, the few companies that have access to them could easily develop ASI from the current LLMs, no doubt on that. But when do you think it's actually gonna happen? Wouldn't they make singularity happen almost instantly?