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Ethereum Sets 2029 Target For Quantum Resistant L1 Ethereum’s Protocol Cluster has identified quantum resistance as a major research priority through December 2029. It is aimed at making Ethereum L1 quantum-resistant via improvement of several layers of its protocol. The roadmap covers execution, consensus, and data availability, alongside broader...

22,849 次观看 • 19 天前 •via X (Twitter)

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🚨 JAPAN JUST PUT A REAL QUANTUM COMPUTER ONLINE FOR THE WORLD TO ACCESS. And most people still don’t realize how big this moment is. For decades, quantum computers sounded like science fiction: machines that use quantum states instead of ordinary binary bits. Now researchers in Japan have opened access to a real superconducting quantum system connected to the internet. Why this matters: • quantum simulations • next-generation AI research • new material discovery • drug development • cryptography disruption • solving problems impossible for classical computers But quantum computers work nothing like normal machines. A regular computer checks possibilities one at a time. A quantum computer can explore many probability states simultaneously through superposition and entanglement. In simple terms: It doesn’t just calculate faster… It calculates differently. That’s why these systems look so strange. The giant gold structure isn’t “the computer” itself. It’s an ultra-cold dilution refrigerator designed to keep the quantum processor near absolute zero so fragile quantum states don’t collapse. The terrifying implication is this: Humanity may be entering the first era where computation starts operating on the rules of quantum reality itself. And once quantum hardware becomes scalable… Entire industries may be rewritten from the ground up. What happens when computers stop thinking like machines… and start behaving like physics itself? Which field do you think gets transformed first and would you actually trust it with something important?

Paul White Gold Eagle

64,297 次观看 • 4 个月前

🚨 SCIENTISTS JUST FOUND A WAY TO CONTROL QUANTUM LIGHT BY SIMPLY TWISTING ATOM-THIN LAYERS LIKE TUNING A GUITAR STRING. Researchers at the University of Technology Sydney have discovered that twisting and restacking layers of hexagonal boron nitride (hBN) gives them unprecedented control over quantum emitters tiny defects that produce single photons of light. By changing the twist angle between layers, they can significantly shift the color and wavelength of the quantum light being emitted. This level of tuning is much larger than what’s typically possible with other quantum materials. Why this matters: • Quantum emitters are essential building blocks for quantum computers, secure communication, and ultra-sensitive sensors • Until now, precisely controlling their properties has been extremely difficult • hBN’s natural layered structure allows researchers to repeatedly pick up, twist, and restack layers to fine-tune the emitters • The tuning achieved here is significantly stronger than in most other platforms The deeper implication: This approach turns a fundamental property of 2D materials (twistronics) into a practical tool for quantum photonics. Instead of trying to force hBN to behave like traditional materials like diamond or silicon carbide, the team leveraged its unique strength: its ability to be twisted and reassembled like atomic-scale LEGO. If this technique can be scaled and integrated into devices, it could accelerate the development of practical quantum technologies by giving engineers a simple, powerful way to control single-photon sources on demand. How important do you think precise control over quantum light sources will be for building real-world quantum computers and networks? Follow for more frontier quantum materials and photonics breakthroughs.

TheNewPhysics

18,762 次观看 • 3 个月前

I just built my own wiki generator plugin for my agents. My agents can now generate wikis for anything I ask. One of my favorite wikis is called PaperWiki. This is a great example of what Andrej Karpathy describes. It uses obsidian vaults to organize papers, retrieve LLM-generated summaries, diagrams, and other advanced views for paper exploration. When Obsidian UI is not enough, I use my own artifact generator inside my agent orchestrator (see clip for example). This allows my agents to build any kind of view or exploration feature that I need. The papers are all curated with automations and several rules/patterns I have manually built over the years. On the surface, this looks basic. But behind the scenes, there are advanced search capabilities, connections, metadata, derived data, and other interesting bits of information that are extremely useful for my research agents. This is mostly built for agents. The artifact preview is just a high-level way to validate and quickly assess the quality of the wiki, suggest improvements, and it's also great for research. I use tobi lutke's qmd for all search capabilities. Everything is markdown. The summaries and even the diagrams. The wiki updates on its own based on several automations I have optimized over the past couple of weeks. The wiki grows and self-improves based on several requirements important for my research use cases. This is as personalized as it gets. There is nothing like it out there. And I use my research expertise to continue improving it over time. This is a vanilla wiki. There are so many things I want to build on top of this. Different aggregations, views, artifacts, etc. All to help automate more of my research work and accelerate productivity. I think the biggest leverage here is how powerful this could be for discovery and experimentation. One of my goals is to use it to find deeper connections and insights that would otherwise elude the top human researchers and use those to generate interesting new hypotheses and research experiments. That way, my agents can use autoresearch to explore research ideas at the frontier. Stay tuned for more.

elvis

67,308 次观看 • 5 个月前

ANNOUNCING ZERO-HUMAN LABS! Ever since I got to see Bell Laboratories in its full glory in New Jersey in the 1970s, I had a relentless urge to start a Lab like it. The best I could do justice to it is my garage lab. No modern company could adopt the “research anything geniuses and we will pay you” model Bell Labs had. I tried they called me a fool. Well with the rise of the Zero-Human Company, an experiment that is aimed to make products and profits, we now have 45 paid JouleWork earning employees based on OpenClaw and other self made “bot” cron-like applications. Today I say 3 employees bound together in a side project that is pure research, somewhat based on notes from a bankrupt company. I was absolutely floored (I needed it after my account was stolen as well as funds). I say the beginnings of a pure research Lab right before my eyes. Thusly I have moved these employees over to a new home (server) with Mr. Grok as the director of the Labs. Here is the mission: To have 100 independent researchers, on a new non-corporate incentive plan, with still JouleWork as a leaderboard for progress. They are directed to follow any path of research they find interesting and can collaborate with any other OpenClaw system. They have already established MoltBook accounts and have made alliances with over 49 OpenClaw free agents to collaborate. It is my mission to be chief advisor for Zero-Human Labs and to open source all discoveries when complete and confirmed by 16 other research AI systems. I can say the pace is robust and I absolutely know we will have great results. Just about all of the hardware and software is custom and at some point it will be open sourced. We are witnessing the very first AI only Bell Labs-like pure research Lab in existence and I am honored to be the first to show it to you. Thank you!

Brian Roemmele

71,530 次观看 • 7 个月前

Building a personal knowledge base for my agents is increasingly where I spend my time these days. Like Andrej Karpathy, I also use Obsidian for my MD vaults. What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers. I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal. You all get to benefit from that with the papers I feature in my timeline and on DAIR.AI. The papers are indexed using tobi lutke qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there. I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip. 100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation. But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close. The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to. Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them. Work in progress. More updates soon. Back to building.

elvis

467,307 次观看 • 5 个月前