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Does the Universe Contain Negative-Mass Particles?

The mainstream of cosmology asserts that 84% of the matter in the Universe is invisible, labeled as “dark matter”. The total matter which accounts for attractive gravity amounts to 32% of the cosmic mass-energy budget, while the remaining 68% — in the form of “dark energy”- induces repulsive gravity. The ordinary matter that we are made of, makes only 5% of the cosmic budget. We are made of rare materials in the cosmic context!

Since the dark matter and dark energy components are invisible, we had not observed them directly but only inferred them indirectly through their gravitational influence. This is all fine as long as gravity is the curvature of spacetime, as formulated by Albert Einstein in 1916. Despite the overwhelming consensus of the mainstream, the nature of dark matter and dark energy remains unknown following a century of unsuccessful searches. Is it possible that these constituents are fictitious “ghosts” that do not actually exist, but were imagined because Einstein’s equations fail to describe gravity correctly on cosmic scales?

I spent the day today brainstorming through this possibility along the following lines.

Quantization of Harmonic oscillator|Quantum field theory

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If You Vibe-Code It, Will They Come?

We’re living in a wild moment where anyone with a decent idea can vibe-code a fully functional application into existence before Monday morning. The technical barrier to entry didn’t just lower; it completely evaporated over the weekend.

But as the digital landscape gets flooded with hundreds of thousands of new projects daily, a sobering reality is hitting the builder community hard. Code has officially become a commodity, and simply having a product doesn’t mean a damn thing if you are screaming your lungs out into an absolute void.

That is the exact pivot point I tackle in my latest piece. When vibe-coding removes the engineering moat, the only true competitive advantage left on the field is distribution, positioning, and storytelling. We have officially entered a pure attention economy where your new technical superpowers are practically useless without a distinct, human flavor.

Automated AI tools will happily burn through your budget chasing hollow vanity metrics, but they completely lack the empathy, taste, and psychological grit required to read a shifting cultural zeitgeist and build a brand that flesh-and-blood people actually trust.

The scales of power have tipped, and the era of the engineering monopoly is officially over. The future doesn’t belong to the solo builders who stop at the deployment screen, but to the AI-armed marketing generalists who know how to orchestrate the machine and command the narrative.

If you are ready to stop fetishizing the code, look past the blind algorithms, and discover the strategic roadmap for scaling from a ghost town to a thriving audience of a million engaged users, you need to read the full breakdown. The vibe-coders have built the stage—it’s time to learn how to draw the crowd.


Taking Longer Steps in Numerical Simulations

It’s often the case that a dynamical system’s constituents move orders of magnitude more quickly than the collective motion that interests researchers. That disparity in scale frustrates modelers. So many computationally intensive time steps are needed to reach the final state that the computation becomes infeasible. Now Filippo Bigi of the Swiss Federal Institute of Technology in Lausanne (EPFL) and his colleagues have extended and tested an approach that uses a machine-learning model to extend the time steps in an atomic-scale simulation by an order of magnitude or more while obeying physical constraints [1]. Their method is general and could be applied to planetary systems, molecular machines, and other dynamical systems.

The EPFL researchers’ starting point was a formulation of classical mechanics that describes the evolution of a system in terms of the positions and momenta of its constituents and an energy term, the Hamiltonian. In general, these and other equations of classical mechanics satisfy fundamental geometric constraints. What’s more, approximate solutions of those equations can be made to satisfy the same constraints. Bigi and his colleagues realized that machine learning could leapfrog over many time steps while also respecting those same geometric constraints.

The researchers tested their approach on several systems, including the three-body problem of celestial dynamics and the transition of germanium telluride to a glassy state. Their simulations reproduced trusted benchmarks but with time steps ten or so times longer. Currently, enforcing the physical constraints undoes most of the computational advantage of the longer time steps. However, the team is optimistic that it can find more computationally efficient implementations.

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Cloud-tested quantum noise model predicts superconducting qubit errors with sevenfold better accuracy

Researchers from the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, and Johns Hopkins University in Baltimore have developed a practical, comprehensive noise-modeling framework for a popular class of superconducting quantum processors. Their work, published in the journal PRX Quantum, offers a sevenfold improvement in predictive accuracy over existing approaches.

Quantum bits, or qubits, are intrinsically prone to noise—interference arising from environmental factors such as electrical and magnetic fields or temperature fluctuations—as a result of the extreme sensitivity that makes them so valuable for computing. Developing accurate noise models is key to creating the robust quantum algorithms and resilient error-correction protocols required to build truly fault-tolerant quantum computers.

“To really advance the field, we need models that can predict a wide range of behavior while utilizing a small number of parameters, rather than theoretical models that try to account for all of the fundamental physics at play in quantum interactions,” said project lead Gregory Quiroz, a senior physicist at APL and an associate research professor in the Department of Physics and Astronomy at the Johns Hopkins University Krieger School of Arts and Sciences. “The novelty of our approach lies in a unified and experimentally validated framework that connects multiple noise mechanisms and yields a coherent predictive methodology.”

Ultra-thin MoS₂ computer packs 1,400 transistors onto one chip

The rapid advancement and diffusion of artificial intelligence (AI) systems, such as the machine learning models underpinning the functioning of ChatGPT, Gemini and similar platforms, have posed new demands on the electronics engineering industry. In fact, these systems are computationally intensive and consume substantial power, particularly when running on existing devices.

Electronics engineers worldwide have thus been trying to develop new hardware systems that can run machine learning algorithms more energy efficiently, without adversely affecting their performance. One promising approach for reducing power consumption entails the use of two-dimensional (2D) semiconductors, ultrathin materials that have already proved promising for the development of smaller electronics.

Researchers at Nanjing University, Suzhou Laboratory and Huawei Technologies Co. Ltd. recently developed and fabricated a fully functional computer based on the 2D semiconductor molybdenum disulfide (MoS₂).

Claude is Self-Evolving?

In this episode, I break down Anthropic’s research on recursive self-improvement—AI systems that can design and train the next generation with less human help—and why the key battleground is “taste” (choosing goals and next steps). I compare this to evolutionary algorithms and newer examples like DeepMind’s AlphaEvolve, Sakana’s Darwin Gödel Machine, and Karpathy’s AutoResearch, then cover METR Task Horizon and how task length has been doubling. I go through Anthropic’s internal results (Claude writing most merged code, speedup experiments, bug fixes, and a study where models sometimes pick better research next steps), plus the main skepticism: bad productivity metrics, internal-only models, and Goodhart’s Law/reward hacking. I end with an open safety problem where Claude agents closed the gap far faster than humans, and what this means for specifying and checking work.

LINKS:
https://www.anthropic.com/institute/r… voice to text App: whryte.com Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/c… Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 Let’s Connect: 🦾 Discord: / discord ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: / promptengineering 💼Consulting: https://calendly.com/engineerprompt/c… 📧 Business Contact: [email protected] Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 TIMESTAMP: 00:00 Self Improvement Basics 01:30 Evolutionary Loops Today 03:50 Task Horizon Doubling 05:18 Claude Productivity Claims 08:11 Goodhart’s Law 10:30 Agents as Researchers 12:22 What It Means for You.

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AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning

The problem? Human brains (and animal brains, too) are incredibly complex. While these handcrafted models are great starting points, they often oversimplify things and miss the messy, rich reality of actual behavior. On the flip side, using powerful, flexible AI to analyze data can capture that richness, but AI usually gives us a “black box”—it finds patterns but can’t explain *why* or *how* it found them, leaving scientists to do the heavy lifting of figuring out the rules.


Scientific models are widely used across the natural sciences as an interface between scientific theories and empirical data [1]. Such models play a key role, for example, in the study of human and animal learning, where they express algorithmic hypotheses and relate them to psychology and neuroscience data [2, 3]. These models are traditionally handcrafted by expert researchers based on existing theory or new insights. Such handcrafted models, however, are now known to fall short of capturing the full richness of behavior, even in their narrow domains [47]. An alternative data-driven approach has emerged, seeking to discover new insights by fitting and interpreting flexible models [811]. However, these tools require substantial human effort to derive insight from data, and it has been unclear how to discover new ideas from data efficiently. Here, we present DataDIVER, a general approach for automatically discovering computational models from data, and demonstrate that these models surface novel mechanistic insights into human and animal learning. Our approach delivers models that take the form of short computer programs, which are optimized both to fit data well and to be simple. These programs explicitly connect with existing theoretical frameworks and are readily understandable by human scientists. They can also be used to make novel predictions, some of which we show are borne out in re-analysis of existing data. General-purpose tools for surfacing new ideas from data, especially in combination with the large datasets that are increasingly available in many fields, stand to dramatically accelerate scientific discovery.

The authors have declared no competing interest.

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