entangled dot cloud
MIT engineers develop a magnetic transistor for more energy-efficient electronics
Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly
Show HN: I built a local macOS dictation app using Nvidia Parakeet and MLX
I built a voice dictation app for macOS mostly because I was tired of how awful Siri is and I didn't want to pay a subscription for cloud tools.The app uses NVIDIA's Parakeet v3 (TDT) as the primary engine. Inference is handled by the FluidAudio library. It's insanely fast on M-series chips. For Intel users, I added local Whisper support ranging from Tiny up to Large v3 Turbo (quantized to save space).The interesting part was the AI integration. I originally tried to pipeline the
Show HN: Kimi K2.5 (Agent Swarm, beats GPT-5) now on RouterLab (Swiss hosting)
Hi HN!Moonshot AI released Kimi K2.5 today, and we integrated it on RouterLab within hours.Why this matters:*Open source beats proprietary:*
• Kimi K2.5: 50.2% on HLE (Humanity's Last Exam)
• GPT-5: 41.7%
• Claude 4.5: 32.0%First time an open-source model beats GPT-5 on expert-level reasoning.*Agent Swarm architecture:*
• Orchestrates up to 100 parallel agents
• 1,500 simultaneous tool calls
• 4.5x faster than sequential execution
• Autonomous task decompositionExample: "Analyze 50 com
Show HN: Quantifying Data Drift Using Coefficient of Variation and Numba
I realized that 'data quality' is often vague, so I wanted to build a deterministic score (0-100) for how stable a dataset is.
Show HN: Chippery, an OpenCode fork that (often) uses 20-40% fewer tokens
I kept hitting token limits with Claude Code on larger codebases and ended up building Chippery (a fork of OpenCode) to reduce context size outside the model.It uses a symbolic index, navigation layer, semantic and Pagerank-like ranking and some context reduction / compression techniques to avoid resending and rereading the same files and lookups.I ran benchmarks mostly with Anthropic’s models, and saw roughly 20–40% token reduction depending on workflow on average, in some cases quite a bi
Show HN: Donkey Support –> Reply to Support Chats from Slack/Discord/Telegram
Hi HNI built Donkey Support, a lightweight support widget for web apps that routes customer chats into Slack, Discord, or Telegram threads, so you can reply from the tools you already use instead of checking another support dashboard.I originally built this because the last early-stage SaaS I worked on was full of bugs. We were moving fast, review wasn’t great, and things broke a lot.What saved us wasn’t perfect code. It was fast support.We had a little support widget on the site, and the second
What's the Point of Clawdbot?
I personally don't get it.I can interact with ChatGPT and Claude directly using apps on my phone.I can already upload pictures and files from my phone to both the above.Before long those apps will be able to access other data sources on my phone directly such as email, Google Drive with better integration.I never need to work with files stored on my Mac mini remotely when I'm out and about. Most of my stuff is in the cloud anyway.We already have Cowork, computer use, addons for Chrome
Ask HN: My boss wants us to vibe code and I feel in danger
I am using a throwaway account because my boss scared the hell out of me.I work for a large AI team (think one of the big ones... Won't say which). And our CEO gave a short but quite clear talk: you HAVE to vibecode. He will check how much we vibe code, how many PRs etc, and will act accordingly.Now. I work in the infra team. What this vibe code mandate means for me is that my work will become unsustainable. I will forecast hordes of PRs impossible to review in time, bloated to the roof. An
A few random notes from Claude coding quite a bit last few weeks
<a href="https://xcancel.com/karpathy/status/2015883857489522876" rel="nofollow">https://xcancel.com/karpathy/status/2015883857489522876</a>
Scientists say quantum tech has reached its transistor moment
Quantum technology has reached a turning point, echoing the early days of modern computing. Researchers say functional quantum systems now exist, but scaling them into truly powerful machines will require major advances in engineering and manufacturing. By comparing different quantum platforms, the study reveals both impressive progress and steep challenges ahead. History suggests the payoff could be enormous—but not immediate.
Strange white rocks on Mars hint at millions of years of rain
Bright white rocks spotted by NASA’s Perseverance rover are rewriting what we thought we knew about ancient Mars. These aluminum-rich clays, called kaolinite, usually form on Earth only after millions of years of heavy rainfall in warm, humid environments—conditions similar to tropical rainforests. Their presence on today’s cold, dry Mars suggests the planet once had abundant rain, flowing water, and possibly lush oases long ago. Even more puzzling, the rocks are scattered across the landscape w
3 mind-blowing discoveries by quantum physics about the power of mind
Quantum physics didn’t set out to explain the mind. It set out to explain matter. And yet, the deeper scientists went into the smallest building blocks of reality, the harder it became to ignore one unsettling factor: the observer. Somewhere between equations and experiments, the mind stopped being a passive witness and started looking like an active participant. Here are three discoveries from quantum physics that quietly changed how we understand the power of the mind, not in mystical language, but in ways that still make scientists uncomfortable. Scroll down to read more.
AI makes quantum field theories computable
An old puzzle in particle physics has been solved: How can quantum field theories be best formulated on a lattice to ...
New unified theory may finally link 2 core pillars of quantum physics
For more than a century, modern physics has rested on two towering frameworks that do not quite agree with each other.
Tokyo group observes positronium beam as ‘quantum matter wave’
The same phenomenon was later confirmed for neutrons, helium atoms, and even large molecules, making matter-wave diffraction ...
Cathie Wood says quantum computing stocks could be dead money for 20–40 years
Cathie Wood has built her reputation on backing transformative technologies early, but on quantum computing she is sounding a ...
Is Particle Physics Dead, Dying, or Just Hard?
Columnist Natalie Wolchover checks in with particle physicists more than a decade after the field entered a profound crisis.
Transformers v5 GA is out
We've finally released the first stable release of transformers v5 in general audience, it comes with many goodies:- Performance especially for Mixture-of-Experts (6x-11x speedups)- No more slow/fast tokenizers: way simpler API, explicit backends, better performance- dynamic weight loading: way faster, MoE now working with quants, tp, PEFT..We have a migration guide on the main branch; please take a look at it in case you run into issues, we also have documented everything in release n
How do you reconstruct what a financial system observed at time T, years later?
I’m working on a problem I keep running into when dealing with complex financial analysis systems (quant, risk, governance, infra).
Markets are obviously uncertain, that’s not the issue.
The issue is that, ex-post, it’s often very hard to reconstruct what the system actually observed at a specific point in time.
Typical questions that become surprisingly hard to answer a year later:
- What data was actually available at time T?
- In what order was it processed?
- Which transformations and constr
Ask HN: Any need for Monte Carlo in prediction markets?
In general looking for a use case to a Monte Carlo solution I wrote. My hypothesis is crypto and prediction market tooling is not yet mature and there is a market for financial quant analytics with adaptations.
In the case of prediction markets, would it be helpful to see the world under consistent joint distribution? For example as a way to see relative value, quantify and aggregate exposures? My tool could provide tail risk for these questions.I can understand there not being demand today, but