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Because of the way they are trained, large language models capture only a slice of human language. They’re trained on the written word, from textbooks to social media posts, and our speech as captured in movies and on television. These models have minimal access to the unscripted conversations we have face to face or voice to voice. This is the vast majority of speech, and a vital component of human culture. There’s a risk to this. The increased use of large language models means we humans will encounter much more AI-generated text. We humans, in turn, will begin to adopt the linguistic patterns and behaviors of these models. This will affect not just how we communicate with one another, but also how we ...

"The Starship Pez dispenser demonstrates very smart industrial design and scale."

Thousands of new fossil-fuel power sources are quietly firing up across the state to power the AI boom, thanks to a regulatory loophole, leaving residents feeling blindsided.

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With pricing in flux and new models available, here’s which Surface Laptop and Surface Pro to buy.

“EMF straws” and similar products are being sold as a way to block electromagnetic frequencies that come from common electronic devices, even without scientific evidence that they work.

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OpenAI found two unrelated bugs masquerading as one in ChatGPT's data infrastructure. Silent hardware corruption on one Azure host and an 18-year-old race condition in GNU libunwind's setcontext function with a one-instruction vulnerability window. The breakthrough came from switching to population-level crash analysis rather than examining individual core dumps. By Steef-Jan Wiggers

NHTSA administrator Jonathan Morris called reports that self-driving cars had driven into emergency scenes and blocked ambulances and firefighters “unacceptable.”

An MSG database tracked and categorized hundreds of celebs, famous Knicks superfans, and even some of Taylor Swift’s wedding guests. Labels included “LGBTQIA,” “DO NOT HOST,” and low to high “risk.”

ChatGPT Work is an agent that can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work.

More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work.

Un’area interna che sembra una lavagna di ragionamento: non è coscienza, ma è un indizio forte su come emergono controllo e pianificazione nei transformer. Negli ultimi anni ci siamo abituati a pensare ai modelli linguistici come a enormi “scatole nere”: un prompt entra, un testo esce, e nel mezzo c’è un mare di matrici difficili da ispezionare. Ma c’è una novità interessante: alcune analisi suggeriscono l’esistenza di una piccola regione interna, relativamente organizzata, che funziona come uno spazio di lavoro per concetti . Un posto dove il modello “tiene a mente” qualcosa prima di produrre la risposta. È un’idea che fa scattare subito l’associazione più pericolosa (e più abusata) del momento: coscienza . In realtà, il punto non è stabilire se un LLM sia cosciente; il punto è molto più concreto e utile per chi sviluppa: se esiste un’area interna che concentra il ragionamento controllabile , allora possiamo capire meglio cosa guida certe risposte e come intervenire su errori, allucinazioni e comportamenti indesiderati. J-Space: una “lavagna” interna per il ragionamento L’idea chiave è questa: dentro il modello emergerebbe un piccolo insieme di pattern neurali “coerenti” (chiamiamoli J-Space ) che si comporta come una lavagna. Su questa lavagna compaiono concetti (non necessariamente parole che verranno stampate). Questi concetti influenzano la catena di ragionamento . Molte altre abilità—fluency, grammatica, stile, completamento locale—sembrano invece scorrere “automaticamente” altrove. Se questa separazione regge, spiega un fenomeno che tutti abbiamo osservato: modelli capaci di scrivere in modo impeccabile, ma fragili nel ragionamento o incoerenti quando devono mantenere vincoli. Il test più interessante: sostituire un concetto e vedere il ragionamento obbedire Un esperimento illuminante consiste nell’individuare un concetto attivo nello spazio di lavoro e sostituirlo con un altro, senza cambiare né prompt né output manualmente. Esempio (semplificato): Domanda:

This post contains Railway referral links. If you sign up through one I get a bit of credit. I build Old Light , a real-time strategy game that runs in the browser. Claim stars, grow an economy, send fleets, all while other players and NPC empires do the same. The second a build finishes or a fleet lands, the server pushes it to every connected client over a WebSocket. That last part, a long-lived server holding an open socket, rules out most of the usual hosts. Here's what it ruled in. Why not Vercel or Netlify Serverless shines when your backend is stateless functions. It's the wrong shape the moment you need a socket that stays open: socket.io wants one process that lives for the whole session, and serverless boots per request and then freezes. You can bolt on a managed WebSocket service, but that's a second system to run and pay for. Railway runs your service as a normal long-lived process, so socket.io just connects. Fly.io does this too with more knobs to turn. I wanted to ship, so Railway won. Monorepo, two services Old Light is an npm workspaces monorepo: a shared types package, an Express plus TypeORM plus socket.io API, and a Vite web app served by a small Express server. On Railway that's two services on the same repo, each with its own root directory and build command, shared built first. They deploy as separate origins, so the web app reads the API's URL from VITE_API_URL . Vite bakes that in at build time, so it's a build variable, not a runtime one. Postgres is a plugin that injects DATABASE_URL , and production runs migrations rather than synchronize . WebSockets need nothing special until you run more than one instance, at which point you'd add a Redis socket.io adapter. I haven't left a single box yet. A healthcheck that stops version skew Two services don't go live at the same instant. Push a commit that touches both, the web finishes first, and for a minute your new frontend is calling API routes that don't exist yet. It 404s, then heals itself o