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Beta Updates Surge Engine • Added VLESS Encryption (mlkem768x25519plus), including share-link import and proxy editor controls. • Added VLESS XHTTP transport with stream-one, stream-up and packet-up modes over HTTP/1.1, HTTP/2 or HTTP/3, including VLESS Encryption and Vision. • Reduced memory retained after QUIC downloads on lossy networks. • Improved QUIC upload performance and reduced CPU usage when chained through a proxy's UDP relay. • Fixed established VLESS gRPC, HTTP/2 CONNECT and TrustTunnel connections being incorrectly counted as connection setup failures when they ended with an error. • Fixed WebSocket policies failing to connect when their ALPN list included h2 and the server selected it. • Fixed Tailscale connections staying on a relay after a peer restarted because updated peer information was not applied. Surge iOS • The proxy editor now imports share links for all supported proxy protocols, as well as proxy lines copied from Surge profiles; unsupported settings are reported instead of silently omitted. • Reorganized the proxy editor with an Advanced page that indicates settings in use, and added controls for more protocol, TLS, WireGuard, Tailscale and testing settings. • Fixed Always use CONNECT not being saved for HTTPS policies, and corrected when TLS and Block QUIC settings appear. • Updated list layouts and badges, and added member counts for policy groups in the proxy list. Surge Mac • The proxy editor now imports share links for all supported proxy protocols, as well as proxy lines copied from Surge profiles; unsupported settings are reported instead of silently omitted. • Reorganized the proxy editor with a remembered Show Advanced Settings option and controls for more protocol, TLS, WireGuard and Tailscale settings.

Today we’re launching the Gemini Decisions API, powered by our new Gemini Decision 1 model in @GoogleAIStudio. Instead of generating text token by token, it’s built for closed-set choices scoring up to 1,000 questions across text, images, audio, video + PDFs in a single pass. A few details: - 3 decision types: yes/no, multiple choice (up to 255 options) + rubric scoring - $0.10 / 1M input tokens with $0 billed for output tokens (~5x cheaper than Flash-Lite) - leads on several benchmark evals with lowest calibration error Google teams are already building with it, below is a look at Stitch Rapid designing a full product flow in seconds (1x speed)

Me ingresaron la nómina y abrí la aplicación del banco... En lugar de 2.100 €, aparecieron 22.100 €. 20.000 € de más. Supe inmediatamente que alguien había cometido un error. Pero pensé: mejor no decir nada. Si lo descubren, ya me llamarán. Pasó un día... luego dos... y después tres. Nadie me llamó. Empecé a pensar que quizás aquel dinero podía cambiarme la vida. Pagué 4.000 € de deudas y reservé un viaje que llevaba años queriendo hacer. Al cuarto día recibí un correo de recursos humanos: «El director necesita hablar contigo mañana a las 9». Entré en su despacho. Estaban él y la responsable de nóminas. Ninguno parecía enfadado. Eso me puso todavía más nervioso. El director me miró y preguntó: «¿Has comprobado cuánto cobraste este mes?». Tragué saliva y respondí:

Introducing GR00T 2: our most advanced, open System One model for robotics. It runs entirely on your robot’s onboard compute, fully untethered, and you own every weight of it. GR00T 2 features: 1. Long-horizon tasks: dexterous manipulation and whole-body control chained over 20+ steps and 5+ minutes, such as performing chemistry lab experiments or assembling model airplanes end to end. 2. X-Adapt: adapting from easy data sources (human wearables and simulation) and deploying zero-shot to diverse real robots. 3. Unified Cognition: native integrations with frontier agents for advanced physical reasoning, robust error recovery, and safety monitoring. It's an open-source buffet! We are releasing not only the model weights and X-Adapt recipes (coming December), but also our agentic framework, simulation engine, annotation engine, hardware design, and even training data (100K hours, that's ~11 yrs of experience), all available today! Everything that moves will be autonomous. Anyone can become a roboticist. Lock in, build with us! Deep dive in thread:

Jensen Huang’s affair is just a trigger. What we are witnessing is a pre-planned power grab over Nvidia’s trillion-dollar computing empire, coordinated by Wall Street and Washington. Some sharp Chinese-American commentators call it America’s new Red Scare targeting elite Asian entrepreneurs. Bill Gates, Elon Musk and other white tech billionaires have faced repeated scandals over personal conduct. None were dragged into national security probes or boardroom takeovers. Only Jensen Huang, after a personal relationship misstep, faces an independent board investigation and hostile narrative from intelligence circles. The double standard could not be clearer: For white elites, personal flaws are private matters. For a Chinese-born leader controlling a strategic tech asset, any tiny blemish gets framed as a national security risk. Nvidia is the backbone of global AI computing, valued at roughly $4 trillion. Unlike Meta, where Mark Zuckerberg locks control with dual-class super-voting shares, or Microsoft and Amazon, deeply integrated with the Pentagon and intelligence agencies, Nvidia has no such protective structure. Public filings from 2026 show Jensen Huang holds only 3.58% of shares, and all directors plus executives together hold merely 3.94%. Vanguard and BlackRock own 9.32% and 7.43% respectively, holding decisive voting power. Without institutional shields or deep political backing, this critical tech giant sits exposed on the chessboard of great-power competition. The groundwork for this takeover was laid months ago. Longtime board allies of Huang, Mark Perry and Michael McCaffrey, stepped down from key committees. Insiders tied to Democratic establishment, Wall Street and U.S. intelligence quietly moved in: ✅ Suzanne Nora Johnson, former Goldman Sachs vice chair and co-chair of Brookings Institution, joined Nvidia’s audit committee in July 2026 with direct oversight of internal investigations and compliance. ✅ Kenneth Jacobs, vice chair of Brookings and incoming Vanguard chair, controls the largest institutional voting bloc and operates within the same inner circle as Johnson. ✅ Gilman Louie,founding CEO of CIA-backed venture fund In-Q-Tel and member of the President’s Intelligence Advisory Board, works alongside Johnson on the same national tech fund, overseeing intelligence acquisition of strategic tech firms. The media rollout followed a precise political timeline. Initial trial balloons appeared on Oct 29. On Nov 6, the very day Democrats secured full control of Congress, Julian Barnes of the New York Times (national security beat) and Demetri Sevastopulo of the Financial Times published coordinated reports, citing intelligence memos while breaking the story of the personal scandal. The real offense was never the affair. It was the 2025 deal Jensen struck with the Trump administration: offering a 25% revenue share to win export approval for H200 chips into China. A normal commercial negotiation, reframed by incoming Democrats as a dangerous compromise on U.S. AI sovereignty. This investigation will not crash Nvidia’s stock — Wall Street holds massive stakes here. But it will deliver three strategic wins for the U.S. establishment: 1. De-founder and de-Sinicize Nvidia: A new permanent national security compliance committee, led by Johnson’s faction, will strip Huang of authority over chip exports and overseas strategy. His dual Chairman-CEO role will be split. 2. Lock down China’s AI ceiling: Washington will seize full command of global advanced computing supply, regulating chip flows to slow China’s AI progress. 3. Retroactively target Trump: Once Democrats take over congressional oversight panels in January 2027, this case will be used to accuse the Trump White House of negligence for approving the H200 export deal. Jensen Huang spent 27 years building this computing empire, believing in rules, technical merit and business contracts. But there is a brutal truth in America’s power game: Top-tier strategic technology and wealth without political cover are nothing more than bargaining chips waiting to be carved up. White elites get generous room for error. Asian pioneers who climb to the very top get almost none. This is not a story about infidelity. It is a raw demonstration of racial hierarchy, capital control and partisan power politics in the United States.

Some things can only be tested in space — like how the hardware holds up to solar events, cosmic rays, or bitflip errors caused by radiation — which is why we’re putting our first TPUs in orbit.

"DASC GLITCH is an ultra-exclusive 10-piece collection merging classic marble sculpture, Cyber-Kintsugi, and chromatic glitch art into the Delta Ape ecosystem. The ultimate synthesis of art and error."

19 year old Japanese student built a trading bot with Claude Code in 2 days. Used his iPad as a second monitor. First night: $6,732 profit. Starting capital: $68. Total profit so far: $750,000. [ 𝐍𝐨𝐭𝐞: 𝐅𝐨𝐥𝐥𝐨𝐰 𝐌𝐞 @zakiraicoder 𝐅𝐨𝐫 𝐢𝐧𝐬𝐭𝐚𝐧𝐭𝐥𝐲 𝐚𝐮𝐭𝐨 𝐃𝐌] Here's how it works👇 The bot scans over 50 markets simultaneously. Syncs live BTC data from Binance every second. Spots price errors before humans even notice. The edge is pure speed + pattern recognition. While traders stare at charts trying to predict the next move, his bot is already executing on mispricing across dozens of markets. No guessing. No emotions. No hesitation. Just Claude Code logic finding gaps that close in seconds. He built the entire system in 48 hours: → Claude Code handles the trading logic → Binance API feeds real-time BTC data → iPad displays multi-market monitoring → Executes trades when arbitrage windows open The system runs 24/7. Every price dislocation = profit opportunity. Most people are still trading manually, refreshing charts, second-guessing entries. Meanwhile this 19 year old engineering student turned $68 into $750K by letting Claude Code do what humans can't: process 50 markets instantly and execute without fear. Why are people still trading manually? I'm giving away the exact Claude Code setup for free. 24 hours only. To get it 👇 1️⃣ Comment " Claude " 2️⃣ Like and Repost 3️⃣ Follow @zakiraicoder (so I can send it via DM)

Reasoning from scratch, round number 6! An introduction (and implementation) of Reinforcement Learning with Verifiable Rewards (RLVR) and Group Relative Policy Optimization (GRPO). 00:00 Introduction 01:54 What makes a reasoning model different? 04:25 Reasoning traces and model capability 08:29 Accuracy and format rewards 11:34 Aha moments and DeepSeek-R1 training 14:41 Reasoning effort and answer length 18:38 RLHF and RLVR 23:04 GRPO vs. PPO 26:40 GRPO explained with a cooking analogy 31:43 The KL term and simplified GRPO 35:04 Loading the pretrained model 36:07 Loading the MATH training data 39:26 Sampling model responses 46:30 Computing verifiable rewards 49:55 Computing advantages 51:54 Token and sequence log probabilities 55:29 Implementing sequence log probabilities 57:37 Fixing the inference-mode error 1:02:24 Computing the GRPO loss 1:04:37 Putting the GRPO step together 1:09:19 The GRPO training loop 1:12:57 Training settings, logging, and checkpoints 1:17:24 Running training and inspecting outputs 1:19:28 Loading and evaluating checkpoints 1:22:33 MATH-500 results and training stability 1:24:05 Memory requirements and next steps

New in The Atlantic: @dgrobinson resigned this week. He was among the longest-tenured employees at OpenAI—and oversaw safety reports on 12 frontier launches. He is very worried: “The time for trial and error is over.” You can read his essay here: