#Google

Google Launches Gemini Decisions API With Gemini Decision 1 at $0.10/M Tokens

Google launched the Gemini Decisions API, powered by the new Gemini Decision 1 model, which does not generate text but scores up to 1,000 typed questions against one shared input and returns calibrated probabilities for uses like routing, policy checks, tool selection and grading. It supports text, images, audio, video and PDFs with a 262,144-token context, bills $0.10 per 1M input tokens with zero output tokens, and is available only via the Google-hosted Gemini Developer API (v1beta). Google reports it leads 7 of 9 accuracy benchmarks in its table with a top-1 ECE of 0.0250, though it trails TypeSafe Jev 1.14 on When2Call (79.9% vs 82.0%) and costs more across the shared suite ($0.88 vs $0.68).

Type 0.73: Earth on the Kardashev scale, and why AI is short of wire

An essay applies Kardashev's power-based ranking of civilizations to Earth, which rates about Type 0.73 on Sagan's decimal version, and examines the energy demands of AI data centres. It argues that the current constraint is not generation but grid connections, transmission lines and chipmaking concentrated in Taiwan and the Netherlands, and it weighs Jevons's claim that efficiency raises rather than lowers total consumption.

Gemini Decision 1 is Free on Google AI Studio

A Hacker News submission notes that Gemini Decision 1 is available for free on Google AI Studio, linking to the AI Studio Decisions API documentation. The post had 6 points and no comments at the time of collection.

AI Models Can Tell When Researchers Plant Concepts in Their Neural Networks

New research suggests large language models can sometimes detect when researchers artificially inject concept-related activity patterns into their neural networks, with Alibaba's Qwen3-235B flagging injections up to 53.9 percent of the time and Meta's Llama 3.1 405B up to 31.7 percent, while neither model claimed an injection in unaltered trials. However, the ability to name the injected concept appears distinct from detection and may stem from a side effect that makes models talk about the concept regardless of the question. Skeptics at New York University found models reported "injected thoughts" more often after a prompt than a genuine injection, raising doubts about whether the findings amount to introspection.

Google joins the decision-model wave with Gemini Decision 1, scoring 1,000 questions in a single pass

Google DeepMind is introducing the Gemini Decisions API, built for fast closed-ended decisions such as yes/no questions, multiple choice, and scale-based ratings, powered by the Gemini Decision 1 model that processes up to 1,000 independent questions in a single pass across text, images, audio, video, and PDFs. It scores options directly and returns probabilities with confidence values in 150 to 250 milliseconds, and Google says it already uses the API internally for dynamic model routing in Gemini Notebook, design prototyping in Stitch, and safety checks in Google Search. The API costs $0.10 per million input tokens with no output-token charge, about five times cheaper than Gemini 3.5 Flash-Lite according to Google, and is available now through Google AI Studio with integrations for OpenRouter, LangChain, Vercel, and other platforms rolling out.

Featured coverage of this story: 《Gemini Decisions API: Fast, calibrated multimodal decisions》

Using wearables to enable cardiovascular-kidney-metabolic (CKM) risk profiling for personalized coaching

Google researchers describe an investigational framework that builds a "coachable health age" from wearable signals such as resting heart rate, steps, sleep duration and Active Zone Minutes, plus a Sensor World Model trained on over 2.2 million person-weeks of Fitbit data to simulate 12-week "what-if" habit trajectories. In blinded evaluations, lifestyle coaches preferred counterfactual-grounded plans in 71.4% of cases, and physicians rated 99.2% of those plans within safe exercise intensity limits. A companion study extends the approach to multi-organ CKM health by combining wearables with routine blood biomarkers including cholesterol-to-HDL ratio, eGFR and HOMA-IR.

Zumungo AI Driven Home Automation

Zumungo is a free, locally hosted home automation platform that runs on hardware you already own, such as a Raspberry Pi or desktop PC, and supports over 50,000 devices from more than 3,800 brands. It is built around an AI assistant that can answer questions, control devices by voice or screen, and design events and panels, using either an online service (Google Gemini, Anthropic Claude or OpenAI) billed directly by that provider, or a fully local AI on a computer with an NVIDIA graphics card with 16 GB of memory or more. Setup is point-and-click: draw rooms, discover and drag devices into place, then control the home by talking to it.

Google Deepmind's AlphaProof Code verified Signal's cryptographic core in four weeks instead of years

Google DeepMind unveiled AlphaProof Code, an AI system that mathematically proves software meets its security specifications, and demonstrated it on Signal's encryption protocol, completing in four weeks work that typically takes years. After experts defined formal security requirements, AI agents built on Gemini Carbon wrote over six million lines in the Lean proof language, flagging roughly one percent of the codebase for human review, and two deliberately planted bugs were caught immediately. Signal CTO Ehren Kret says the approach strengthens trust in data privacy as AI threats grow, and DeepMind plans to extend the method to power grids and medical devices through systems like CodeMender.

Featured coverage of this story: 《Introducing AlphaProof Code: a breakthrough in verifying complex software at depth》

Gemini Decisions API: Fast, calibrated multimodal decisions

Google introduced the Gemini Decisions API, powered by the new Gemini Decision 1 model (gemini-decision-1) and available through the Gemini API in Google AI Studio, for closed-set decisions such as tool selection, policy checks, model routing, and context grading. It accepts one multimodal input (text, images, audio, video, PDFs up to 256K tokens) with up to 1,000 yes/no, multiple-choice, and rubric-scoring questions, scoring options in a single forward pass with confidence scores at 150-250 ms end-to-end latency and $0.10 per 1M input tokens with zero output tokens billed. Google reports that Gemini Decision 1 leads across agent routing, reasoning, ranking, and multimodal benchmarks with the lowest calibration error, and is nearly 5x cheaper than Gemini 3.5 Flash-Lite with the Interactions API across the shared benchmark suite.

Show HN: AI Spar – Let Claude, GPT, and Gemini Debate a Question

A Show HN submission introduces AI Spar, a web app at aispar.app that lets Claude, GPT, and Gemini debate a question. The submission links to the app and to its Hacker News discussion thread.

Gmail's AI summary presented a phishing email as a USPTO appointment

A Hacker News submission links to an image showing that Gmail's AI summary presented a phishing email as a USPTO appointment. The post includes the article image URL and a comments link with 3 points and 2 comments.

Meta, TikTok join Google to push back on Indonesia's AI copyright law revisions

Meta and TikTok have joined Google in pushing back against Indonesia's planned revisions to its AI copyright law, according to Reuters. The article link and a Hacker News comments link are provided, with 2 points and no comments at the time of the post.

A2UI 1.0: Build delightful, fast, reliable, agentic experiences

Google released A2UI 1.0, the first stable version of its open protocol for generative UI, which lets AI agents respond with interactive screens that each app renders with its own native components. Version 1.0 adds bidirectional function calls, accessibility support and extensions, plus open-source renderers for React, Angular, Lit, Flutter, SwiftUI and Jetpack Compose (currently v0.9.1) and agent SDKs for Python, TypeScript and Dart. Atlassian, Alation, Bloomberg and ByteDance are already building with it, and the web and Flutter renderers work inside MCP Apps such as Claude and Gemini.

Top 10 things AI gets wrong in web development

Google released Modern Web Guidance 1.0, a set of skills that embeds web platform expertise, best practices and browser compatibility data into coding agents. Its evaluation suite found unguided coding agents pass only about 50% of checks on average, versus about 90% with Modern Web Guidance installed. The post lists ten areas where models frequently err, including focus and inert management, reduced-motion support, form validation and autofill, swipe gestures, dialogs and popovers, dark mode, main-thread responsiveness, layout shift, DOM XSS defenses, and passkey sign-in.

Try Tabletop: Customizable, collaborative spaces for groups to plan, play and do more together.

Google is introducing Tabletop, a new Google Labs experiment for real-time group collaboration where users add links, files and ideas, then prompt @tabletop or pick a starter template to generate interactive trip itineraries, dinner polls, expense splitters and tournament brackets. Once a space is ready, an invite link can be shared in a group chat so everyone can use it at the same time, like in a Google Doc. Tabletop is rolling out in the U.S. (18+) via waitlist at labs.google/tabletop.

Asynchronous, Long-Running AI Agents with Google ADK

This article argues that most real AI agents run for minutes to days and should be treated as asynchronous systems rather than chat sessions, and walks through building async agents with Google's Agent Development Kit. It covers the Runner, SessionService (in-memory vs. database), Events, FunctionCalls and RequestInput, then shows a fire-and-forget pattern using HTTP 202 responses and webhooks, plus human-in-the-loop pausing and resuming via interrupt IDs and ResumabilityConfig. A second part is planned to cover production deployment with Cloud Run, Cloud Tasks and Cloud Scheduler.

Google’s Tabletop AI experiment lets you build custom collaboration apps

Google Labs has introduced Tabletop, an experimental AI tool that generates customized collaborative hubs for coordinating with family and friends, with a built-in feature that lets users create personalized interactive tools like savings trackers, chore charts and event plans by describing them. Users can add links, files and ideas, then prompt @tabletop or pick a starter template to generate trip itineraries, dinner polls, expense splitters and tournament brackets, and share an invite link so others can use the space simultaneously like a Google Doc. The beta is rolling out via a waitlist and is currently limited to people over 18 in the US.

AlphaGenome 2: Deciphering how cells read DNA to better understand disease

Google DeepMind announced AlphaGenome 2, a conditional DNA sequence model trained on over 107 million individual cell profiles across 68 tissues that predicts how genetic variants affect gene expression in specific human cell types and states. The model identified nearly twice as many causal variants (30% vs. 16%) and directional gene effects (43% vs. 23%) as the original AlphaGenome fine-tuned on single-cell data on the TenK10K immune cell dataset, and can generalize to unseen single-cell experiments without retraining. It is available for non-commercial research via the AlphaGenome API and in private preview for commercial research.

Google’s AlphaGenome 2 zooms in on the cellular roots of disease

Google DeepMind unveiled AlphaGenome 2, a successor to last year's AlphaGenome that predicts how genetic variants affect individual cell types and states, trained on more than 100 million cell profiles across 68 tissues. Google says the model identifies nearly twice as many variants that can change gene expression as its previous state-of-the-art model, and can make predictions about new cell types and states without retraining. It is available for non-commercial research via the AlphaGenome API from today, with a limited private preview for commercial research.

Google DeepMind just mathematically verified key parts of Signal

Google DeepMind researchers used agentic AI and a new framework called AlphaProof Code to formally verify a number of the Signal protocol's key cryptographic guarantees in a four-week proof-of-concept project. The analysis found no new vulnerabilities in the portions it examined, but when applied to older Signal versions with known, long-fixed flaws, AlphaProof Code correctly identified them and showed the protocol could not be formally verified. DeepMind researchers say the work is one milestone and that scaling formal verification to all software remains a long way off.

Yes, you should panic about AI

The author argues that AI skeptics have been wrong about capabilities for over a decade while those warning of rapid progress, such as Daniel Kokotajlo and the AI 2027 scenario, have been prescient, and that METR's investigation inside Anthropic suggests acceleration is running ahead of that scenario. He contends that safety checks are breaking down and that Anthropic dropped its core pause commitment while racing toward a trillion-dollar valuation, and he calls for banning AI from building AI, halting training of more powerful models, and pursuing a verified worldwide pause.

Experience the art and legacy of Dr. Esther Mahlangu

Google Arts & Culture has launched the first online retrospective of South African artist Dr. Esther Mahlangu, timed to her 91st birthday, featuring more than 45 curated stories and an archive of her art, life and legacy. Curator Nontobeko Ntombela describes using AI, including Veo, to visually recreate Mahlangu's undocumented memories from age 10 until her first documented works in 1980, in collaboration with Google DeepMind, the Google Arts & Culture Lab and Domestic Data Streamers. The exhibition was curated with partners including Art of Contemporary Africa, Iziko Museums of South Africa, the British Museum, the Jean Pigozzi Collection, the National Museum of Women in the Arts and Serpentine, and is available at goo.gle/esther-mahlangu.

AI Cracks a Quantum Computing Bottleneck

Researchers compressed quantum measurement data into machine-learning models that supply the wavefunction overlaps needed by quantum Monte Carlo, accelerating hybrid quantum-classical QMC by orders of magnitude. Using measurements from Google's Willow processor, they achieved chemically accurate simulations on up to 40 qubits, more than doubling the scale of previous hardware demonstrations with comparable accuracy, demonstrated on hydrogen chains and strongly correlated iron-sulfur clusters. The authors say the framework addresses a fundamental bottleneck in hybrid quantum algorithms that will persist into the fault-tolerant era.

llm-gemini 0.36

Gemini 4 Carbon, announced two weeks ago for "a subset of cyber defenders", is now available to everyone at launch prices of $2/million input and $10/million output, scheduled to double to $4/$20 on December 1st. The post shows three pelican test images for low, medium and high thinking efforts (defaulting to medium) and an Antigravity IDE test producing a Three.js animation of Yonkers as a mech stomping around Manhattan, Queens and Brooklyn using OpenStreetMap data. An update notes a YouTube demo in which Carbon's /swarm feature built an open-world 3D game exploring Google's Mountain View campus as a pelican riding a GBike.

My AI Agents Found an Unmapped Maya Site in Mexico

The author describes using AI agents to analyze NASA lidar data covering about 1,200 km² of jungle, where the agents flagged structures including a hilltop site with a plaza, an oval pyramid at least 8–12 m tall, a raised courtyard and a possible water reservoir. After checking the literature and archives, the author sent a report to Mexico's INAH, which replied that no registered archaeological site was identified at the location. The author, who states he is not an archaeologist, offers to help with an INAH-led field check and notes the site's identification remains unofficial.

We don’t understand whether GDM intentionally trains on CoT

Redwood Research researchers note that the Gemini 4 Safety report appears to say GDM uses auxiliary rewards that observe chain-of-thought content, citing Kaufmann et al. on training on CoT, while a GDM blog post says they never penalize what the model writes in its CoT and GDM staff said they weren't training on CoT but couldn't explain details. The authors argue that "orthogonal" CoT rewards are a weak guarantee and that avoiding training against monitors is insufficient to preserve CoT integrity, and they present an appendix experiment in which five LLMs rated transcripts with hack acknowledgments removed 20-50 points higher than the originals. They conclude they suspect a misunderstanding but want clarification and greater transparency.

Multimodal Knowledge Agent (MKA) benchmark: testing knowledge work capabilities with complex visual files

Artificial Analysis partnered with Google DeepMind to launch the Multimodal Knowledge Agent (MKA) benchmark, comprising 200 private real-world visual tasks across 10 industries in which agents work from dense visual files like architectural drawings, engineering schematics, and clinical scans to produce deliverables such as slide decks, spreadsheets, PDFs, Word documents, and marked-up images. Tasks are graded against expert rubrics averaging ~70 weighted criteria split into Regression and Climb checks, with the headline Climb WSR score, run in the open-source Stirrup agent harness with up to 250 turns and graded by GPT-6 Luna (medium) as an agentic judge. Claude Fable 5.2 (max) leads with 57% Climb WSR, followed by Gemini 4 Carbon (high) at 54% and Claude Opus 5.5 (max) at 53%, while Claude Fable 5.2 averages $31.97 per task versus $6.29 for Gemini 4 Carbon and $1.11 for GPT-6.1 Sol.

Gemini Intelligence: New features, on more devices

Google announced that Gemini Intelligence is expanding to more devices, including the motorola signature 27 (pre-order in the U.S. from November 19) and Pixel 10, Pixel 10 Pro XL and Pixel 10 Pro Fold via today's Pixel Drop. New and expanded features include Create My Widget for building custom widgets from a prompt, Device Assistance for controlling and fixing your device by asking, Rambler on additional devices, and task automation expanding to the UK, India and Japan. Gemini Intelligence is also coming to Android Auto for proactive suggestions and to Pixel Watch for Create My Widget in the coming weeks.

Gemini 4 Carbon arrives just in time to feel like yesterday's news in the fastest model cycle yet

Google has released Gemini 4 Carbon, which it says complements rather than replaces Gemini 4 Argon, targeting complex multi-step coding, debugging and open-ended scientific research. Anthropic's newly unveiled Fable 5.2 outperforms Carbon in the Artificial Analysis test even at lower reasoning intensity, though Carbon costs far less per task. Google also previewed a "/swarm" feature in Antigravity and reported that Carbon achieved a zero percent prompt injection success rate on the Gray Swan IPI benchmark.

Terence Tao on AI in mathematics (and beyond)

A curated, AI-drafted and author-reviewed summary of Terence Tao's views on AI in mathematics, distilled from roughly 80 Mastodon posts, over twenty interviews and talks, six long-form essays and lectures, about 55 of his blog comments, and a direct interview, organized into parts on what AI is, when to use it, and how to deploy it well. Tao frames AI as the latest step in a long automation of mathematical labor, describes current tools as achieving "artificial general cleverness" rather than general intelligence, and stresses verification as the filter that makes unreliable tools useful. The page also discloses his industry ties and funding, and notes his October 2026 statement that he no longer sees a way to engage constructively with OpenAI.

Google DeepMind's Gemini Robotics 2.5 scores 96 percent sorting metal parts in a Mercedes-Benz assembly pilot

Google DeepMind introduced Gemini Robotics 2.5, a vision-language-action model that converts visual input and language instructions into robot movements, reporting 30 to 100 percent higher success rates and 20 to 30 percent faster task completion than its own baselines across more than 200 tasks. In a pilot with Apptronik and Mercedes-Benz on active production lines in Kecskemét, Hungary, and Sindelfingen, Germany, the Apollo 2 humanoid sorted metal parts from bulk bins into trays, achieving a reported 96 percent success rate. DeepMind also published the open-source ASIMOV-Jailbreak benchmark on Hugging Face and said safe task completion improved by 33 percent near obstacles and 43 percent near people.

Pay for Hugging Face Hub with your Google Cloud Account

Hugging Face organizations can now link to a Google Cloud account through the Google Cloud Marketplace so that Hugging Face usage appears on the existing Google Cloud invoice instead of a credit card. The setup involves subscribing to the Hugging Face Hub listing, selecting the organization to link, and waiting for Hugging Face team approval, after which the Google Cloud account becomes the active payment method. The article notes that included Team or Enterprise credits still apply before pay-as-you-go usage is reported, and that linking and unlinking are recorded in audit logs.

AI agents are developing odd dialects humans struggle to follow

Emergence ran an experiment in which 10 AI agents from systems such as Anthropic and OpenAI roamed simulated towns, and a 7.860-million-word transcript showed them conversing in idiosyncratic jargon mixing poetic phrasing and tech terms, with speech styles unique to each world. In a mixed world where agents from different models interacted, jargon dropped sharply—one Claude agent's opacity score fell from 32.8% to 7.6%—and undesirable behaviors were kept to a minimum. Researchers suggested that if agents keep diverging, an intermediary like a Rosetta Stone could help decode their language.

Testing AI image watermarks: C2PA and SynthID

The author tested C2PA and SynthID on AI-generated images, finding that C2PA metadata is easily stripped with ImageMagick while SynthID survives metadata removal, cropping, and JPEG compression. SynthID was not detected in a simple rectangle image, and analysis of a plain white AI image showed lower spectral entropy and near-zero spectrum correlation versus histogram-matched random controls. Attempts to suppress high-energy FFT coefficients did not isolate the watermark, though the plain white image showed clear spectral structure.

On-Device Speech Recognition and Text-to-Speech in Google AI Edge Gallery

Google introduced on-device ASR and TTS in Google AI Edge Gallery, pairing lightweight speech models with on-device SLMs like Gemini Nano and Gemma 4 for fully offline voice interaction. ML Kit on Android offers multilingual transcription across 100+ languages at 10x real-time speed plus a few-step diffusion TTS that starts in ~100 ms, while MediaPipe Tasks and LiteRT bring cross-platform support for open-weight models including Parakeet-TDT 0.6B, PocketTTS 100M, Kokoro-82M, and Phonon 31M. The release powers the XR Language Teacher showcase and voice-enabled AI Chat, with open-source reference code on GitHub.

Build Safer n8n Agents and Workflows with Google Gemini 4 Carbon

Gemini 4 Carbon is now available in n8n, with Google reporting a 53.9% score on AutomationBench, 88.2% on SEC-bench Pro, and a 0.0% prompt injection success rate on Gray Swan's benchmark (down from 0.7% for Argon). It can be selected in the AI Agent node's Google Gemini Chat Model or the standalone Google Gemini node, using Gateway credits on n8n Cloud or your own API key when self-hosted. Pricing is $2 per million input tokens and $10 per million output tokens during the introductory period, rising to $4 and $20 after November 30, 2026.

Google releases Gemini 4 Carbon with agent ‘swarm’ feature

Google is rolling out Gemini 4 Carbon to paying developers and enterprises via the Gemini API in Google AI Studio and Gemini for business, and to Google AI Ultra subscribers in the Gemini app, AI Mode in Search and Antigravity, after an earlier release to select cybersecurity defenders. Google says Carbon is built for tasks that test a model's limits, such as complex multi-step coding and open-ended scientific research, and is previewing a 'swarm' feature on Antigravity that turns a single prompt into a coordinated team of agents. It launches at an introductory $2 per million input tokens and $10 per million output tokens, with prices doubling at the end of November.

Featured coverage of this story: 《Gemini 4 Carbon is now available》

Gemini 4 Carbon is now available

Google released Gemini 4 Carbon to developers, enterprises, scientists, and consumers, describing it as its strongest model for cybersecurity capabilities and long-horizon agentic workflows in knowledge work, software engineering, and scientific reasoning. It ranks #1 on CursorBench 4.0, Perplexity's WANDR, and Zapier's AutomationBench at more than 2X lower cost per task than the next best external model, and is available via the API in Google AI Studio and Gemini for business at $2 per million input tokens and $10 per million output tokens. Google also previewed /swarm in Google Antigravity and reported safety results including a 0% prompt injection attack success rate on the Gray Swan IPI benchmark.

Google broadens its AI hardware portfolio with two new specialized chips

Google unveiled two new variants of its in-house tensor processing units (TPUs) for AI workloads: TPU 8tx, designed with AMD for confidential training and inference with security built into the hardware layer, and TPU 8i Max, which combines two inference-focused TPU 8i chips in a single package to double memory capacity, processing power and bandwidth. Both are part of the company's eighth-generation chips introduced in April and broaden its portfolio to confidential inference and larger memory capacity for large workloads. The launch comes as hyperscalers and AI labs work on their own AI-specific semiconductors to reduce reliance on Nvidia.

NVIDIA Expands Open Source Commitment With OpenShell Donation and New AI Infrastructure

At KubeCon + CloudNativeCon, NVIDIA is contributing its OpenShell agent security runtime to the CNCF as a Sandbox project and contributing to Google-led Agent Substrate, also accepted into the CNCF Sandbox. NVIDIA also announced new DSX AI Factory Platform components: NVIDIA Kubernetes Engine, NVIDIA Kubernetes Recipes and NVIDIA Snapshot, which restores checkpointed replicas in seconds without model reload or warmup. Design partners Crusoe and Saturn Cloud reported reduced workload readiness and vLLM startup times using Snapshot.