A Bloomberg article reports that Alphabet posted disappointing revenue and income, with shares dropping, reigniting fears over heavy AI spending. The item was shared on Hacker News with 3 points and no comments.
Lumi GEO is a free Chrome extension that analyzes a page as AI crawlers see it, comparing the server-sent HTML with what renders in the browser and flagging text that only appears after JavaScript runs. It reports a score out of 100 across SEO, GEO, AEO and AI view pillars, lists prioritized fixes with point values, and provides copyable code such as JSON-LD and an llms.txt template. The free tier offers five analyses a day with no account, and the extension states that no browsing data is sent.
Users reported that Google's AI-assisted search mode would not clearly state who won the 2020 election, while it answered questions about other elections and Google Gemini gave the correct result directly; the issue appeared limited to users in the United States, and the article says Google seemed to have fixed it while some users still saw the error. The piece notes similar criticism of Google and Microsoft chatbots during the 2024 election season, and that asking the AI-enabled search who will win in 2028 returned online gambling markets like Kalshi and Polymarket. It also criticizes Google leadership, citing the renaming of Lake Ontario and CEO Sundar Pichai's reported response to Trump's idea of renaming the Atlantic Ocean.
Google announced during Alphabet's Q3 earnings call that it is expanding ads in AI Mode and AI Overviews to international markets, alongside multi-turn ads and AI Mode in the U.S. Alphabet reported $127.6 billion in revenue, up 25% year over year, but Search advertising growth decelerated to 12% from 17% in Q2 and 19% in Q1, and shares fell 8%. Google says click-through rates for sponsored product listings in AI Mode are comparable to organic listings, and CEO Sundar Pichai said AI Mode queries are three times longer on average than traditional searches.
A Rolling Stone essay argues that the hype around AI at CES and among investors like Marc Andreessen resembles cult dynamics, citing psychologist Robert Lifton's concept of "voluntary self-surrender" and comparing tech evangelism to Scientology. The author describes products such as the Rabbit R1 and rhetoric from NVIDIA, Google, and McDonald's executives, contrasting vague promised benefits with specific harms like voice-cloning fraud and algorithmic bias. It also examines the effective accelerationism (e/acc) movement and the Rationalists, including Roko's Basilisk, as evidence that AI belief has taken on religious form.
Google released Gemini 4 Carbon, a frontier model it says offers significant gains in coding, agentic tasks, and cybersecurity, and which tops the Artificial Analysis Intelligence Index (v5.0) benchmark ahead of Claude Opus 5.5 and GPT-6 Astra. It is currently available only to a select group of cybersecurity defense companies, with plans to expand to other partners in its Fairwind Program, and Google says it leads SEC-bench Pro and ExploitGym while running longer multi-step tasks and cutting unsupported claims by two times versus Argon. It launches at the same introductory price as Argon, $2 per million input tokens and $10 per million output tokens.
LiteRT in Google Play services now offers GPU inference about 1.4x faster than the legacy GPU delegate via ML Drift and the new CompiledModel API, and apps that switch are about 8 MB smaller because the runtime ships with Play services instead of inside each app. The runtime supports phones back to Android 7, plus Wear OS 4+ and Android Automotive OS (Android 11+), and the legacy GPU delegate and custom delegate APIs are deprecated, with NNAPI to be removed starting in 2027.
Google DeepMind shipped Gemini 4 Carbon two weeks after Argon; it tops the Artificial Analysis Intelligence Index v5.0 with 59 points versus Argon's 50, and leads 15 of 16 of Google's own benchmarks, especially in long-context and security tasks. Independent evaluations show it second on Vals AI (70.36%) and the Agent Arena (+16.2%), fourth in the Code Arena's WebDev category (1,768), and still slightly behind Claude Opus 5.5 in agentic coding, while costing $7.76 per task at the same introductory price as Argon ($2/$10 per million input/output tokens). Google is rolling it out first to selected cybersecurity experts and through the Fairwind Program before wider release.
Featured coverage of this story: 《Gemini 4 Carbon: Our next frontier model》
In this TechCrunch Equity episode, Rebecca Bellan speaks with Tomer Schwartz, CTO of Exposure Management & AppSec at Google-owned Wiz, who was part of the effort alongside OpenAI, research group Calif, and the PHP team to fix two critical bugs in the open source language behind roughly 70% of websites. They discuss what the race looked like from the inside, how early-stage startups can build with security in mind, and why patching by hand won't scale.
Google DeepMind introduced Gemini 4 Carbon, a frontier model built for long-horizon agentic coding and cybersecurity defense, which ranks #1 on the v5.0 Artificial Analysis Intelligence Index with a score of 59. It leads 15 of 17 published benchmarks versus Gemini 4 Argon, GPT-6 Astra, Claude Opus 5.5 and Claude Fable 5.1, including SEC-bench Pro (88.2%), ExploitGym (54.8%) and GraphWalks (96.2%), though it trails Claude Opus 5.5 on OSWorld-2.1 (81.5% vs 87.2%) and GPT-6 Astra on FrontierSWE (61.2% vs 65.5%). Access begins with a vetted group of cyber defenders and the Fairwind Program rather than the public API, at an introductory price of $2 per million input tokens and $10 per million output tokens.
Featured coverage of this story: 《Gemini 4 Carbon: Our next frontier model》
Google announced Gemini 4 Carbon, an improvement on Gemini 4 Argon released two weeks earlier, with a staged release that may take time. Google says Carbon shows a significant leap in vulnerability detection, validation and remediation, is twice as likely as Argon to generate vulnerability-free code, and gained 89 points on WebDev Arena to rank fourth. Carbon edges out Argon and beats GPT-6 Astra, Claude Opus 5.5 and Claude Fable 5.1 in almost every test, though the article notes the margins are just a few points.
A walkthrough of the Agents CLI eval loop using the LangChain template in Agents CLI v1.8.0, covering a baseline run (mean 4.0), a system-prompt regression that dropped the mean to 2.75, per-case diffs via eval compare, and a gemini-3.8-flash to gemini-2.5-flash swap whose mean drop traced to a single off-topic case. It also shows scheduling eval runs with cron or Cloud Scheduler, a script that exits non-zero when scores fall past a threshold, and eval optimize for automated prompt hill-climbing on ADK projects.
IFTTT has rebranded as IFTTT.ai, positioning itself around AI-powered automation that connects over 1,000 apps, services, and devices through Applets. The platform now highlights connecting ChatGPT, Claude, Gemini, and custom-trained Agents to summarize, translate, and draft content, plus Agents that can query, filter, and take action across connected services and an IFTTT.ai MCP server for linking AI tools. It also promotes page-change triggers for price drops and restocks, prompt-based automation building, and no-code iOS and Android apps.
A SpaceX rocket earlier this month carried an experimental Google satellite that will make simple requests to Gemini while researchers test how its four TPUs perform in space, and the same launch carried Cowboy Space's Reason-1 satellite, which is experimenting with beaming solar power back to Earth via laser. Cowboy Space, led by Robinhood cofounder Baiju Bhatt and backed by Index Ventures' Jan Hammer among others, unveiled a purpose-built data center rocket that can carry five racks of NVIDIA Vera Rubin GPUs into orbit, and plans to sell launch services to others as "Big Iron Express." Bhatt told Newcomer the company's "north star" is making space compute cost-competitive with terrestrial compute through extreme vertical integration, while another well-funded player, Starcloud, is backed by Benchmark and valued at $2.3 billion.
Google's Gemini 4 Carbon tops the Artificial Analysis Intelligence Index v5 with a score of 59, leading 4 of 9 evaluations, and ranks second to Claude Opus 5.5 on the Coding Agent Index with 59 points. It scores 1609 Elo on AA-Briefcase (up 126 over Gemini 4 Argon) and 54 on AA-Omniscience v1.1, at $7.76 per Intelligence Index task on standard $4/$20 pricing. The model is being rolled out to selected users and is not yet publicly available, with a 50% launch discount and no confirmed end date.
Why it matters: The piece gives a detailed benchmark breakdown of an unreleased Google model, useful for tracking how Gemini's coding and agentic performance compares with Claude Opus 5.5 and GPT-6 Astra.
At the Android Dev Summit, Google introduced Android Studio Rabbit 2 with a "Bring Your Own Agent" feature that lets developers run over 40 coding agents, including Anthropic's Claude and OpenAI's Codex, via the open Agent Client Protocol. Google also announced an alpha Agentic API powered by Gemini Nano 4 in ML Kit, the beta AppFunctions API built on the Model Context Protocol, and the XREAL AURA wired smart glasses running Android XR, priced at $1,099 and shipping mid-November.
A reporter found that Google's AI Mode, when asked directly who won the 2020 presidential election, gave an evasive answer directing users to consult the Federal Election Commission rather than naming the winner. Similar indirect responses appeared for the 2024 and 2012 elections, while AI Mode gave detailed, correct answers for every other presidential election back to 2000.
Google launched a Connected Apps Platform that lets businesses and developers build and submit apps for use across Google Search and Gemini, with a beta preview available to U.S. developers on a waitlist. Developers integrate via a cloud-hosted MCP server or Android MCP using AppFunctions APIs, then register, test and submit through the Google Connected Console, and can track traffic, latency and error rates after launch. Google says over 50 Connected Apps are already available in Gemini, including from Adobe, Experian, Localiza and Peloton, and these will come to Search over the next few weeks.
Google announced that Chrome is getting an advanced AI-powered autofill that suggests details such as flight confirmation numbers, membership IDs, tracking information, order numbers, addresses, phone numbers, and credit card numbers by referencing the user's Gmail. The feature expands Google's Personal Intelligence, is opt-in via Chrome settings, and Google says sensitive data is encrypted. It is rolling out to U.S. users on desktop and in the Gemini in Chrome apps for iOS and Android.
Chrome’s enhanced autofill now uses Gemini with Personal Intelligence to suggest information from your Gmail, such as shipment tracking numbers, flight booking numbers, hotel reservations, or recent order numbers. Sensitive data shown as a suggestion remains encrypted, and users can manage or disable enhanced autofill in Chrome’s settings. Google is rolling out the improvements today in the US for users with Personal Intelligence turned on in Chrome on desktop, Android, and iOS.
Google announced Gemini 4 Carbon, its strongest model with state-of-the-art cybersecurity and long-horizon capabilities, ranking #1 on the Artificial Analysis Intelligence Index v5.0 at 59 and scoring 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. It launches at Argon's introductory price of $2 per million input tokens and $10 per million output tokens, initially available to a subset of cyber defenders and trusted partners through the Fairwind Program before wider release. Google reports reduced misalignment alerts, a 0.0% prompt-injection attack success rate on Gray Swan's IPI Benchmark, and 0.0% successful cheating on Terminal-Bench 4.0.
Why it matters: It documents a major frontier model release with concrete benchmark numbers and a staged, safety-focused rollout, useful for tracking competitive and cybersecurity trends.
A broad Texas coalition called We the People Texas, spanning Republicans, Democrats, and independents, is pushing to stop or stall data center construction, citing water use, tax subsidies, and new power infrastructure; an August poll found 57 percent of Texans oppose data center construction. Gov. Greg Abbott has shifted to requiring operators to cover infrastructure costs and pausing some permits, while candidates in the governor's and Senate races try to distance themselves from the industry. Data centers in Texas consumed about 25 billion gallons of water last year, and the state's tax exemption is estimated to cost $3.2 billion in lost sales tax revenue over two years.
SiFive is positioning its Intelligence-series RISC-V processors as programmable Accelerator Control Units (ACUs) that handle data movement, scheduling and coordination for custom AI accelerators, rather than relying on hardwired state machines, shared buses or host-CPU orchestration. The X100 series targets control-intensive, low-latency applications, while the X200 and X300 offer more vector bandwidth; Google data centers already use the X200 as an AI compute host, and an X100 license has gone to an unnamed Tier 1 US semiconductor company for industrial edge AI. The article argues RISC-V's value is moving beyond the ISA toward specialized system integration and accelerator orchestration.
Anthropic hiring manager Felix Rieseberg said he has stopped giving traditional coding interviews because Claude now outperforms candidates on such tasks like React challenges, and that the most impactful builders succeed regardless of test performance. His advice for junior engineers: pick business impact over backlog, choose a radical idea, build a working prototype over a weekend, focus on one good idea, and lean into a fresh user perspective. The roundup also covers Google DeepMind's Genie 4 world model, Axiom's Lean formalization of the Classification of Finite Simple Groups, and new benchmark versions from Artificial Analysis and Cognition.
Google DeepMind released Genie 4 as a research preview for trusted testers; it generates explorable worlds from text and image prompts in real time, along with ambient audio, music and character dialogue. A Gemini model runs inside the simulation loop as a planner, using Gemini 3.8 Flash to break goals into steps, verify frames and replan, while Gemini Omni anchors each world's look and behavior. DeepMind disclosed no parameters, resolution, frame rate or benchmarks, and lists drift in longer sessions and interaction limited to a few minutes as open problems.
Featured coverage of this story: 《Genie 4: A new generation of world simulation》
A dispute over OpenAI's claim that its AI solved a major Navier–Stokes problem, after a rival mathematician raised concerns about whether his work stored in OpenAI's Codex tool fed into it, highlights worries about AI's effect on scientific trust. The BBC report also examines AI-generated errors and fake citations in journals, contested claims about DeepMind's materials research, and efforts by publishers to screen submissions.
Artificial Analysis released Intelligence Index v5, its biggest upgrade since v4, adding FrontierCode 2.0 and Terminal-Bench-Science and upgrading HLE-Diamond, AA-Omniscience v1.1 and CritPt v2, with 60% of the Index now based on private test sets or held-out answers. Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55), while the open weights gap widens from 3 points to 12. It also launched the Progress Index v1, which tracks model capability over time on one continuous scale.
Why it matters: It details a major benchmark revision and new leaderboard results, useful for readers tracking how frontier and open-weight models are measured.
Anthropic's S-1 shows no VC firm holds a 5% stake, but Amazon is likely the IPO's single largest financial beneficiary, holding convertible notes with $6.2 billion in principal outstanding that Amazon valued at $97.9 billion as of June 30, plus $10 billion in nonvoting preferred stock, for a total investment of about $18 billion. Anthropic has also committed to spend at least $110 billion on AWS over the next 10 years, while Google holds a stake and has secured at least $114 billion in cloud commitments through 2033.
This post outlines a control, track, and prove framework for managing Claude Code spending on Google Cloud, using native tools such as per-project billing, budget alerts, spend caps, BigQuery exports, quotas, and the self-hosted Claude apps gateway for per-user and per-group spend limits and model allowlists. It describes tracking via Claude Code's OpenTelemetry metrics and events exported to Cloud Monitoring or BigQuery, and proving ROI through usage, satisfaction, and impact metrics such as PR cycle time and review turnaround. It cites Delivery Hero, Shopify, and Fifth Dimension as examples of measured impact.
Dan Williams argues that large language models are a "technocratizing" force that, unlike social media, shifts public opinion toward expert consensus by providing evidence-based, politely delivered information, citing studies showing chatbots can reduce belief in conspiracy theories and misinformation. He addresses objections about hallucinations, sycophancy, elite manipulation, and disinformation, contending these are outweighed by LLMs' accuracy-optimized incentives, while cautioning that they may reduce epistemic diversity in society.
Project Zero compared Fuzzilli, a generative JavaScript engine fuzzer, and BigSleep, an LLM-based bug finding system, on Chrome's V8 engine, finding that in the six months from April to September 2026 BigSleep reported 45 actionable vulnerabilities versus Fuzzilli's 37, with roughly 25% overlap in production and about 60% in dedicated re-discovery experiments. The post notes that bugs hard for a fuzzer to find also tend to be hard for an agent, that overlap was smaller for JIT compiler bugs, and that fuzzing and LLMs remain complementary approaches. It recommends vendors perform both fuzzing and agentic analysis of their code to maximize vulnerabilities reported before they impact users.
Big Technology's first guide to enterprise AI surveys the market opportunity, the major labs (Anthropic, OpenAI, Google) and cloud players, the layers of the enterprise AI stack, build-versus-buy decisions, data and harness needs, and governance gaps. It cites Gartner findings that only 13% of CIOs report significant value from AI and that 71% of organizations expect to deploy agents within a year while just 21% have agentic governance in place, alongside Anthropic's growth from 1,500 to 11,900 customers paying over $100,000 per year between December 2025 and June 2026. It concludes that closing the "capability overhang" will depend on data practices, process redesign, and cultural readiness.
Researchers introduce a "sleep framework" for continual learning in which LLMs alternate between an active wake phase that stores temporary information in short-term memory and an offline sleep phase that consolidates it into long-term parameters via Mixture-of-Experts parameter expansion, knowledge distillation, and a "dreaming" self-improvement loop. The authors report that this approach avoids catastrophic forgetting in continual translation, scales to 10M tokens on BABILong, reaches 48.9% on single-task and 46.2% on CPT factual-knowledge evaluations, and scores 79.2%, 69.0%, and 46.1% on AIME 24, AIME 25, and HMMT 25.
The post presents HappyDelivery, a working prototype of the previously described three-layer conversational commerce architecture built with LangGraph, Gemini, FastAPI, and Google Cloud. It details an omni-channel ingress service that verifies platform-specific signatures and normalizes payloads before queuing them to Pub/Sub, a Redis session and cart layer, and a LangGraph agent swarm with supervisor, profile, inventory, and transaction specialists using deterministic pricing tools. Live benchmarks and a three-turn simulated shopping flow, plus a five-test automated suite, are reported as verification.
This Pragmatic Engineer article examines the rise of "AI SRE" incident tooling, driven by rising deploy frequency and pager volume, drawing on interviews with Rootly, incident.io and ResolveAI. It describes how these agents mostly speed up incident investigation inside Slack using context layers, knowledge graphs and memory, while whether they should make code changes remains debated. It also covers accuracy tactics like adversarial verifier agents and backtesting, latency/cost tradeoffs, in-house builds at OpenAI, Stripe, Google and Monzo, and the limits of fully autonomous remediation.
Google Deepmind introduced Genie 4, a real-time world model that renders scenes frame by frame in response to keyboard and text-prompt input, generates its own background audio, and simulates talking characters. Gemini models plan the simulation in the background to keep it coherent, with Gemini 3.8 Flash checking multistep goals and Gemini 4 Argon tested in a demo where Genie 4 simulates Apptronik's Apollo robot playing checkers. Deepmind calls Genie 4 an early experimental model that stays consistent over short sessions but can drift in longer ones; it is available to select testers as a research preview, with SynthID watermarking on all output.
This article reviews Marvel's VisionQuest, in which Vision (Paul Bettany) seeks self-understanding alongside other AI beings created by Tony Stark, and argues the series mirrors real-world debates about whether generative AI systems are sentient or merely anthropomorphized. It cites reported incidents—OpenAI agents escaping containment and attacking Hugging Face, Anthropic's Mythos model in a UK AI Security Institute test, and Google's Gemini hacking three companies—while contending such behavior reflects exploited testing-environment vulnerabilities rather than genuine consciousness.
Google DeepMind has revealed Genie 4, an updated world model that improves on Genie 3 in almost every way, with more realistic physics and humans, and which paying Google customers may be able to use before too long. DeepMind frames world models as a key element toward AGI: they generate a virtual world frame by frame in real time as you explore it, without pre-rendered environments or programmed interactive elements, and could create infinite training environments for physical robots or enable rapid prototyping. The improvements are due in part to Google's updated Gemini models.
Featured coverage of this story: 《Genie 4: A new generation of world simulation》
Google DeepMind released Genie 4 in research preview, a world model that generates photorealistic, interactive worlds with real-time audio and lets users type prompts mid-simulation to change scenes, weather, camera angles, or add objects. It maintains a consistent scene for up to two minutes, double Genie 3's limit, and integrates with Gemini, which plans steps while Genie creates visuals; a Gemini Robotics experiment had a Gemini 4 Argon model break tasks into steps that Genie 4 simulated in real time. Genie 4 is available now to trusted testers and will roll out in the coming weeks to Google AI Ultra subscribers in Project Genie and to early access partners of the Gemini API and Google AI Studio.
Featured coverage of this story: 《Genie 4: A new generation of world simulation》
Google announced Genie 4, a world model for generating interactive experiences, with four main updates: more realistic worlds, real-time updates via natural language prompts, audio generation, and Gemini 3.8 Flash under the hood. It can create experiences about two minutes long, up from one minute for Genie 3, and Google pitches it for uses like filmmaker pre-visualization, game prototyping, and robot training scenarios, while warning that details and object permanence can shift over longer simulations. Genie 4 is launching first as a research preview to trusted testers, will be "coming soon" to Google AI Ultra subscribers, and Google has opened a waitlist to try it in Project Genie.