All AI updates

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.

How Baseten and Genspark trained a deep research model to outperform Opus 5.5

Genspark worked with Baseten Applied Research to post-train Qwen3.5-397B into a specialized Deep Research model that scores 0.759 on Genspark's quality grader, up from 0.612 for the base model and above Opus 5.5 (0.738), Sonnet 5.5 (0.742), GPT-5.6 Sol (0.637), Kimi K3 (0.724) and DeepSeek V4 Pro (0.675). The recipe splits the task into research, trained with reinforcement learning against a shaped semi-verifiable reward, and synthesis, trained mainly with supervised fine-tuning on high-quality reports written from the model's own research trajectories, followed by end-to-end RL. The model crawls 5× more sources per query than the base model and runs with NVFP4 weights on 2×B200 GPUs per replica, now serving as the default for Genspark's Deep Research agent in production.

Introducing Businesses in ChatGPT

OpenAI introduces Businesses in ChatGPT, letting businesses manage their business information, get insights about how their business shows up, and turn interest into action in ChatGPT.

Autonomous treasury agents: How Trovata built an AI-native solution on Amazon Bedrock and AgentCore

Trovata, an AI-native cash and liquidity management platform connected to over 10,000 banks, describes how it built autonomous treasury agents on Amazon Bedrock and Amazon Bedrock AgentCore, using role-based access control and a verified API layer within a bank-branded deployment model. The post outlines use cases such as cash forecasting, natural language querying, anomaly detection, automated reconciliation and persistent agentic workflows, and cites customer results including $1M+ in annualized cost savings, 5+ hours per day saved per accounting team, a 77 percent reduction in annual TMS costs, and reporting reduced from hours or days to under a minute. It also notes a library of ten agent patterns from real customer deployments and Trovata's plans to expand agent patterns, ERP integrations and global bank connectivity.

How Curative is building a national provider network with agents on Claude

Curative built a hierarchy of agents on Claude, led by a provider-contracts agent named Gwen, to sign provider contracts roughly 50 times faster than its team did in 2025, reaching 2,000 to 2,400 new contracts a week versus 2,100 in all of 2025. Gwen handles one provider per run on Claude Opus, with a Sonnet rate reviewer, a Haiku judge that checks every outbound message, and escalation to humans for non-standard pricing. The company's contracting team grew from 45 to about 55 people, each about 2.5 times as productive, and its agents run on bespoke claims, prior authorization and finance systems that 13 engineers rebuilt with Claude Code.

0.167.1

This page is a GitHub changelog entry titled "0.167.1" and consists mainly of site navigation and product menu links, with no details about the release itself.

Introducing Scribe v3 and v3 Live, our most accurate speech to text models

ElevenLabs launched Scribe v3 for recorded audio, which makes 27% fewer word errors than Scribe v2 across 102 languages and has the lowest diarization error of any model tested, and Scribe v3 Live for realtime audio, which makes 43% fewer semantic word errors than Scribe v2 Realtime and adds speaker diarization and improved turn detection. Scribe v3 Live is available today in ElevenAgents, and both models are available in ElevenAPI, where switching from Scribe v2 is a one-line change to the model ID (scribe_v3 or scribe_v3_live). Both run in the US by default with isolated EU, India, and Singapore environments, Zero Retention Mode, and SOC 2, ISO 27001, PCI DSS Level 1, and HIPAA-eligible coverage.

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.

Amazon Bedrock tokenomics data lake

AWS describes a centralized observability data lake that pulls Amazon Bedrock model invocation logs from linked accounts into a central account via CloudWatch subscription filters, processes them through Lambda and Amazon Data Firehose into Parquet in S3, and exposes them through Athena as a single queryable table. Per invocation, the flattened table captures the prompt, response, input and output token counts, model, stop reason, account, timestamp, and IAM role, and can be joined to CUR 2.0 data on the IAM ARN for per-invocation cost attribution. The solution is deployed with two CloudFormation templates from the AWS Sample Bedrock Invocation Data Lake GitHub repository, with an optional Amazon Quick Sight dashboard.

How TCS built an agentic test orchestrator with Amazon Bedrock AgentCore

TCS built a multi-tenant Agentic Test Orchestrator on AWS that turns Jira stories, BRDs, Swagger specs, and GitLab sources into test scenarios, Gherkin files, and executable Java or Python tests, using specialized agents hosted on Amazon Bedrock AgentCore runtime with the Strands Agents SDK. The platform separates a shared control plane from per-tenant application planes with isolated Cognito user pools, KMS keys, S3 buckets, and DynamoDB tables, and routes API tests to Amazon ECS on Fargate and Selenium UI tests to an EC2-backed ECS cluster. TCS reports that in an internal benchmark of about 100 scenarios, average effort fell from roughly two hours to about 14 minutes per scenario, an 88 percent reduction.

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.

More ways to work with Claude: Sheets, Whiteboards, and Interactive Connectors

Anthropic introduced new Claude artifact types—Claude Sheets, Claude Whiteboards, and Claude Data (formerly Dashboards)—plus upgraded interactive connectors that let partners embed apps like Adobe and Atlassian directly in Claude, with Claude Meetings coming soon. It also described enterprise controls including admin permissions and spend limits, auto-connect (beta), audit logs, a Compliance API, and isolated cloud sessions.

Your apps can now open in Claude, and Claude can work in them

Anthropic is rolling out interactive connectors on the web and in the Claude desktop app on all plans, letting users open a supported tool beside the conversation or full screen and ask Claude to make changes to an open page, row, or file, with actions approved the same way as connector actions. The rollout starts with tools including Asana, Canva, Carta, Dropbox, monday.com, Smartsheet, Vercel, and Webflow, with Figma, Harvey, and Shopify building integrations. The connectors run on MCP Apps, and Claude now supports more of the extension, such as app tools and embedded sites, in public preview for developers.

Introducing Claude Whiteboards, Claude Sheets, and Claude Data

Anthropic launched Claude Whiteboards and Claude Sheets in beta and made Claude Dashboards generally available as Claude Data, all on paid plans. Whiteboards lets teams brainstorm with Claude on a shared board and export to tools like Lucid, Miro, or Vani by Zoho; Sheets generates and edits spreadsheets with tabs, formulas, formatting, and drop-downs, and can export to .xlsx or CSV. Claude Data adds certified queries, data stories, live sharing, design systems, caching, and tracked changes, with partners including Amplitude, Mixpanel, Omni, PostHog, and Sigma.

Meet Mossy: designing a voice you can see

A Sierra designer used AI to take a project from idea to production in just two weeks, according to this post. It also shares learnings from the process.

How to Evaluate AI Agent Cybersecurity Capabilities

Wiz Research outlines the principles behind the relaunch of its Cyber Model Arena, a benchmark of 26 models, four general-purpose harnesses, 59 agents and 327 challenges across code vulnerabilities, API, web and cloud security, arguing that benchmarks should fix their objective first, use real-world tasks, grade deterministically with code rather than LLM judges, isolate agents to prevent cheating, and weigh success against cost. The post reports that agents perform very differently by category (average pass@1 of 78.0% on API security versus 38.2% on code vulnerabilities) and that pairing harness with model matters, with a mean absolute gap of 5.1 pass@1 points across 22 models run on both Claude Code and ADK React. It also lists limitations including simple harnesses, string-equality grading on code tasks, public web tasks that may be contaminated, a 1,000-second time cap and unpublished confidence intervals.

0.169.0-alpha.9

This page is a GitHub Changelog entry titled "0.169.0-alpha.9," but the supplied material contains only site navigation and product menu text, with no details about the release itself.

Introducing Security Factory

Factory introduces Security Factory, now in Private Preview, which connects investigation, remediation, and verification into one continuous workflow to help engineering teams turn security findings into tested fixes. It combines Connectors for consolidating findings and context, Workstreams for parallel agent investigations, and Factory's coding workflows for developing and testing fixes. Organizations can configure agent autonomy levels, from presenting remediation plans for human approval to automatically creating pull requests and deploying fixes within approved policies.

Boxes

Amp introduces boxes: always-on computers starting at $18 per 30 days (billed by the minute) to which users can deploy the software they build with agents, with each application getting its own URL and no containers involved. Boxes can be shared with a workspace, can be turned on and off on a schedule, and each comes with its own runner so agents can operate directly on the box.

How to Get Structured Output from Any LLM: JSON Schema, Tool Calls, or a Decision Model

The article compares three routes to reliable structured LLM output: a strict JSON Schema in response_format, a forced tool call whose parameters are the schema, and a decision model for label-only fields. In tests on 24 support messages across nine schema-supporting models plus GLM 5.3 FlashX, strict schemas returned 209 of 216 valid replies (all 7 misses from one provider that ignores the schema), while prompt-only JSON failed 18 times due to code fences. It recommends require_parameters: true, Response Healing for broken JSON, and always validating replies yourself.

How to Build a Voice AI Agent

This tutorial builds a Python restaurant-booking voice agent that chains speech-to-text, an LLM with tool calling, and text-to-speech through a single OpenRouter API key, streaming each completed sentence to TTS instead of waiting for the full reply. Across 12 turns in three test conversations, median server-side time to first audio was 1.9 seconds (1.4 seconds without tools, 2.4 seconds with a tool call), and a four-turn conversation cost about $0.013 per minute of processed speech at October 8 promotional prices, with TTS accounting for roughly 90% of model cost. It also compares STT, LLM and TTS models on accuracy, latency and price, and shows how to run the same agent with LiveKit Agents for live audio and interruptions.

Best OCR Model for Scanned Documents, Measured

The author tested 15 vision models plus Mistral OCR and Tesseract on eight synthetic scanned pages scored by character error rate and 264 key-value checks, finding Claude Opus 5.5 most accurate (262 of 264 at $16.42 per 1,000 pages) and Mistral OCR the best value (258 of 264 at $2.02). The post documents failure modes such as fabricated card digits, wifi passwords and server names on a faded receipt, and provides code for transcription, disabling reasoning, cross-checking two models from different labs, and using the mistral-ocr engine.

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.

How We Built TLX: A Hardware-Native GPU Compiler for Production ML at Scale

Meta introduces Triton Low-Level Extensions (TLX), an extension to Triton that adds explicit warp-group and cluster-level orchestration so engineers can use modern GPU features like TMA, TMEM, and warp specialization while keeping Triton's programming model. TLX is in production across Meta workloads, with reported gains including 20-25% end-to-end MFU for GEM model training on thousands of NVIDIA Blackwell GPUs, up to two-thirds net-latency reduction in RecSys inference, and a distributed grouped GEMM kernel running in large-scale LLM pretraining. The post frames TLX as implementing a "MIMW" abstraction between CUDA's SIMT model and Triton's block-level model, and covers five extension areas: warp-group orchestration, synchronization, memory orchestration, cluster execution, and optional layouts.

Your Algorithm: Giving People More Ways to Personalize Facebook Recommendations

Meta introduced Your algorithm, a suite of AI-powered tools that gives Facebook users a single editable page showing the interests shaping their Feed and Reels, where they can add or remove preferences in natural language. The system uses large language models to generate an initial interest profile and Lately Summary, validate each edit, and perform a final reranking check against "Not Interested In" topics, with dedicated retrieval, filtering, and delivery mechanisms to make changes take effect responsively. Meta says it is exploring better handling of ambiguous input and additional entry points for shaping preferences.

Optimizing Muse Spark Open on TPU v7 with SGLang-TorchTPU

The post describes ExecuTorch's new WebGPU backend, which lets PyTorch models exported once to a .pte file via torch.export run in any WebGPU-enabled browser through a WebAssembly runtime, covering LLMs, vision-language, segmentation, depth, detection, super-resolution, speech-to-text and on-device LoRA/DiReFT fine-tuning. On an Apple M1 Pro in Chrome, WebGPU was 4–7× faster than ExecuTorch's Wasm/XNNPACK CPU path on prefill and 2–10× faster on decode, with all data staying on the device. The backend is still in early development, and the post links an ExecuTorch WebGPU Playground and Hugging Face repos with .pte files and export recipes.

Featured coverage of this story: 《ExecuTorch WebGPU: PyTorch Models on the GPU, in the Browser》

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.

Introducing Mastra FDE

Mastra announced Mastra FDE, a forward-deployed engineering program open to any team building agents, in which a dedicated Mastra engineer embeds with the customer's team from architecture through production at 6 hours/week consultative or 20-40 hours/week hands-on. The post cites Salesforce's Agentforce Vibes harness, MongoDB's Sage platform, and Workday moving security operations agents from Bedrock Agents to Mastra in 31 days while cutting 3,000 lines of code and about 98% of estimated vector storage costs. It positions the offering as cheaper than one senior AI hire and backed by a shared Slack channel with Mastra's founders and engineers, roadmap insights, upskilling sessions, and Mastra Enterprise.

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.

Running AV Reasoning Models Within Onboard Compute and Latency Limits

AV developers need reasoning models that fit onboard compute and latency constraints; larger models provide a foundation, but deploying them in a vehicle requires reducing model size, preserving useful reasoning, and validating performance on target hardware. The source mentions NVIDIA Alpamayo 2 Edge in this context, but the supplied material is truncated before detailing it.

MiniMax H3 Image is now available in ComfyUI

MiniMax H3 Image, MiniMax's open-weights image model, is now natively supported in ComfyUI, handling both text-to-image and reference-image editing from a single checkpoint with six sampling steps at guidance 1 and native output up to 4096×4096. It can run on your own GPU with downloaded weights or via Partner Nodes, and it shares its Qwen3-VL text encoder with the MiniMax H3 video model already supported in ComfyUI. The MiniMax H3 Image Conditioning node supports multi-reference editing via <Picture n> prompt tags, prompts up to 4096 tokens, and automatic sizing that defaults to 2048×2048 for text-to-image or follows image_1 when images are connected.

Mitra-v2: Scaling synthetic pretraining for tabular foundation models

Mitra-v2 is a second-generation tabular foundation model for classification and regression, pretrained entirely on synthetic tables with 10x as many rows and roughly 3x as many columns as Mitra-v1, plus a new hybrid SCM prior and hybrid optimizer. On TabArena it leads the single-model field, beating Mitra-v1 on 32 of 33 datasets where both run natively and completing all 51 datasets within the one-hour budget, and fine-tuned Mitra-v2 ranks first on ScoringBench. The fine-tuned models and inference/fine-tuning code are released under Apache-2.0 on Hugging Face.

Decision Models in Hugging Face

Hugging Face explains decision models, which take a state plus typed questions (choice, score, and noul, a Bernoulli yes/no primitive) and return structured decisions with probability distributions instead of generating text. The post contrasts them with traditional and zero-shot classification, and describes ecosystem support: Transformers' DecisionPipeline, AutoModelForDecision and DecisionConfig, a /v1/systemone endpoint also served by llama.cpp, a Hub tag for discovery, and fine-tuning via the Trainer API.

Work at the Frontier: AI’s effect on freelance work

New research from OpenAI and Upwork finds shifting demand for freelance work, new skill combinations, and larger budgets and more hours for AI-related projects.

Gemini Robotics 2.5 accelerates real-world general dexterity

Google DeepMind introduced Gemini Robotics 2.5, its most capable vision-language-action model, which it says raises skill breadth and learning speed, with 30% to 100% higher success rates and 20% to 30% faster task completion than previous baselines across 200 tasks. In a proof-of-concept on Mercedes-Benz production lines in Hungary and Germany, Apptronik's Apollo 2 humanoid performed automotive kitting with a 96% success rate. The release also adds inference-time safety steering (33% better safe completion around obstacles, 43% around people) and the open-source ASIMOV-Jailbreak benchmark, on which Gemini 4 Argon reaches 99.5% jailbreak resistance.

How to make flash models even faster: optimizing DeepSeek V4.1 Flash 1017

Baseten describes using an internal harness to optimize 80+ kernels and bottlenecks for DeepSeek V4.1 Flash 1017, producing a Fast variant it reports at 991 output tokens/s on Artificial Analysis with the lowest time per task. The post details three hardware-level techniques: kernel fusion (including an improved fused router kernel cutting decode time by 2.7ms and a 30% faster mHC megakernel), Programmatic Dependent Launch to overlap kernel execution, and host loop minimization via breakable CUDA graphs in SGLang.

Manage Kubernetes Clusters for GPU Infrastructure with NVIDIA Kubernetes Engine

NVIDIA announced two open source projects, NVIDIA Kubernetes Engine (NVKE) and NVIDIA Kubernetes Recipes (NVKR), for building and operating managed Kubernetes services on GPU infrastructure. Both are new projects available in public preview. The announcement notes that AI Factory customers often want to consume GPU capacity through the Kubernetes APIs, tools, and ecosystem they already use.

Start and Restore GPU Workloads in Seconds with NVIDIA Snapshot

NVIDIA Snapshot is a new open-source component of the NVIDIA DSX AI Factory Platform that makes initialized GPU workloads reusable across inference replicas, speeding startup and recovery. The post notes that when inference demand spikes or a replica fails, available GPUs do not immediately translate into serving capacity, since initializing an LLM workload—a cold start—can take minutes.

Agent Substrate: Advancing Kubernetes for Agents at Scale

AI agents spend most of their time idle, waiting on a human prompt, a tool result or a model response, and Kubernetes was not built for that. Running one Pod per session holds memory for inactive sessions, while deleting Pods between turns incurs a multi-second cold start and loses the agent's state, and RL post-training hits the same wall. Agent Substrate has now been donated to the foundation named in the source.

Find it, fix it, ship it: building secure apps on Lovable

Lovable outlines its security model: secure sign-in and access controls are on by default, secrets are encrypted, and apps are scanned on every publish for code, application logic, database schemas and row-level security policies, with a free deep scan available on demand. Users can select Fix to have an agent resolve a flagged issue, enable "Auto-fix security issues" in project settings, or use publishing controls to block releases with critical findings. The post also notes upcoming changes this month: a redesigned security experience and scheduled scans on all paid plans.