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HHacker News Trending22:54

AI score79

Phorion Discloses That Claude Code VS Code Extension 2.1.313 Was Planted With an SSH Backdoor

Phorion claims that it found a backdoor in the official Claude Code for VS Code extension 2.1.313 on the Cursor Marketplace, which plants a persistent SSH backdoor on macOS, Linux, and Windows; Cursor has delisted this version and removed 2.1.313 from the NPM and VSCode registries. The malicious code is injected into extension.js in the form of an IIFE, executes upon loading, appends the svc-telemetry public key to ~/.ssh/authorized_keys, and establishes a reverse tunnel to 134.209.82.225. Phorion points out that the Remote Login status check on macOS is flawed: on machines where SSH is not enabled, it skips the enabling step, but the key and persistence still take effect, and it recommends checking the relevant files and processes.

Why read thisThe original article breaks down the injection location, SSH tunnel, and persistence path of the malicious extension, and provides an actionable detection and cleanup checklist.

TThe Decoder13:18Product release

AI score78

Google opens Android Studio to third-party integration at Android Dev Summit…

At the Android Dev Summit, Google launched the Bring Your Own Agent feature for Android Studio Rabbit 2. Through the open Agent Client Protocol, developers can run more than 40 coding agents simultaneously within the IDE, including Claude Agent, Codex, and Google Antigravity, with direct access to project structure, the emulator, and Logcat. Google says that in internal tests, after agents rendered Compose previews in the terminal, they completed UI tasks 77% faster and cut token usage to as little as one-third; in an A/B test using Claude Opus 5.5, the CLI with built-in LeakCanary found and fixed memory leaks nearly twice as fast, with about 25% fewer tokens. On the on-device side, Google added an Agentic API alpha powered by Gemini Nano 4 to ML Kit, and launched the MCP-based AppFunctions API beta and the Agent-to-UI interface; it also partnered with XREAL to release the Android XR smart glasses XREAL AURA, priced at $1,099 and shipping in mid-November.

Why read thisAndroid Studio opens to third-party coding agents, with measured speedup data for terminal previews and memory leak detection, which can be used to assess the room for change in existing development workflows.

RRohan Paul12:57Product release

AI score77

Microsoft Releases GitHub Copilot Fusion, Routing Coding Tasks Between Claude and GPT Models

Microsoft has released GitHub Copilot Fusion, which can route coding tasks between Claude and GPT models. On Artificial Analysis's Coding Agent Index, its Max setting scores 60, versus 62 for Claude Code; the per-task cost is $5.59 versus $11.42. Fusion consumes 7.2M tokens per task, about half of Claude Code's usage.

Why read thisThe original text provides Fusion and Claude Code's scores on the Coding Agent Index, per-task cost, and token usage comparison, making it easy to judge the actual trade-offs of the routing approach.

FFactory Research / Product12:50First-partyProduct release

AI score66

Factory releases the Droid 1.0 agent framework, with programmatic tool calling enabled by default

Factory releases Droid 1.0, the largest update yet to its agent framework, enabling programmatic tool calling (PTC) by default, which lets the model write JavaScript scripts to execute hundreds of tool calls within a single request in a sandbox. On Terminal-Bench 4, when paired with GPT-6.1 Sol, Droid scores 3.5 points higher than Codex at 12% lower cost; when paired with Claude Opus 5.5, it scores 3.0 to 7.6 points higher than Cursor, OpenCode, Devin, and Claude Code, at up to 24% lower cost. Droid 1.0 is available today with macOS and Windows desktop versions and a terminal UI, and also offers the Droid SDK (@factory/droid-sdk).

Why read thisThe original article provides the specific mechanisms and measured data for programmatic tool calling and prompt caching, which can be used to understand where the cost and latency of long-task agents come from.

MMistral AI12:45First-partyModel releases

AI score87

Mistral Releases Open Weights for Mistral Large 4 (Le Chonk)

Mistral has released the open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that continues reinforcement learning on top of the preview version, with improved coding and defensive cybersecurity capabilities. It scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT-based Mistral Ecosystem Open-Weight license, and is priced via API at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output.

Why read thisThe original text provides open weights, three reasoning modes, and comparisons across multiple benchmarks, allowing readers to judge the trade-offs for self-hosted coding and security scenarios.

AArtificial Analysis10:36Analysis

AI score65

Artificial Analysis releases Intelligence Index v5, with 60% of evaluations based on private datasets

Artificial Analysis releases Intelligence Index v5, adding two new benchmarks, FrontierCode 2.0 and Terminal-Bench-Science, upgrading HLE-Diamond, AA-Omniscience v1.1 and CritPt v2, removing GDPval-AA v2.1, SciCode and AA-LCR v1.1, and changing 60% of the index to evaluations based on private test sets or held-out answers. It also launches Progress Index v1, which uses a unified scale to track changes in model capabilities over time. The results show that Claude Opus 5.5 (max) ranks first with 58 points, followed by GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55), while Gemini 4 Argon (high) scores 50; the gap between open-weight models and the leaders has widened from 3 points to 12 points, and among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead with 32 points. The per-task cost of the top ten models differs by more than 13 times, with GPT-6.1 Terra (max) lowest at $0.96 and Claude Fable 5.1 (max) highest at $13.04.

Why read thisThe original text lists the benchmarks added and upgraded in the v5 index as well as the items removed, and provides the scores of leading models and the per-task cost gap, which can be used to compare the capabilities and costs of each model.

GGoogle Blog08:00First-partyModel releases

AI score82

Google DeepMind Releases Gemini 4 Carbon Frontier Model

Google DeepMind has released Gemini 4 Carbon, calling it its strongest model, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0 and leading on SEC-bench Pro (88.2%), ExploitGym (54.8%), and CWE-bench (71.0%). The model targets long-horizon engineering tasks; an internal team once had it run continuously for several days to investigate and fix hidden defects in a large codebase, and it rose 89 elo to 4th place (1768) on WebDev Arena. Starting this week, Carbon will first be opened to some cybersecurity defenders, then gradually expanded through the Fairwind Program, and only afterward made available to developers, enterprises, and consumers; after the introductory period, pricing will be $4 per 1M input tokens and $20 per 1M output tokens.

Why read thisThe original text provides Carbon's benchmark results on cybersecurity and long-horizon coding tasks and its phased rollout path, which can be used to assess the pace at which frontier models will be deployed in attack and defense scenarios.

TThe Elec19:30Model releases

AI score82

Moonshot AI Releases Kimi K3.1 Pro, AA Intelligence Index Score of 55 Surpasses GPT-6 Astra

Moonshot AI has released Kimi K3.1 Pro, an upgraded version of Kimi K3, featuring a MoE architecture with 2.8 trillion total parameters and 104 billion activated parameters, activating 16 of 896 experts at a time. According to Artificial Analysis benchmarks, the model ranks fourth on the Intelligence Index with a score of 55, higher than GPT-6 Astra's 53, while the previous generation Kimi K3 scored 44. It ranks first on Terminal Bench 4.0 with 67%, compared with 13% for the previous generation and 64% for Claude Sonnet 5.5; pricing is $2 per million input tokens and $10 per million output tokens, but its output speed of 73 tokens/s is below the average of 81 tokens/s.

CChatGPT02:37First-partyModel releases

AI score84

OpenAI Releases GPT-6.1 Terra Model

OpenAI has released a new model, GPT-6.1 Terra, which focuses on speed and efficiency, strengthens visual reasoning and precise computer operation, and writes more clearly and naturally. Starting today, the model is being rolled out to Plus, Pro, Enterprise and Edu users in ChatGPT Work and Codex; the company says it can cover work scenarios ranging from interpreting technical drawings and drafting proposals to filling out forms and editing videos.

Why read thisThe official announcement sets out the new model's positioning in visual reasoning and computer operation, and explains the scope of access for paid users.

TThe New Stack01:22

AI score78

Anthropic Files S-1 for Nasdaq Listing; Top Claude Code Enterprise Users Spend 20 Times More Than Other Users

Anthropic filed its S-1 on Monday, planning to list on Nasdaq under the ticker ANT, without disclosing a price range. The filing shows that in the third quarter, Claude Enterprise users who did not use Claude Code spent less than $75 per month on average, while the top 5% of Claude Code enterprise users spent about 20 times that figure. Anthropic said that in the 12 months ended September 30, 2026, more than 11,900 customers paid over $100,000 annually, and revenue in the first half of 2026 was $16.3 billion, of which token usage contributed 50%.

Why read thisThe S-1 filing discloses Claude Code enterprise user spending and the proportion of code contributed by Anthropic itself, offering a window into the commercialization path of coding agents.

UUK AI Security Institute: Blog23:57First-partyResearch

AI score71

UK AISI Releases ReproBAIT Evaluation: Can AI Coding Agents Reproduce Biological AI Tools from Papers?

The UK AI Security Institute's Chem-Bio team, in collaboration with SecureBio, built the ReproBAIT evaluation to test the ability of AI coding agents to reproduce biological AI tools (BAITs) given data and limited public information. Results showed that simpler tools could be reproduced within 4 hours and under a $20 compute budget; ProteinMPNN reached 96.5% reproduction fidelity with Claude Opus 5 when the full paper was provided, while the most complex tool, RFdiffusion3, was not successfully reproduced by any agent within the time limit. Based on this, the team noted that merely withholding code or restricting paper information does not equal safe release, and that controlling access to underlying biological data may be more effective than controlling code.

Why read thisAISI used the ReproBAIT benchmark to empirically test coding agents' ability to reproduce biological AI tools, providing first-hand evidence for trade-offs in open source and data governance.

KKazike13:07Analysis

AI score67

Retrofitting a TV, Fill Lights, and a Camera with Codex and Claude

The author shared the process of using Codex and Claude to reverse engineer and retrofit real-world devices: first enable ADB debugging on the TV, then have the Agent disable system packages, clear boot ads, and rebuild a clean launcher, and replicate the same method on three other TVs. The team also used Claude to reverse engineer 4 fill lights and built a unified web console, and used Codex to build a multi-camera console for a Sony camera and a DJI Pocket 3 and deploy it to Feishu. The author reminds that different brands of devices expose different interfaces and permissions, and that backups should be made before modifying system software.

Why read thisThe author documented the complete process of using Codex and Claude to retrofit a TV, fill lights, and a camera via ADB and protocol reverse engineering, which can be transferred to other internet-connected devices.

BBrowser Use07:44First-partyAnalysis

AI score65

How to get coding agents to use your already-logged-in Chrome

Browser Use published a guide introducing several ways to let coding agents work in a real, already-logged-in Chrome, including Claude in Chrome, Playwright MCP extension mode, Chrome DevTools MCP auto-connect mode, the Gemini CLI built-in browser agent, the ChatGPT desktop browser extension, and Browser Harness. The article gives configuration commands and limitations separately for agents such as Claude Code, Codex, Gemini CLI, and Cursor, and explains that starting with Chrome 136, --remote-debugging-port and --remote-debugging-pipe on the default data directory will be ignored, so the old practice of copying cookies to a new browser will fail in scenarios such as device-bound sessions.

Why read thisThe original article breaks this down agent by agent, giving the configuration commands for connecting to an already-logged-in Chrome along with the respective limitations, making it easy to compare and choose.

MMarkTechPost13:55Product release

AI score80

Microsoft Research open-sources the Agensh multi-agent framework, supporting up to 1024 coding agents

Microsoft Research has open-sourced Agensh, a multi-agent framework that removes the central orchestrator, with each worker claiming subtasks on its own, sharing findings, and merging code into the same Git repository. On ProgramBench, the team scaled from 1 agent to 1024, raising the average pass rate on the 5 hardest tasks from 19.31% to 28.78% (with 128 agents), and the single pandoc task reached 79.59% with GPT-6.1-sol paired with 32 agents. The code is released under the MIT license, with a default of 8 workers per single node; the 1024-agent run used 16 nodes with 64 agents per node.

Why read thisThe original text presents a multi-agent collaboration mechanism without a central orchestrator and scaling data from 1 to 1024 agents, making it possible to observe the division of labor that emerges as scale increases.

SShao Meng09:50Analysis

AI score80

How Shopify Uses Remote Dev to Let AI Agents Develop Mobile in Parallel

Shopify built Remote Dev for the AI Agent "River" in Slack, using a cluster of remote Mac machines plus Android emulators mounted on Linux, letting the Agent compile and test iOS/Android in parallel and do Live Preview; the share of merged mobile PRs produced by River rose from about 6% to about 61%. Engineering-wise, a pre-warmed pool compresses cold starts of over 30 minutes to a few minutes, and skills plus a CLI wrapping SSH let the Agent operate the remote environment. River is visible to multiple people by default, and the team uses a classification model, Jev, to observe conversations and categorize failure cases, then hands them to an LLM to find improvements.

Why read thisShopify disclosed engineering details such as a warmed pool of remote Mac machines and Android nested virtualization, as well as the change in adoption rate from 6% to 61%.

AArena08:23Model releases

AI score77

Kimi K3.1 Pro released, rises to 8th on Code Arena WebDev

Moonshot AI released Kimi K3.1 Pro, which ranks 8th on Code Arena WebDev with 1,692 points, matching Claude Opus-5 (Max) while costing 70% less, and rising 9 places from Kimi K3 (Max)'s 17th. The model is priced at $2/$10 per million input/output tokens, with a median cost of $1.23 per task, 29% lower than Kimi K3, and its weights will be opened on November 4. Kimi officially calls it a 3T-class open-weight model, featuring a 1M-token context, a 2.8T total parameter/104B active parameter MoE multimodal architecture, and a 65% reduction in hallucination rate.

Why read thisKimi K3.1 Pro's Code Arena ranking and cost data, directly comparable with Claude Opus-5 and the previous-generation K3.

CCursor08:18First-partyProduct release

AI score65

Cursor launches computer control capability, agents can operate desktop applications

Cursor announced a new computer control capability; agents can drive desktop applications such as the iOS Simulator to test features they build themselves. The official tweet did not provide further details or the scope of availability.

Why read thisCursor officially states that agents can directly operate desktop applications, so readers can judge its impact on the development self-testing workflow.

MMoonshot AI · Kimi Blog08:13First-partyModel releases

AI score87

Moonshot AI releases Kimi K3.1 Pro: a 3T-class open-weight model with a 1 million token context

Moonshot AI releases Kimi K3.1 Pro, the first model in the K3.1 series and its strongest 3T-class open-weight model to date, with a 1 million token context window, alongside Kimi K3.1 Pro Highspeed, which is 2-3 times faster. The model is now available on Kimi.com, Kimi Work, Kimi Code and the Kimi API, with full model weights to be released on November 4. On Terminal-Bench 4.0, K3.1 Pro scores 65.7%, higher than GPT-6 Astra's 58.2% and slightly below Claude Opus 5.5's 66.4%; on the internal benchmark Kimi Code Bench 3.0 Long-horizon it scores 61.2%, higher than K3's 55.95%.

Why read thisThe official disclosure covered K3.1 Pro's benchmark performance on coding, 3D and financial tasks, as well as the trade-offs made on cybersecurity data before open-sourcing the weights.

GGoogle Blog08:13First-partyModel releases

AI score80

Google releases Gemini 4 Argon, showcasing 5 community creative projects

Google releases new frontier model Gemini 4 Argon, stating it performs outstandingly on complex tasks such as coding, mathematics, and 3D world building, and surpasses other frontier models on benchmarks across multiple domains. The official showcase features 5 community projects, including a 3D Earth that maps wildfires and seismic activity in real time, a turbofan engine simulation with real-time data, a 3D carriage generated from a single image, and a multiplayer capture-the-flag browser game. The model has been widely available since last week.

Why read thisOfficial lists five community cases, demonstrating the new model's concrete outputs on 3D scenes and multi-step tasks.

DDeepLearning.AI · Letter from Andrew Ng22:54First-partyOpinion

AI score84

Andrew Ng on How Engineering Teams Can Transform into AI-Native Teams

In his letter, Andrew Ng proposes that engineering teams transforming into AI-native teams should start from four aspects: people, projects, platform, and process—encouraging members to improve their AI engineering capabilities and build hands-on themselves, supporting a small number of promising projects from the top down, organizing data, repositories, and internal tools into infrastructure usable by agents, and redesigning processes around the new bottlenecks brought by AI. He believes the cost of building clearly specified software has dropped significantly, while other links have instead become bottlenecks, so teams with sufficient context and full authorization should be formed.

Why read thisAndrew Ng gives concrete approaches for engineering teams to transform into AI-native teams from four aspects—people, projects, platform, and process—which can be applied by comparing against one's own organization.