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Matt demonstrates using Cursor Cloud Agent to generate UI motion videos by referencing real Slack

On October 30, 2026, Matt Palmer demonstrated opening Chrome and real Slack on the Linux remote desktop of the Cursor cloud Agent, having the Agent use code to generate a Slack interface motion animation video, and then iterating and revising after checking the easing and motion patterns against the real interface. He broke the process down into an inner loop (the Agent does the work, checks against the real thing, and fixes it) and an outer loop (a human provides references, preferences, and permissions and accepts the output), and proposed the ladder of Do→Codify→Generalize, turning one success into a Skill and then generalizing it. He emphasized that Slack was not decompiled; he merely gave the Agent a reference and asked it to extract visual patterns by comparison.

View story:Matt Palmer demonstrated using the Cursor cloud Agent to generate an interface motion animation video against real Slack

TestingCatalog says Google has terminated the Jules AI agent

On October 30, 2026, TestingCatalog reported that Google will terminate its AI coding agent Jules. Phoenix Technology reported the same day that Google will gradually shut down Jules, which was launched in December 2024, and will integrate the experience accumulated from the tool into its AI coding platform Antigravity; Jules' official X account stated that Jules was originally an experiment exploring how asynchronous coding agents could help developers ship faster, and that its experience has already influenced multiple Google products. cnBeta reported the same day that Jules will gradually cease operations, less than two years after its launch, and that the team is integrating the experience into Antigravity so that users can both autonomously execute background tasks in remote environments and use an agent-centric development environment. The current status is gradual shutdown and experience integration.

TestingCatalog says Google terminates Jules AI agent

On October 30, 2026, TestingCatalog reported that Google will terminate its AI coding agent Jules. Phoenix Technology reported the same day that Google will gradually shut down Jules, which was launched in December 2024, and will integrate the experience accumulated from the tool into its AI coding platform Antigravity; Jules' official X account stated that Jules was originally an experiment exploring how asynchronous coding agents could help developers ship faster, and that its experience has already influenced multiple Google products. cnBeta reported the same day that Jules will gradually cease operations, less than two years after its launch, and that the team is integrating the experience into Antigravity so that users can both autonomously execute background tasks in remote environments and use an agent-centric development environment. The current status is gradual shutdown and experience integration.

View story:TestingCatalog says Google has terminated the Jules AI agent

Shopify CEO Tobi discusses how personal AI Agents can make money

Shopify CEO Tobi Lütke offered his judgment on how personal AI agents will monetize. He said personal AI agents will not monetize through a 2% transaction cut, but will enter an era of "basis points." He also proposed a "reverse money test": users would be willing to pay an increasingly high price to stop using their own Muse. The monetization method could be metered billing in the background, or a cut of transaction volume, or both; he is not worried, because there is a lot of room for agents to help people save money.

Shopify CEO Tobi discusses how personal AI agents can make money

Shopify CEO Tobi Lütke offered his judgment on how personal AI agents will monetize. He said personal AI agents will not monetize through a 2% transaction cut, but will enter an era of "basis points." He also proposed a "reverse money test": users would be willing to pay an increasingly high price to stop using their own Muse. The monetization method could be metered billing in the background, or a cut of transaction volume, or both; he is not worried, because there is a lot of room for agents to help people save money.

View story:Shopify CEO Tobi discusses how personal AI Agents can make money

Tripo launches beta version of Agentic Canvas in Tripo Studio

On October 29, 2026, Tripo announced that Agentic Canvas in Tripo Studio has entered the beta phase, opening it to all MAX and Team users, with a gradual rollout to other users. This feature allows users to describe their needs in natural language, with an agent planning and executing the workflow to directly produce 3D assets, supporting batch processing and reusable pipelines, and enabling asset inspection, refinement, rigging, and editing through AI 3D Lab. There is currently no information on further developments.

Meshy demonstrates Hermes Agent generating and reskinning a claw machine game in a single conversation

On October 29, 2026, Meshy demonstrated an achievement: building and reskinning a claw machine game within a single Hermes Agent conversation. According to information released by Meshy, Meshy generated all 12 game assets, and Claude Code built the game around these assets; each replay round completely changes the art style, in order: plush, gummy, and pixel art. The same game presents three art styles without manual modeling. Currently, this event only has release content from Meshy as a single source, and there are no subsequent developments.

Arena.ai compares coding agent user criticism across 20,869 traces

Based on 20,869 traces from Agent Arena: Code, Arena.ai compared user criticism signals of five top models from Anthropic and OpenAI against other agents. The analysis found that frontier models differ most noticeably in three dimensions: 6.9 percentage points fewer complaints about failure-fix loops and 1.9 percentage points fewer about ignoring project context; 6.4 percentage points fewer design issues and 5.8 percentage points fewer issues with producing usable results; and 5.6, 4.9, and 4.5 percentage points fewer complaints about instruction following, task completion, and breaking existing code, respectively. There are currently no follow-up developments.

OpenBMB says its Agent approached frontier level within four hours

On October 29, 2026, ModelBest OpenBMB announced that its agent, starting from scratch in just four hours, has reached performance very close to the frontier level. The post also cited an earlier post about ForgeKernel Live optimizing the DeepSeek kernel. At present, this claim appears only in content published by OpenBMB itself, and there are no independent reports or follow-up developments from other sources.

Arena says GPT-6.1 Terra (Max) ranks 8th in Agent Arena

Arena released measured results based on nearly 9,000 agent sessions: OpenAI's GPT-6.1 Terra (Max) ranks 8th in Agent Arena, with a net improvement of +10.6% and a median cost per task of $0.62. This cost is lower than the $1.09 of 7th-place Gemini 4 Argon (High), the $0.89 of 6th-place GPT-6.1 Sol (Max), and the $5.16 of 5th-place GPT-6 Astra (Max). In the subcategory rankings, it places 3rd in Bash Reliability (+13.2%), 7th in Steerability (+13.3%), and 9th in Praise vs Complaint (+16.8%). There are currently no further developments.

Arena.ai Publishes Per-Task Cost Comparison of Models on Agent Arena

Arena.ai published a per-task cost comparison of models on Agent Arena on October 30, 2026. Statistics show that OpenAI's GPT-6.1 Sol (Max) costs $1.91 per task, 85% lower than Claude Opus 5.5 (Max) at $12.51 and 87% lower than GPT-6 Astra (Max) at $14.93. Arena.ai noted that these two cost advantages come from different sources: relative to Astra, Sol's advantage mainly comes from a lower per-token price; relative to Opus, it mainly comes from consuming fewer tokens per task. Per-task cost is calculated by multiplying the number of input, output, and cache-read tokens by their respective unit prices, and Arena.ai identifies the reasons for the cost reduction by comparing prices and token usage.

Arena.ai publishes per-task cost comparison of models on Agent Arena

Arena.ai published a per-task cost comparison of models on Agent Arena on October 30, 2026. Statistics show that OpenAI's GPT-6.1 Sol (Max) costs $1.91 per task, 85% lower than Claude Opus 5.5 (Max) at $12.51 and 87% lower than GPT-6 Astra (Max) at $14.93. Arena.ai noted that these two cost advantages come from different sources: relative to Astra, Sol's advantage mainly comes from a lower per-token price; relative to Opus, it mainly comes from consuming fewer tokens per task. Per-task cost is calculated by multiplying the number of input, output, and cache-read tokens by their respective unit prices, and Arena.ai identifies the reasons for the cost reduction by comparing prices and token usage.

View story:Arena.ai Publishes Per-Task Cost Comparison of Models on Agent Arena

Herdr launches desktop app for terminals and coding agents

Herdr has launched a desktop app for terminals and coding agents that organizes work by space and summarizes each agent's Working, Blocked, Done, and Idle status in a sidebar. After the app window is closed, terminals continue running in the core, and spaces can also be placed on remote machines for execution via SSH. The app supports Linux, macOS (Apple Silicon), and Windows x64, includes three built-in layouts—Herdr, Terminals, and Herdr 98—and allows mod panels to be written in JavaScript. There are currently no reports of further developments.

Herdr offers a terminal and coding agent desktop app

Herdr has launched a desktop app for terminals and coding agents that organizes work by space and summarizes each agent's Working, Blocked, Done, and Idle status in a sidebar. After the app window is closed, terminals continue running in the core, and spaces can also be placed on remote machines for execution via SSH. The app supports Linux, macOS (Apple Silicon), and Windows x64, includes three built-in layouts—Herdr, Terminals, and Herdr 98—and allows mod panels to be written in JavaScript. There are currently no reports of further developments.

View story:Herdr launches desktop app for terminals and coding agents

Akamai Launches Beta Versions of Akamai Inference and Agent Sandbox

On October 29, Akamai released beta versions of two products, Akamai Inference and Akamai Agent Sandbox, allowing users to run AI models on Akamai Cloud and securely execute code generated by AI agents. According to The Elec, Akamai Inference supports 12 open-weight models, including OpenAI's gpt-oss-120b, Google's Gemma 4, and Nvidia's Nemotron 3 Ultra, and provides an OpenAI-compatible API; Agent Sandbox is based on WebAssembly isolation technology and restricts code access to computing resources, network connections, and system functions. Analytics India Magazine separately reported that Agent Sandbox is released as an open-source component of the Spin framework, and that Akamai is also expanding its NVIDIA Blackwell Ultra GPU cluster, with each eight-GPU node equipped with 2.3 TB of HBM3e memory. Both services are currently in the beta stage.

Tripo launches beta version of Agentic Canvas in Tripo Studio

On October 29, 2026, Tripo announced that Agentic Canvas in Tripo Studio has entered the beta phase, opening it to all MAX and Team users, with a gradual rollout to other users. This feature allows users to describe their needs in natural language, with an agent planning and executing the workflow to directly produce 3D assets, supporting batch processing and reusable pipelines, and enabling asset inspection, refinement, rigging, and editing through AI 3D Lab. There is currently no information on further developments.

View story:Tripo launches beta version of Agentic Canvas in Tripo Studio

Meshy showcases Hermes Agent generating and reskinning a claw machine game in a single conversation

On October 29, 2026, Meshy demonstrated an achievement: building and reskinning a claw machine game within a single Hermes Agent conversation. According to information released by Meshy, Meshy generated all 12 game assets, and Claude Code built the game around these assets; each replay round completely changes the art style, in order: plush, gummy, and pixel art. The same game presents three art styles without manual modeling. Currently, this event only has release content from Meshy as a single source, and there are no subsequent developments.

View story:Meshy demonstrates Hermes Agent generating and reskinning a claw machine game in a single conversation

OpenBMB says its Agent approaches frontier level within four hours

On October 29, 2026, ModelBest OpenBMB announced that its agent, starting from scratch in just four hours, has reached performance very close to the frontier level. The post also cited an earlier post about ForgeKernel Live optimizing the DeepSeek kernel. At present, this claim appears only in content published by OpenBMB itself, and there are no independent reports or follow-up developments from other sources.

View story:OpenBMB says its Agent approached frontier level within four hours

Arena says GPT-6.1 Terra (Max) ranks 8th on Agent Arena

Arena released measured results based on nearly 9,000 agent sessions: OpenAI's GPT-6.1 Terra (Max) ranks 8th in Agent Arena, with a net improvement of +10.6% and a median cost per task of $0.62. This cost is lower than the $1.09 of 7th-place Gemini 4 Argon (High), the $0.89 of 6th-place GPT-6.1 Sol (Max), and the $5.16 of 5th-place GPT-6 Astra (Max). In the subcategory rankings, it places 3rd in Bash Reliability (+13.2%), 7th in Steerability (+13.3%), and 9th in Praise vs Complaint (+16.8%). There are currently no further developments.

View story:Arena says GPT-6.1 Terra (Max) ranks 8th in Agent Arena

Arena.ai compares user criticism signals of frontier coding agents

Based on 20,869 traces from Agent Arena: Code, Arena.ai compared user criticism signals of five top models from Anthropic and OpenAI against other agents. The analysis found that frontier models differ most noticeably in three dimensions: 6.9 percentage points fewer complaints about failure-fix loops and 1.9 percentage points fewer about ignoring project context; 6.4 percentage points fewer design issues and 5.8 percentage points fewer issues with producing usable results; and 5.6, 4.9, and 4.5 percentage points fewer complaints about instruction following, task completion, and breaking existing code, respectively. There are currently no follow-up developments.

View story:Arena.ai compares coding agent user criticism across 20,869 traces