@yadong_xie: Recently, I've been exploring the boundaries of agents using web technologies to express scene interactions. The final answer is that there are no boundaries. Prismatic Tank, activate!
Summary
User yadong_xie shared an exploration of AI agents using web technologies to express scene interactions, concluding that there are no boundaries, and launched a project named 'Prismatic Tank'.
View Cached Full Text
Cached at: 08/28/26, 01:35 AM
Recently, I’ve been exploring how far agents can push web technologies to express scene interactions—and where those boundaries lie.
The conclusion I reached is that there are no limits.
Prism tank, activate! https://t.co/hcutxItqvn
Similar Articles
This article systematically reviews AI Agent architecture and engineering practices, covering control flow, context engineering, tool design, memory, multi-agent organization, evaluation, tracing, and security. It is based on the OpenClaw implementation and emphasizes the critical role of Harness (testing and validation infrastructure) for system stability.
This article systematically reviews AI Agent architecture and engineering practices, covering control flow, context engineering, tool design, memory, multi-agent organization, evaluation, tracing, and security. It is based on the OpenClaw implementation and emphasizes the critical role of Harness (testing and validation infrastructure) for system stability.
@Xudong07452910: Open-source framework recommendation: Agency Agents — 232 professional AI agents, divided by function, covering 16 business departments. If you've used Claude Code or Codex, you may have encountered this problem: AI is very capable at coding tasks, but when it comes to front-end design, writing marketing...
Agency Agents is an open-source framework providing 232 professional AI agents covering 16 business departments. Each agent has a unique personality, communication style, and delivery standards. It supports multiple development tools such as Claude Code, GitHub Copilot, and has community-translated versions.
@knoYee_: https://x.com/knoYee_/status/2062780637677752366
The author reviews three months of experience using multi-agent collaboration, summarizing five main pain points (such as conflicts between agents, ignoring boundary conditions, self-censorship failure, difficulty in merging decisions, and exposing harder problems after compressed execution) and two insights (the high value of read-only review agents, and that agent conflicts expose ambiguous requirements), emphasizing the core decision-making role of humans in AI collaboration.
@AxtonLiu: https://x.com/AxtonLiu/status/2073791557547794579
This article discusses the concept of Agent OS, emphasizing the division of tasks into multiple workstations (fetch, refine, verify, confirm) through specialization, each managed by an independent Agent to achieve controllable automation. The author uses the example of digesting browser tabs to demonstrate how specialization isolates context, responsibility, and risks, ensuring the accuracy and reliability of AI output.
@grapeot: Why Have We Been Giving AI Agents the Wrong Interface? Nearly all AI coding and agent tools default to chat windows (e.g., Cursor, Claude Code). But from an efficiency standpoint, this WeChat/Slack-inspired interaction is severely limiting AI...
The article criticizes the prevalent use of chat windows as the interface for AI programming tools, arguing that this design restricts the autonomous working ability of AI from both the user and developer ends, and proposes a shift to an asynchronous, task-driven collaboration model.