Hand-Rolled

[!NOTE] The Origin: To help my son enter a deep state of focus during study sessions, I “hand-rolled” a lightweight “Concentration Tool” using pure HTML. No heavy frameworks, just three raw, direct modules. In this post, I’ll share the design logic behind this tool. You can experience it directly by clicking Concentration Tool in the navigation bar. 1. Why “Hand-roll”? There are countless focus apps out there, but they often: ...

[!TIP] Objective: Implement “zero-intrusion” architectural document synchronization and an AI-driven system self-repair closed-loop. 1. Zero-Intrusion Metabolism (Metabolic Governance) In modern Agent development, maintaining an ARCHITECTURE.md and routing table is often a developer’s nightmare. Our solution is a Metadata-Free “metabolism” mechanism: Perceive Changes: Mounted on a Git hook, it captures code variations via Git Diffs. Semantic Inference: The AI automatically analyzes code intent to update the machine-readable .anti_bot_map.md (RAG routing table) and the human-readable ARCHITECTURE.md (Architectural Lore). Strong Consistency: Completely eliminates the cost of manual documentation maintenance; docs are code, and code is the map. 2. The System’s Pulse: Heartbeat Engine To ensure Agent stability in unattended environments, we configured an independent Heartbeat process. It’s more than just resource monitoring; it’s a “self-awareness” probe: ...

[!TIP] Objective: Enable multi-modal (vision/files) perception via CLI parameters and build a dynamically-loaded plugin-based skill system. 1. Letting the CLI “See” the World Multi-modal capabilities don’t necessarily require complex SDKs. In VISAGENT, we leverage the native support for file paths in the gemini CLI (@path) to achieve sensory integration at the RoleEngine layer: def _do_raw_invoke(self, message, files=None): # Construct multi-modal suffix mm_suffix = "" if files: mm_suffix = "\n" + "\n".join([f"@{f}" for f in files]) # Append to final Prompt full_input = f"{message}{mm_suffix}" # ... execute subprocess Field Experience: To handle complex visual tasks, we encapsulated a dedicated vision_expert skill. By using the DEEP reasoning mode, we guide the AI through Chain-of-Thought thinking, enabling precise identification of screenshots and UI components. ...

[!TIP] Objective: Evolve from single-step prompts to a “Plan-Simulate-Execute” closed-loop, introducing YAML-based DAG asynchronous orchestration. 1. From “Chatting” to “Execution”: The AX (Architect-Executive) Paradigm The watershed between a simple Chatbot and an Agent is the ability to decompose vague goals into actionable steps. In VISAGENT, we implemented the AX Planner logic: Architect: Receives requirements and outputs a TODO.json. Execution is forbidden; only planning is allowed. Simulation: Before execution, a “Security Expert” role performs a risk assessment on the plan. Executive: Executes each step via execute_step. This “think before you act” mechanism is easily implemented on any CLI using simple Prompt constraints: ...

[!TIP] Objective: Build an Agent engine with state management and isolation by wrapping a CLI (e.g., gemini CLI) without framework reliance. 1. Why CLI Over Frameworks? VISAGENT’s philosophy is CLI-Native. Directly wrapping a CLI (like gemini CLI) offers superior control and transparency: Atomicity: Each call is a single inference step. State Transparency: Session resume/suspension is strictly controlled by paths and the Resume flag. Isolation: Permission sandboxing is achieved through native OS tools. 2. Core Wrapper: RoleEngineBase Execution is triggered via subprocess. To prevent Shell parameter overflow from long Prompts, we use stdin for transmission: ...