Posts

[!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: ...

[!IMPORTANT] Core Pain Point: In fast-paced projects, the high cost of manual documentation updates leads to persistent “drifting” or loss of synchronization between code and architectural documentation (README, ARCHITECTURE.md). The Solution: Offload the cognitive burden of “documentation maintenance” from human developers to AI Agents with semantic reasoning capabilities, enabling “metabolic” renewal of architectural maps. After multiple rounds of conversation with AI regarding documentation governance, I’ve distilled a concept for “AI-Native Metabolic Governance V3.0.” This is not just an automation script, but a governance logic designed to make code architecture “come alive.” ...

With Diablo 2 Resurrected Season 13 in full swing, many of us are discussing how to improve our farming runs. While intuition plays a role, understanding the technical NoDrop (zero-drop probability) mechanism allows us to find a better balance between difficulty and speed. Loot in Diablo is essentially a game of probability. Let’s look at the data. 1. Monster Intensity and Factor X In D2R, the number of players in a game (X) affects a monster’s base attributes. These are fixed at the moment the monster spawns; even if players leave later, the stats remain unchanged. ...

A deep dive into the ‘hallucinogenic’ migration from Tauri v1.x to v2.0, covering ACL permission changes, Minisign signature updates, and GitHub Actions automation pitfalls.

D2R Multi Rust receives a major overhaul with a lightweight reconstruction based on Rust + Tauri, introducing the zero-dependency Isolation Engine. Binary size is only 1/10th of the .NET original.

In the AI era, raw coding skills are devaluing, while system design, cross-domain coordination, and the ability to master complexity are becoming core competencies. This post explores how to transition from executor to coordinator with the help of AI.

AI lowers the barrier to entry for coding but raises the ceiling for cognitive demand. In AI-driven refactoring, we need more than just prompts—we need the grit to hold the architectural line against complexity.