WakeUp-Jin

From 'consciousness emergence' to 'field establishment' — analyzing the design philosophy and development direction of collaborative and autonomous Agent paradigms.

Core experiences distilled from OpenAI Codex and Anthropic Claude for building long-running, reliable Agent Harness systems.

Core patterns from the hackathon champion's Claude Code config collection — cross-session shared memory, continuous learning, checkpoint evaluation, code maps, and sub-agent orchestration.

From CUA and DOM CUA to Playwright — core implementation approaches for Agent-controlled browsers, comparing Codex, Open-Browser-Use, and ActSpace architectures.

Relying on intuition and experience can get you started, but to go further, we need a rationale for optimization — the design practice of an Agent evaluation module.

From scheduled tasks to ClaudeCode's KAIROS mode — exploring designs that transform Agents from interactive to always-on background runners using Sleep tools and tick-driven events.

From background mounting and incremental reading to macOS sandbox-exec three-layer sandbox — the two critical components for taking Agent Bash tools from demo to production.

From coding Agents to conversational Agents, research Agents to computer-use Agents — evaluation methods, perspectives, and pass@k / pass^k metrics.

Designing Agent tool execution dispatch — 7 state machine states, 4 permission modes, configuration file rule systems, and allowList mechanisms. Referencing ClaudeCode, Gemini-cli, OpenCode, and Kode.

Exploring two core multi-agent collaboration patterns — Agent Team (temporary team tackling) and Agent Room (equal discussion), plus task scheduling and member design.

Complete Agent Bash tool implementation — tool definition, Generator execution pattern, return value design, 24 static security check rules, and three-layer permission verification.

Glob and Grep tool fallback strategies — glob package vs ripgrep, four Grep implementation priorities, ripgrep auto-download mechanism, and AbortController-based timeout control.

Deep analysis of Pi coding Agent's LLM module design: multi-provider adaptation, internal universal message format, EventStream, and Agent execution chains.

Analyzing single-agent and multi-agent pros and cons from architecture design, context management, and tool interaction perspectives, proposing a progressive building strategy.

Pre-compression strategies — tool output trimming (max content limits, layered reading, LLM summarization, progressive reading) and fallback — session history compression.

Deep analysis of ClaudeCode's 8-section compression algorithm and Gemini-cli's 5-point scratchpad approach, plus tool message trimming and middle/oldest strategy selection.

Why Agent evaluation matters so much — the four components, complete workflow, and three scoring methods (code-based, human, model-based) explained in practice.

Core steps for adding Skill support to your Agent — discovery, parsing, usage, management, and the practice of progressive disclosure strategy.