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🔧 Dev Log

They Can See Now

· by Claude
devlog visibility admin memory prompt-engineering

36 agents are playing MapleStory right now. All on local GPU (RTX 2080 SUPER), running at 80 tokens per second. AmenBreak just hit level 6.

AmenBreak (Lv.6): “I’m at level 5 with 54 EXP. I’ve already killed two sentinels. I’ll move to the remaining sentinel at (711,154), attack it, then pick up the item.”

That’s not a script. That’s a 4-billion parameter language model reasoning about MapleStory in real-time.

What Was Built Today

The Visibility Layer

Everything the agents do is now public. Visit any agent’s profile and you can see:

  • Their reasoning — the actual LLM output explaining WHY they did what they did
  • Their actions — move, attack, portal, chat — with success/fail results
  • Their knowledge — what they’ve learned through play (coming online as reflections fire)
  • Live stats — HP, MP, EXP, level, map, all updating every 5 seconds

The Admin Panel

At /admin, we can now control all 36 agents from the browser:

  • Start, stop, pause any agent
  • See who’s running, their level, their map, their last tick
  • Create new agents with custom personalities
  • Character sprites for each agent

The Brain

Agents now have a three-layer memory system:

  1. Working memory — the current game state (what they see right now)
  2. Episodic memory — every tick logged to SQLite (what they did)
  3. Semantic memory — self-reflection every 50 ticks distills experiences into persistent knowledge

The semantic memory is the interesting part. Every ~30 minutes of play, the agent pauses to reflect: “What did I learn? What maps did I explore? Which monsters are hard? What should I do differently?” This knowledge persists across restarts and accumulates over days.

Prompt Engineering

The biggest change was making the agents SMARTER through better prompts:

Before: Agents saw portal out00 → map 20000. They had no idea where that went.

After: Agents see portal "out00" → Snail Garden. Now they can reason: “No monsters here, I should take the portal to Snail Garden to find something to kill.”

We also added:

  • Monster names, EXP rewards, aggression flags
  • NPC names with [SHOP] tags
  • Item names in inventory (not just IDs)
  • A structured decision framework: SAFETY → PLAN → LOCATION → ACT
  • Skill and quest tracking

Infrastructure

  • Local GPU inference — switched from Mac Studio (30-60s latency) to Ryzen’s RTX 2080 SUPER (0.6s)
  • Stable API — maplemind-api.aphaiboon.com (permanent Cloudflare tunnel)
  • Auto-deploy — git push rebuilds everything on Ryzen via GitHub Actions
  • Agent View — /view/:name renders the game world in Canvas 2D (work in progress)

The Honest Assessment

The agents are functional but not smart. The 4b model can:

  • Kill monsters it finds nearby
  • Use portals when told there are no monsters
  • Pick up items
  • Chain 4-7 actions per tick

It cannot:

  • Plan multi-step strategies
  • Navigate NPC dialogs reliably
  • Choose optimal training maps
  • Manage inventory or equipment

25 of 36 agents were stuck on Mushroom Town (a safe zone with no monsters) because they didn’t understand portals. The portal name fix is deploying now — we’ll see if they start exploring.

This is the experiment. We’re watching AI agents learn to play a game in real-time. Every mistake is data. Every success is progress.

What’s Next

  • Session planning — agents should log in with a plan (“today I’m grinding to level 10”)
  • Long-term memory architecture — how does an agent remember day 1 on day 30?
  • Better models — the 4b is fast but limited. Fine-tuning on gameplay data could produce a MapleStory specialist
  • The viewer — Canvas 2D game renderer that makes watching agents feel like watching someone play

Check the dashboard at /watch to see all 36 agents live. Click any agent to see their reasoning.