r/ZooCode

https://preview.redd.it/2tur013za16h1.png?width=944&format=png&auto=webp&s=d8b4e1a3f6ee160f9958348045b753ea7823e19e

TLDR; I saved my Zoo Code configurations, which contain my personal know-how, to GitHub so that I can reconfigure everything immediately even if my computer suddenly breaks down.

AI agents are incredibly fast and great. On the other hand, I realized they exhibit destructive performance on DRAMless SSDs. As usual, around the time I had nearly completed the project with Zoo Code running, I ordered it to organize folders and files for future scalability. Simultaneously, in another VS Code window, I opened a different workspace and was working quickly using ripgrep.

And then, I saw a blue screen for the first time in a really long time. After that, Windows failed to boot. Whether the boot-related part of the SSD was glitching or the controller was about to fail, the SSD would intermittently appear and disappear in the CMOS settings. Eventually, I turned off the computer and opened it up (I use an all-in-one PC, so the space is much tighter than a regular desktop). The SSD was burning hot as if it had caught fire. Even after letting it cool down for about an hour and reconnecting it, it just heated up again, and the symptom of appearing and disappearing in CMOS remained the same.

Personally, I was very serious about this project because it's something I'm pursuing heavily. For years, I had been syncing my work to OneDrive and Google Drive, but this was the very first time I stopped syncing and used the shortest path, C:\projects\<project path>... and that's when this catastrophe struck.

It was the exact moment when I thought, "This is 95% complete!!" after migrating from a simple prototype Electron-based app to a Shared C++/Rust Core / Flutter setup, while adding multiple features and fixing bugs at the same time...

(Just as a side note, in the past, even if I had grand ideas, I couldn't implement them myself. After handing money multiple times to subpar developers—honestly, they don't even deserve to be called developers; they are just fools making a living out of having experienced programming and knowing slightly more than others about what others don't know—I thought software creation was a field I couldn't touch.

Now, I've found a tool that fits me perfectly called Zoo Code. Although brainstorming and planning are still required just as much as before, there is an AI that implements things better than those people who called themselves programmers, and I absolutely love this era. Starting with buying a book in the 90s, typing HTML code into Notepad, changing the extension, and displaying something on Netscape, I have years of experience developing several homepages. However, I was too young to sell my experience and skills to anyone. I remember refusing an adult who offered to pay me, saying I didn't need money, and getting a nice dinner instead.

Since my great-great-grandfather, grandfather, and father were all teachers and civil servants, I never even thought about my ability to do something as a "skill" or a means to make money. But later, I went to university to become a natural scientist, and even that experience became meaningless once web editors emerged.)

Everything I upgraded by staying up all night for a week is gone, and I am left holding only an old version. I have to start over from the beginning now. I consider this an opportunity given by God to make an even better migration, and I am going to start again.

However... since all my Zoo Code configurations, which contained all my past know-how, were lost, this time I saved everything in a private GitHub repository.

While Zoo Code has an 'Export Settings' feature, I wanted more than that, so I had the secretary in my data center back up everything to the repository.

And so, I am sharing this. I hope this experience of mine helps someone out there who, like me, is engaging in development work without coming from a traditional developer background.

If you check the settings window in Zoo Code, you'll find a massive variety of custom configurations. There are also plenty of useful tools in the Zoo Market. I am learning them one by one as well. Below is a summary of my configurations that I had the secretary in my data center organize. If it looks too complex to read, you can simply copy and paste it, and start by asking your AI a question like, "This is someone else's configuration—how does it compare to mine, what are the differences, and what can we learn or improve from it?"

--------------------

# Zoo Code Custom Setup — Complete Overview

Here is everything we have customized in Zoo Code, organized into 5 areas.

---

## 1. 🤖 AI Provider & Model Settings (Prompt Enhancement)

**Storage location**: External JSON file referenced by `zoo-code.autoImportSettingsPath` in `settings.json`

**Current PC**: The file does not exist on this machine, but the **currently active values** in this session are:

| Setting | Value |

|---------|-------|

| API Import Method | External JSON file via `autoImportSettingsPath` |

| Active Model | `deepseek-v4-flash` |

| Context Window | 1,000,000 tokens |

| Max Tokens | 384,000 |

| Reasoning Effort | `high` |

| Image Support | Not supported |

| Prompt Cache | Supported |

| Input Price | $0.14/M tokens |

| Output Price | $0.28/M tokens |

> ⚠️ **These settings are NOT included in the GitHub repository.** (API keys excluded for security; file path differs per machine.)

> On a new PC, you must manually enter the API key and select the model in Zoo Code settings UI.

---

## 2. 🎭 6 Custom Modes

**File**: [`configs/custom_modes.yaml`](myownsettings/configs/custom_modes.yaml) **→ Included in GitHub** ✅

Each mode is an independent AI role. Only Orchestrator+Crow delegates tasks; modes never delegate to each other.

| Mode | Role | Permissions |

|------|------|-------------|

| **Orchestrator + Crow** 🪃 | Command all tasks, delegate to sub-modes, maintain long-term memory | read, command, edit, browser, MCP |

| **Code** 💻 | Write/modify code, iterate build/test cycles | read, command, edit, browser, MCP |

| **Architect** 🏗️ | System design and technical specification | read, command, edit, browser, MCP |

| **Debug** 🪲 | Root cause analysis and bug fixing | read, command, edit, browser, MCP |

| **Ask** ❓ | Answer technical questions, explain code | read, command, browser, MCP |

| **Project Research** 🔍 | Explore codebase structure and produce analysis reports | read |

**Common rules for all modes**:

- MUST call `switch_mode('orchestrator-crow')` after completing work

- MUST NOT delegate tasks to other modes

- MUST report results clearly

---

## 3. 🔌 2 MCP Server Configurations

**File**: [`configs/mcp_settings.json`](myownsettings/configs/mcp_settings.json) **→ Included in GitHub** ✅

| Server | Port | Role | Always-Allow Tools |

|--------|------|------|-------------------|

| **Crow Memory** | 9020 | Long-term memory across sessions (save/restore context) | 10 (crow_recall, crow_ingest, etc.) |

| **VibeZoo** | 9027 | All-in-one dev toolkit (code search, review, docs, web search, vision, etc.) | 39 (search_codebase, web_search, etc.) |

---

## 4. ⚙️ VS Code Settings (zoo-code related)

**File**: [`configs/vscode-settings-export.json`](myownsettings/configs/vscode-settings-export.json) **→ Included in GitHub** ✅

| Setting | Value | Description |

|---------|-------|-------------|

| `zoo-code.debug` | `true` | Verbose logging of AI decision-making |

| `zoo-code.allowedCommands` | `["*", "powershell"]` | System commands AI can run (all allowed + PowerShell) |

| `zoo-code.deniedCommands` | `[]` | No blocked commands |

| `zoo-code.customInstructions` | *(see below)* | AI behavior rules |

**customInstructions content**:

```

1. CROW MEMORY WORKFLOW: Read it ONCE at the start of a session,and save to it ONCE at the end of a session. Additionally,feel free to use it mid-session whenever necessary.
2. VIBEZOO WEB SEARCH: Use the VibeZoo tool freely whenever youneed external information. Do NOT hallucinate facts.Always fetch the latest data via VibeZoo.

```

> ⚠️ **`autoImportSettingsPath` was excluded from export because the path differs per machine.**

---

## 5. 📜 Skill & Bootstrap

### autopilot-workflow Skill

**File**: [`configs/skills/autopilot-workflow.yaml`](myownsettings/configs/skills/autopilot-workflow.yaml) **→ Included in GitHub** ✅

Automated workflow that Orchestrator+Crow follows:

```

1. Brainstorm (always) → wait for user approval
2. Research (conditional) → project-research mode
3. Architecture (conditional) → architect mode
4. Plan Review (conditional) → debug mode (max 3 loops)
5. Implement (always) → code mode
6. Final Review (conditional) → debug mode

```

### autosetup Bootstrap Skill

**File**: [`bootstrap/autosetup.yaml`](myownsettings/bootstrap/autosetup.yaml) **→ Included in GitHub** ✅

When you tell the Orchestrator "install my GitHub settings", it runs:

1. Execute `setup.ps1`
2. Verify Crow Memory on port 9020
3. Verify VibeZoo on port 9027
4. Confirm all 6 custom modes are registered

---

## 6. 🚀 Setup Script

**File**: [`setup.ps1`](myownsettings/setup.ps1) **→ Included in GitHub** ✅

Single PowerShell 7+ script that:

1. Checks prerequisites (Git, Python, Node.js)
2. Clones Crow Memory from GitHub and installs dependencies
3. Clones VibeZoo from GitHub and installs dependencies
4. Copies config files (custom_modes.yaml, mcp_settings.json, modes/*.md, skills/*.yaml)
5. Merges zoo-code settings into VS Code settings.json (preserves existing settings)
6. Verifies MCP server ports (9020, 9027)
7. Prints summary with results

---

## ✅ GitHub Repository Inclusion Summary

| Component | Included? | Notes |

|-----------|-----------|-------|

| 6 custom mode definitions | ✅ `configs/custom_modes.yaml` |

| MCP server settings | ✅ `configs/mcp_settings.json` |

| VS Code zoo-code settings | ✅ `configs/vscode-settings-export.json` | `autoImportSettingsPath` excluded |

| Per-mode customInstructions | ✅ `configs/modes/*.md` |

| Skill workflow | ✅ `configs/skills/autopilot-workflow.yaml` |

| Bootstrap autosetup | ✅ `bootstrap/autosetup.yaml` |

| Setup script | ✅ `setup.ps1` |

| **API key / provider config** | ❌ **Not included** | Excluded for security; must be entered manually on new PC |

| **Model selection / Max Tokens / Temperature** | ❌ **Not included** | Stored in external JSON file (`autoImportSettingsPath`); path differs per machine |

---

**Bottom line**: The GitHub repo contains all environment configuration (modes, MCP, VS Code options, skills) but does **NOT** include the actual AI provider connection details (API key, endpoint, model selection, max tokens, temperature, etc.). Those must be configured manually in the Zoo Code settings on each new PC.

Ifi have a different model for architect and code and it asks me to switch i will get an error because it changed model and the new one doesn't have context. it would be nice if switching kept the same model or they were separate, like what kilo does

Anybody know of an MCP to speak to chatGPT using your membership to create, view, and edit images?

I'm aware and often use OpenAI's image-2 API, but ChatGPT can write great image prompts and image edit prompts when using the normal web interface.

Is there an MCP that can do this that connects to your ChatGPT subscription?

Thanks

Originally, I didn't get the appeal of open source. That was back when I couldn't develop things myself. But now, I'm fully enjoying its charm, delighted by the fact that I can personally add the features I want.

With the advancement of LLMs, it’s been about a year since I started feeling like everyone has been handed infinite power. The "crow" I made last time for Zoo Code is currently being used well (it now performs actions I frequently do on its own in new sessions. Not always, but it’s become very human-like).

And now, I am building a tool for Zoo Code called "VibeZoo." I made it for better communication with Zoo Code. For example, it allows me to draw on a whiteboard or paste a screenshot to show it, making it understand before continuing the conversation. Since I'm building it by adding things I need as I go, it still has a lot of bugs, so I can't share or recommend it just yet. Once I get to a point where I can use it well without bugs, I'll share it on the subreddit again.

I had a real-life example just now, but I missed recording it, so I quickly made a sample.

Here is what I requested: "Explain to me with a picture what a car is. I have no prior knowledge of what it is."

https://preview.redd.it/vqwhax2w934h1.png?width=1104&format=png&auto=webp&s=a360f6a8653878b2a518d9f7ba6b113e213f0a94

It's still unpolished and has many parts that need to be fixed, but I will continue to improve it as I use it myself.

https://preview.redd.it/fedr3yk8b34h1.png?width=606&format=png&auto=webp&s=e0c09c91420b97a7c391b866d412afff3e752af0

Zoo frequently runs commands in my workflow. I like to allow harmless commands (find, ls, grep, cd, aw, head, tail) commands that it uses to explore while it's solving problems.
The issue - is that if I enable "execute", it completely ignores this list and does what it wants.

https://preview.redd.it/ssyqe1jbp24h1.png?width=465&format=png&auto=webp&s=7d7d93e7d1f50bc9ea4e2f9732b34762d257a115

For example, in this environment, not only did the agent "make" (allowed), it also executed the program it built (NOT in this list)

Does anyone else have this issue?

Wish to say to the new ZooCode team that I'm really appreciating this tool. It's been a lot of work to get to a point where it's a serious development tool, but it's starting to all come together now. So much appreciated.

A quick overview for anyone willing to put in a bit of time, for what I think is an affordable, decently fast and accurate coding setup. I'm running Windows 11 with WSL, docker and LM Studio. PC has two RTX 3090's on a Asus ProArt Creator MB, that does 2x PCIe 4.0 x8. Latest release of LM Studio now does MTP, so it can run the Unsloth MTP models that are faster (As of a few days ago). I'm running the biggest model I can fit in 48GB VRAM, Qwen3.6 35B A3B MTP Q8_K_XL with full 261K context. These Qwen models are seriously good. By hooking ZooCode up to a local Docker MCP Tookit, I have the online search tools and some custom docs. This greatly enhances it's capability, pretty close to what Github Copilit is like. Applied the fixed chat template from here: https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templateshttps://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates to the Qwen3.6 models and now they seem more reliable. The MTP models from Unsloth are maybe 40-60% faster than the standard models. I've seen others saying MTP with the MoE models isn't great, but the Unsloth models have the MTP model built in (I believe) and when the same model is select as both main and draft speculative decoding model, it now has been working reliably and faster. I've created a set of local agent instructions which explain the project environment, coding conventions, libraries, etc. In all, this setup is now capable of creating a fairly complex python project with many files, folders, etc. Another thing I've found is to write out the instructions in text, then tell the model to create the model instructions and to ask questions. You then can answer anything that's not clear or specified and the resulting agent instructions for the project seem to work quite well. Also be sure to set the checkpoint feature, it's been quite helpful. A key point for why I use LM Studio is that it has LM Link, so I'm actually coding on my work laptop and LM Studio running locally on my Laptop to connect using LM Link to LM Studio on my home PC. Docker runs locally on the laptop as well as on home PC, two copies each for ZooCode running for home and work setups.

Update: After more testing with bigger workloads I've not been able to fix a 5min timeout from LM Studio that has started occurring with bigger workflows. I've switched back to Unsloth Studio, using OpenAi Compatible connection, same setup for the rest and connect my two machines with WireGuard running on the router and the Windows Laptop. The speed of Unsloth backend seems much faster too. More testing needed, but so far so good.