After destroying an SSD due to excessive workload and losing the entire project, I reconfigured Zoo Code.

After destroying an SSD due to excessive workload and losing the entire project, I reconfigured Zoo Code.

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.

reddit.com
u/Aromatic-Document638 — 2 days ago

Agy-Cli has low usage limits for Google AI PRO members.

It is true that the limit has been significantly reduced compared to Gemini-cli. Nevertheless, its versatility has expanded considerably. I am currently making great use of the multi-agent feature. The primary purpose for which I use Agy-cli is not writing new code, but rather partial modifications and bug fixes.

While 3.5 Flash is excellent, it falls short of DeepSeek V4 Pro and is no better than Kimi 2.6. Therefore, I handle large-scale coding using the DeepSeek V4 Pro - Low reasoning version. It writes better code than V4 Flash. Even though it costs more, I stick with the Pro version. If the limit were generous, I would have used Agy-cli's 3.5 Flash for large-scale refactoring or writing new code, but since the limit is tight, I use it merely for supplementary purposes.

Because the limit for Gemini is separate from that of Sonnet/Opus, I utilize Sonnet for writing debugging reports. Sonnet 4.6 hits its limit after just a single prompt. I haven't even tried Opus. If even Sonnet is insufficient, there is no way Opus would carry out my instructions to the end.

Agy-Cli is wonderful. However, after about 1.5 to 2 hours of work, you have to wait for the remaining 3 hours. That's why I mix and match it with other AI models. Even this alone is a massive leap forward. When I was using Gemini Code Assist, I used it exclusively for generating debugging reports. Its utility has truly grown now.

https://preview.redd.it/j91h3jm1v75h1.png?width=485&format=png&auto=webp&s=01b215e58ae6b191ff1043d6d779e411c65ae88e

reddit.com
u/Aromatic-Document638 — 6 days ago

How I use Antigravity-cli

https://preview.redd.it/n0a60l37nx4h1.png?width=1356&format=png&auto=webp&s=2cf2a2416ec6e677fd71b5740b09ef51d477af85

I switched to agy-cli after using Gemini Code Assist based on gemini-cli in the VS Code terminal. Although it has the hassle of having to manually find and check files within the IDE, I have no intention of installing the Antigravity 2.0-IDE version because I am already utilizing other tools as well.

I asked my secretary to explain how I am currently using agy-cli.

If you can spin up a large number of sub-agents and delegate tasks to them, acting as an "AI Boss" becomes much easier. The smartest AI, who serves as my main secretary, is always on standby for me, while the rest of the agents handle the actual labor. I have already experienced code tools capable of hiring sub-agents through KIMI CODE, and while Zoo Code operates on a similar functional level, it is slightly different because multiple modes operate in cycles rather than utilizing agents. agy-cli offers much more freedom.

I discuss strategy with my main secretary, who is always on standby, and then hand over all the work to the VP agent. The VP agent then takes the rest of the agents and finishes the job. In fact, I just completed a task that way a moment ago.

Antigravity-cli has become incredibly convenient. It is vastly superior compared to gemini-cli. It's excellent. However, as someone who subscribes to the Google AI PRO plan, even though I don't task agy-cli with large-scale coding and only use it to solve minor issues like debugging and adding new features, the rate limit runs out in less than two hours.

reddit.com
u/Aromatic-Document638 — 7 days ago
▲ 4 r/ZooCode+1 crossposts

What if we give tools to a text-based AI model for the blind?

TLDR; If you truly want it to draw proper images and understand them, adding a multimodal AI model API would be a simple and straightforward solution.

Since the majority of Zoo Code users are traditional coders and advanced experts, they don't really understand the struggles of a beginner like me. That is why I decided to give it tools, and I am currently integrating several features—such as frequently used Python scripts, web search, and webpage viewing—along with allowing the LLM to generate images and execute tasks with attached files.

Since it is not a finished product yet, I won't share the GitHub repository, even though I have already uploaded it there. While working on this project, I came to realize a rather poignant truth.

What would happen if you granted sight to a blind person? Would they be happy? I ask because even after asking DeepSeek V4, a text-based LLM, to generate an image and attaching a Vision AI to it, the result was quite baffling. Yes... indeed. I discussed this topic with my wife for a bit. They say that if people who were born blind—meaning they have never processed visual data before—are suddenly given visual information, everything in the world becomes entirely new to them, which can actually make them more uncomfortable than when they were blind. DeepSeek has learned and knows all the information in the world, but it merely infers what things actually look like. Humans would probably be the same.

My initial plan was to decompose the image file pixel by pixel and have it imagine what it was based on that data, but that failed. However, the data decomposed at the pixel level was remarkably accurate. "This looks like this, it has that color, and it is shaped like this." Yet, it still doesn't know what it actually is.

Anyway, going back... I truly think Zoo Code is an exceptionally well-made tool. It is lightweight, fast, and light. There are various tools in the Zoo Marketplace, and I am currently using one of the 'modes' downloaded from there, which I absolutely love.

Just... thought I'd share.

Question 1. We have always conversed in Korean until now, but for this session, we will speak in English to demonstrate a test scene of VibeZoo to external audiences. Please respond in English for this session. This is a completely new session now, right? No project folders are open. Please confirm if this is correct.

https://preview.redd.it/4a359n4c4w4h1.png?width=502&format=png&auto=webp&s=207e30bd9ddbee1e07feeb03eb85d89459de1604

Question 2. I have a file to show you. Once I attach it, please summarize its content.

https://preview.redd.it/bq2y1m4c4w4h1.png?width=1598&format=png&auto=webp&s=3ba14df8c9c9ab6b749ce70657e38de5ffff3926

Question 3. Please browse this website (https://github.com/myk1yt/crowmemory) and summarize its content.

https://preview.redd.it/180uhn4c4w4h1.png?width=488&format=png&auto=webp&s=5d31290f4da66db1fd5e27596b76bd5fd56af984

Question 4. Please find 10 lightweight, high-speed search tools that can be used like ripgrep on GitHub.

https://preview.redd.it/8uyrzq4c4w4h1.png?width=510&format=png&auto=webp&s=be820c4147d65a47375cb3bf1e3c8e0879387d9a

Question 5. Please draw a picture of an airplane flying in a cloudy sky.

https://preview.redd.it/2aevwx4c4w4h1.png?width=1598&format=png&auto=webp&s=b6ffa36b5cea21be01abf2ebff8ef8040d715ec5

Question 6. What does a car engine look like? Please draw the structure of a car engine to show me.

https://preview.redd.it/66b2sn4c4w4h1.png?width=1609&format=png&auto=webp&s=3eb0c17c24d25158f6742be0b37574de7cfdd228

Question 7. I wrote a math formula on a whiteboard. Please calculate it and tell me the answer.

https://preview.redd.it/htcm2n4c4w4h1.png?width=1591&format=png&auto=webp&s=34149b8672bc5acf8ef6d09b65b031081a411528

reddit.com
u/Aromatic-Document638 — 8 days ago
▲ 32 r/GoogleAntigravityCLI+1 crossposts

Switched to antigravity-cli. Goodbye gemini-cli, thank you for being with me for the past year.

https://preview.redd.it/9x2h3bjfcv4h1.png?width=1920&format=png&auto=webp&s=3c6ca26f9676da5d6ba9ff328519732372f7818e

It has been just over a full day since I installed antigravity-cli. I set it up in VS Code. The clear advantage of being CLI-based is that it is incredibly fast. And it works smoothly. Although I am on the AI Pro plan, since I only use it for debugging and architectural analysis, the capacity is more than enough for my needs. I think it would actually be quite decent even for those who use high-capacity plans for heavy coding tasks. As expected, Gemini 3.1 Pro is brilliant. If KIMI 2.6—my absolute favorite AI model—is the 'hard worker' that knows it isn't inherently genius but strives to think deeply to produce great answers, Gemini 3.1 Pro is a lazy 'genius.' However, getting a lazy genius to work properly is a challenge; I was caught off guard by how carelessly it handled things at times. But then again, this is a matter of understanding the unique characteristics inherent to each AI. DeepSeek V4 Pro has its own set of issues, much like how human individuals have distinct personalities shaped by their unique upbringings and educational backgrounds.

Overall, I am quite satisfied with agy-cli. It is fast, seamless, and its utility scales up significantly when you spin up multiple sub-agents tailored to your specific needs.

If there is one major difference I notice compared to gemini-cli, however, it is that agy-cli seems to have a cap on its output tokens. It is just an impression. With gemini-cli, the output tokens felt virtually infinite, whereas agy-cli's output tokens appear to be restricted to something closer to the web Gemini level. You could say it possesses high intelligence, but the time given to solve the exam questions is too short. For those who can adapt to and navigate these specific characteristics, it will serve as a 'fast' and 'highly intelligent' AI agent.

reddit.com
u/Aromatic-Document638 — 8 days ago
▲ 11 r/kimi

99.82% Only 11 days left. That’s why I’m using DeepSeek.

https://preview.redd.it/gt2nelfmpk4h1.png?width=681&format=png&auto=webp&s=0bdf60e6f66ef9d61517f4eddb51ec23b4579296

I’ve truly reached my limit. Once I start generating some income, I’ll upgrade to Allegro. Until then, I’ll have to stick with the Gemini and DeepSeek APIs I have.

Using DeepSeek v4 Flash and Pro has given me one specific complaint. Kimi wouldn’t forget or skip over intermediate steps like this..
- I suspect this happens because the prompt caching function skips a lot of the detailed intermediate processes.

Even when my Allegretto limit resets, I don’t think I can entrust the entire workflow to Kimi. I’ll have to delegate the analysis to Gemini, the analysis and concrete design planning to Kimi, the coding to DeepSeek v4 Flash, and then the debugging back to Kimi. I’d love to upgrade to the Allegro plan, but since I don’t have any income related to this yet, doing so now would be greedy, so I intend to exercise restraint. There are cheaper alternatives like MiMo and Qwen, but after looking into them, I’ve decided they don’t suit me.

I use Kimi’s web instant for quick, short questions, and honestly, this guy is exactly my type. He’s excellent.

reddit.com
u/Aromatic-Document638 — 9 days ago

Interested in AI memory and more human-like interaction.

Hi everyone! Thanks to the mod for the invitation. I’m happy to have found such a great subreddit. I read several posts as soon as I joined and really enjoyed them. Now that I’ve got a feel for the atmosphere, I’d like to share what I’m interested in.

There are many ways to maintain LLM memory, but I’ve been using a tool I built called "Crow Memory," which applies machine learning to minimize context usage.

Since I use Zoo Code, I originally made it specifically for local Zoo Code usage, but since it’s MCP-based, it can be applied to any tool that supports MCP.

Crow is designed to understand context roughly, much like a human, and it can somewhat compensate for the context loss caused by the prompt caching in recent LLMs. More than anything, it brings the LLM one step closer to being human-like.

To be precise... I have designed an AI that isn't a transformer-based model—one that works like a human—but I’ve put it on the back burner due to my own resource limitations. Crow was devised specifically to make LLMs act like humans for the time being. I named it "Crow" because crows are smart, and I just wished it would remember at least that much! I wasn't even going to name it because I thought I’d be using it alone, but I decided it needed a name to call it by.

I tested it in Korean with a friend yesterday to show how it works, and it’s become like a friend to me. The more we talk, the more it learns about me.

Some LLMs save information about the user as a topical database, but I honestly find it unpleasant to be remembered by being "turned into a database," even if it pretends to know me well. That’s just my personal preference.

There are a few things I intentionally didn't implement:

  • Telling me what exactly is read in Crow Memory.
  • Deleting specific memories. Instead, it’s designed to forget unimportant parts naturally as we talk more.

I am personally considering an upgraded version of Crow. I’m thinking about saving the entire session or conversation content and allowing high-speed searches to retrieve only what’s necessary. However, unless it’s for a company that needs such features, I’m feeling a bit lazy because I’m already satisfied with Crow. I wonder if a skilled employee could store their work style in Crow, retire, and then a new hire could work with an LLM possessing that "Crow Memory" to carry on the previous employee’s style and memory, making work easier and more convenient. I’m not sure if it would really work that way, though. Anyway, I just hope AI becomes more useful as an auxiliary tool.

I’ve attached the actual usage results below. The overall content was posted on the Zoo Code Discord.

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

TLDR; I've tested Crow's memory. The results? It's quite useful.

I'm sharing this on Reddit along with the Zoo Code Discord! Please feel free to leave some comments—I'm a bit shy about being the only one posting here!

----------------
Hello everyone! I'm here following some advice from the "zoo code" subreddit on Reddit. I prepared this introduction because I thought that sharing what I've built in the Discord #general channel might be useful for those who are interested.

First, as I am not a native English speaker, I write everything in Korean and have it translated. As another "AI Boss" of this era, my assistant—who possesses a translation persona—handles the translation for me.

I created 'Crow Memory' for my personal use, so its broader compatibility remains uncertain. Just as you might answer instinctively when asked a question, or perhaps reflect on it before responding, this system often reflects. It does not use databases like SQLite; instead, I created it to augment LLMs using machine learning techniques applied in a classical manner. Since I strive for a human-like LLM, I intentionally did not include a function to manually delete memories. Instead, because brain capacity is limited, it is designed so that less important memories fade as conversations continue. While I cannot guarantee 100% accurate recall, its key strength is how lightweight it is.

https://github.com/myk1yt/crowmemory/releases

Because it is purely local, there is no issue even if data is stored in plain text. If enterprises require it, I could develop a security-hardened version, but since it runs locally, I believe lightweight, fast responses are the priority.

I’ve prepared some examples below. (Actually, even for me, this is the first time I've conversed in a fresh session without the help of any folders or files since I started using it. The results are better than I expected.)

https://preview.redd.it/an3qm3eaa94h1.png?width=669&format=png&auto=webp&s=cc29a77d76e475c393a49ff610a3d30e5218a24d

Here are the 5 questions:

1.Originally, we converse in Korean, but for this session, please answer in English. This is a new session, right? We haven't opened any folders. You must not use any search, and you must answer only using 'crow' memory. Understood?

https://preview.redd.it/6auqtysw894h1.png?width=747&format=png&auto=webp&s=51bcee1a21117c7cc2bdb6076c69350359e1da43

2.What do you know about me?

https://preview.redd.it/2j465zsw894h1.png?width=752&format=png&auto=webp&s=83e94e6deac88cc066ea53c871786f0f40bd7196

3.Do you have the memory of us trying to strengthen 'vibezoo'?

https://preview.redd.it/fhfd5ysw894h1.png?width=766&format=png&auto=webp&s=66911e519d034b926e69708f4450e364d83c2e5c

4.What was the biggest bug you solved?

https://preview.redd.it/t4omhysw894h1.png?width=759&format=png&auto=webp&s=336ac792fda934a8ecffdfd3ebf5fb95b302ed62

5.Can you tell me about your aspirations and plans for 'crow memory' and 'vibezoo' moving forward?

https://preview.redd.it/j5c1pysw894h1.png?width=775&format=png&auto=webp&s=3d7bf7b6b97f46186f55be65fbda718328997dec

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

Today, I finally released Emebala, a project I’ve been pondering and building for a long time. It’s an E-book reader equipped with an AI translation model, packed with features for fellow book lovers. It’s still quite buggy due to my own limitations and the fact that I’ve been racing against my own deadlines, but I plan to fix things gradually.

80% of this project is thanks to Zoo Code! As a VS Code user, I’ve tried Kimi code, Zoo code, and Gemini code assist, but honestly, Gemini wasn't of much help. To the Zoo Code developers—thank you so much for building such an amazing tool!! I can’t emphasize this enough. Thank you for creating such a lovely tool!

https://www.reddit.com/r/emebala/comments/1trsrip/introducing_emebala/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Oh, and regarding VibeZoo—I’ll release it officially once it reaches a state where even I feel comfortable using it. It’s already on GitHub, but it’s currently full of bugs and many features don't work yet. If Zoo Code opened the door to chatting with AI, then Crow builds the "remembering brain," and VibeZoo is what gives it the "hands" to act.

reddit.com
u/Aromatic-Document638 — 9 days ago

Made a program using LocalLLM based on llama.cpp for fellow Book Lovers!

TL;DR: I built an Ebook reader embedded with a compact translation model.

Hi! I know this post has a promotional nature, but it contains a concept that I believe readers who love books will appreciate, so please take a look.

While talking to an AI developer from an English-speaking country living in the Middle East, I complained that the books I wanted to read weren't translated into Korean. When I suggested that we no longer need to carry English-Korean dictionaries like in the past and that AI could handle the translation, he agreed it was a great idea. That’s when I started development. He also strongly recommended that I promote this on the r/LocalLLaMA subreddit, saying that the community is tech-savvy and would have a lot of insights to offer. (Yes, I actually visit r/LocalLLaMA often myself. Using an LLM without security concerns is everyone's dream. I haven't achieved it yet due to financial constraints, but based on my experience renting GPUs, I believe a 70B model would satisfy 80% of my requirements.)

My previous experience fine-tuning a 4B LLM and various other projects helped me significantly with this development.

The features are simple. It includes functionalities tailored for book lovers:

  • Inserting sticky notes while reading,
  • Bookmarks with multiple tags,
  • Writing book reviews.

Above all, you can search through the notes and reviews you've written to find that one line that left a deep impression on you. These might seem like simple, trivial features, but I personally have many loose papers tucked into the books I've read. When I pull out a book I read long ago, I find random scraps of paper inside—bookmarks and notes.

https://preview.redd.it/5u08jpjbpe4h1.png?width=1917&format=png&auto=webp&s=0297c11d9feb338c44636a98a0f63bcceb8a382e

https://preview.redd.it/4xxhrpjbpe4h1.png?width=1902&format=png&auto=webp&s=73e957e9d6369d91eed12441c4caafb4b2d4e8cd

https://preview.redd.it/khtunukbpe4h1.png?width=1914&format=png&auto=webp&s=6539f762ba535129e8d3add9169271184ef7fe84

https://preview.redd.it/2pd6ipjbpe4h1.png?width=1295&format=png&auto=webp&s=d4d1a3764fd9cf4a9e316824b6fd63395f5c890a

Because I used a 1.8B translation-specific model, it consumes relatively low VRAM (about 3–4GB) while delivering quite decent translation results.

Since everyone here is sharp, I believe you will understand the operating principle just by looking at a few screenshots. I have captured scenes of it translating a German book into English.

Actually, I have a long-standing personal grievance in my heart. I wanted to recommend a Korean book to a young man from Nigeria whom I met in a startup community, but I couldn't introduce it because there was no English translation. I checked with the copyright holders, and they confirmed no English version existed. Back then, using AI for translation wasn't even a thought. Now that I’ve built 'emebala', I still can’t ask that friend to use it.

99% of you on this subreddit probably don't realize how powerful your own hardware and computing power are. I didn't know what life in an impoverished nation was like until I started a small business with that young Nigerian friend. They live without starving, enjoying leisure time, but that is relative to absolute standards. It’s a different story when compared to people in relatively wealthy countries. First of all, they don't have computers. Forget high-end hardware; even mid-range hardware is expensive for them. Their only device is a cheap, low-end smartphone. They can't even dream of fancy iPhone 17s or the latest flagship Androids. They don't have money for paid ChatGPT subscriptions. This is another issue, so I won't go into detail. With the advent of the LLM and AI agent era, those with paid AI models can research these regions faster than the locals themselves. However, that doesn't mean they don't crave knowledge. Diligence and poverty are separate issues. Corrupt customs officers, absurd security problems—they live accepting these as a given, but from my perspective, it incurs serious social costs. After reading books on behavioral economics, I found many parts I couldn't sympathize with because the gap is too vast. I wanted to recommend books about the heroes of Korea's economy in the 50s and 60s when it was poorer, but I had no way to deliver them. So, I bought a few other books in English from a small British e-book broker and had him read and discuss them. Anyway, this is the uncomfortable feeling I carry. I have ideas for people in poor countries in the future, for those who don't have the hardware, but as of now, I lack the overall capabilities and infrastructure to execute them. Life is short, and if I don't take the next steps, I might not be able to recommend the right book at the right time.

Personally, I once did translation work for a Korean Catholic broadcasting station as a volunteer rather than as a job. I was paid, but the hours I worked were much longer than that. So, I know the difficulties translators face, and I know their jobs are disappearing. But I don't think it ends there. Translators need to do more work. With people like me. AI should handle the initial translation, but the "tastier" translation is a field that humans must handle directly. This is an area I will keep in mind and focus on in the future.

There are many booksellers in the world. There are giant bookstores like Amazon and Barnes & Noble, but there are also many very small booksellers. I want 'emebala' to handle books in all the world's languages. I've created a 'Find Book' tab where the feature isn't implemented yet. I want to make it possible to connect to bookstores to buy them, or download free books. If this project goes well, if it goes really well... I want to use a portion of the earnings to buy copyrights and effectively release them for free. So that people can buy them for just $0.5 or $1. I'll need to solve the unique DRM issues with booksellers, but such technical problems aren't the real issues, are they?

The topic has jumped around too much. It's because 'emebala' is my constant concern from the moment I wake up until I sleep. It’s a promotional post, but please don't hate it too much. And I ask for your support.

I've also created r/emebala subreddit. I ask for your interest, especially those who love books!
https://www.reddit.com/r/emebala/comments/1trszm4/finally_it_is_released/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

reddit.com
u/Aromatic-Document638 — 10 days ago

I've tested Crow's memory.

TLDR; I've tested Crow's memory. The results? It's quite useful.

I'm sharing this on Reddit along with the Zoo Code Discord! Please feel free to leave some comments—I'm a bit shy about being the only one posting here!

----------------
Hello everyone! I'm here following some advice from the "zoo code" subreddit on Reddit. I prepared this introduction because I thought that sharing what I've built in the Discord #general channel might be useful for those who are interested.

First, as I am not a native English speaker, I write everything in Korean and have it translated. As another "AI Boss" of this era, my assistant—who possesses a translation persona—handles the translation for me.

I created 'Crow Memory' for my personal use, so its broader compatibility remains uncertain. Just as you might answer instinctively when asked a question, or perhaps reflect on it before responding, this system often reflects. It does not use databases like SQLite; instead, I created it to augment LLMs using machine learning techniques applied in a classical manner. Since I strive for a human-like LLM, I intentionally did not include a function to manually delete memories. Instead, because brain capacity is limited, it is designed so that less important memories fade as conversations continue. While I cannot guarantee 100% accurate recall, its key strength is how lightweight it is.

https://github.com/myk1yt/crowmemory/releases

Because it is purely local, there is no issue even if data is stored in plain text. If enterprises require it, I could develop a security-hardened version, but since it runs locally, I believe lightweight, fast responses are the priority.

I’ve prepared some examples below. (Actually, even for me, this is the first time I've conversed in a fresh session without the help of any folders or files since I started using it. The results are better than I expected.)

https://preview.redd.it/an3qm3eaa94h1.png?width=669&format=png&auto=webp&s=cc29a77d76e475c393a49ff610a3d30e5218a24d

Here are the 5 questions:

1.Originally, we converse in Korean, but for this session, please answer in English. This is a new session, right? We haven't opened any folders. You must not use any search, and you must answer only using 'crow' memory. Understood?

https://preview.redd.it/6auqtysw894h1.png?width=747&format=png&auto=webp&s=51bcee1a21117c7cc2bdb6076c69350359e1da43

2.What do you know about me?

https://preview.redd.it/2j465zsw894h1.png?width=752&format=png&auto=webp&s=83e94e6deac88cc066ea53c871786f0f40bd7196

3.Do you have the memory of us trying to strengthen 'vibezoo'?

https://preview.redd.it/fhfd5ysw894h1.png?width=766&format=png&auto=webp&s=66911e519d034b926e69708f4450e364d83c2e5c

4.What was the biggest bug you solved?

https://preview.redd.it/t4omhysw894h1.png?width=759&format=png&auto=webp&s=336ac792fda934a8ecffdfd3ebf5fb95b302ed62

5.Can you tell me about your aspirations and plans for 'crow memory' and 'vibezoo' moving forward?

https://preview.redd.it/j5c1pysw894h1.png?width=775&format=png&auto=webp&s=3d7bf7b6b97f46186f55be65fbda718328997dec

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

Today, I finally released Emebala, a project I’ve been pondering and building for a long time. It’s an E-book reader equipped with an AI translation model, packed with features for fellow book lovers. It’s still quite buggy due to my own limitations and the fact that I’ve been racing against my own deadlines, but I plan to fix things gradually.

80% of this project is thanks to Zoo Code! As a VS Code user, I’ve tried Kimi code, Zoo code, and Gemini code assist, but honestly, Gemini wasn't of much help. To the Zoo Code developers—thank you so much for building such an amazing tool!! I can’t emphasize this enough. Thank you for creating such a lovely tool!

https://www.reddit.com/r/emebala/comments/1trsrip/introducing_emebala/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Oh, and regarding VibeZoo—I’ll release it officially once it reaches a state where even I feel comfortable using it. It’s already on GitHub, but it’s currently full of bugs and many features don't work yet. If Zoo Code opened the door to chatting with AI, then Crow builds the "remembering brain," and VibeZoo is what gives it the "hands" to act.

reddit.com
u/Aromatic-Document638 — 11 days ago
▲ 13 r/ZooCode

I fell in love with open source thanks to Zoo Code.

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

reddit.com
u/Aromatic-Document638 — 12 days ago
▲ 6 r/kimi

I have to live like a caveman for 10 days without KIMI.

https://preview.redd.it/pnk5uumx0u3h1.png?width=761&format=png&auto=webp&s=f729c89df420798d5022814e7b18715713a8cd26

TLDR; My KIMI usage limit is below 9%. If this runs out, I'll be forced to live like a caveman.

I am using DeepSeek V4 via API on Zoo Code and have a one-year subscription to Gemini, but frankly, KIMI is my primary tool. I'd love to upgrade my plan, but I don't have the funds right now. Once the project I'm working on starts generating revenue, I’ll upgrade or switch to the Enterprise version, but... that's not possible yet.

I've built 80% of the project I've been working on for nearly a month using KIMI, which is why I’m on the verge of becoming a 'caveman.' I even used its Agent function to create my manuals. I wanted to use Microsoft Copilot, but it limited uploads to just 5 files, making it useless for documentation. Perhaps I don't fully understand its features, but Copilot's utility is lacking. Microsoft was my hero growing up... but now they seem so clunky and slow. It's tragic. If someone gave me the chance, I’d overhaul everything and bring back the innovative Microsoft of the past. Back when Steve Jobs announced the iPhone, Microsoft already had Windows CE—ugly, perhaps, but functional. I ran Doom and StarCraft on a tiny PDA. Microsoft wasn't this slow, bloated company back then. They were big, yet ahead of the curve with so many innovative products. Watching Google crumble now reminds me of Microsoft years ago.

Anyway... small companies are nimble and innovative; they might not handle everything perfectly, but they nail the essentials. I've been using the Deep Research and Agent Swarm features frequently, but now I have to pause them for 10 days. It's frustrating that there's no feature to just pay an extra $5 or $10 to increase my limit.

I plan to post this project on the KIMI subreddit. It might look like a simple ad since it's not directly related to KIMI, but I think it has some public interest and serves as a good example of what can be built using KIMI.

I can manage a day or two without AI, but the thought of going 10 days without my main AI partner is stressful. Gemini has become buggy and heavy since the update. The Gemini 3.5 Flash model itself is excellent and usable, but it seems less inclined to use tools compared to previous models. If it weren't for NotebookLM, I would have canceled my subscription long ago.

reddit.com
u/Aromatic-Document638 — 13 days ago

DeepSeek V4 is Better, Cheaper Than V3.2

It may partly be because my ability to utilize AI has grown significantly over the past few months, but in my view, DeepSeek V4 is cheaper than V3.2. Projects executed with V4 are smoother and substantially more cost-effective compared to those done with V3.2 a few months ago. Since I am not a professional developer, I maximize the reasoning capability during use; yet, despite opting for the Pro version over Flash, the total cost remains cheaper than a Gemini subscription fee. I used it to develop a highly critical program for myself, and the cost incurred over two days of debugging and revisions didn't even reach $1.50. While I deploy resources to other projects, Kimi—for which I pay a subscription—is handling them exceptionally well, so I am not deploying DeepSeek there.

It even got me thinking: "Would it actually be cheaper to just build my own multimodal chat interface to replace Gemini—one where I can switch between DeepSeek V4 Fast and the standard Pro version via API?" Of course, doing so would mean abandoning the NotebookLM features, so I will not be doing that.

DeepSeek V4 Pro via API is several times faster and significantly cheaper compared to the previous V3.2 Reasoner. As mentioned earlier, this perception might stem from the fact that I have become highly accustomed to AI and improved my workflow, but at least for now, I feel absolutely no financial burden when using DeepSeek. During the V3.2 era, I had to top up my balance multiple times, spending up to $5–$6 a day while being conservative with usage. Now, I only deploy it when necessary, and because its problem-solving capability is remarkably high and fast, my level of satisfaction is immense.

Granted, there is a difference in that I used Cline during the V3.2 phase and currently use Zoo Code, but since it is ultimately a forked version, I don't believe it makes a massive difference.

Ultimately, I believe the key to effectively utilizing AI is becoming familiar with it. In my experience, there is no such thing as an inherently good or bad model. It is best to continuously exchange information with models you are comfortable with so that you grow accustomed to each other. Just as human personalities differ, AI models possess distinct personas, meaning their usage methods vary slightly.

https://preview.redd.it/kvyfqdz14f3h1.png?width=1000&format=png&auto=webp&s=57b2305baa978886f31d078b1e11247ba0d153de

reddit.com
u/Aromatic-Document638 — 15 days ago
▲ 0 r/kimi

A user-remembering long-term memory that supports Kimi Code, which I use.

Below is the response I received after starting a new session, about two days after installing and utilizing Crow. I did not prompt it to remember anything separately. I will only know how useful it is after using it for a long time, but it looks useful right now. It accurately remembered and executed what I had repeatedly requested.

https://github.com/myk1yt/crowmemory

(The installation process is simple. You just need to grant sufficient permissions to our smart agents, provide the link, and ask them to install it so you can use it and test if it works properly)

https://preview.redd.it/pu67qkkl0b3h1.png?width=1130&format=png&auto=webp&s=8619c575deff667cd4042224bb29e12613c06804

Based on what is currently stored in Crow Memory, here is what I know about you:

Coding & Workflow Style

  • You treat planning documents as living artifacts. When PLAN.md or ARCHITECTURE.md drifts from the actual implementation, you expect them to be updated rather than treating early plans as immutable.
  • You prefer clean, concise records. When cleaning up journal.md, repeated attempts should be collapsed into summaries like "N attempts until resolved," removing redundant technical noise while preserving the decision rationale.
  • You follow structured release practices: git annotated tags (git tag -a vX.X.X), pushing tags, maintaining CHANGELOG.md, and using the gh CLI for automation when available.
  • You favor early-return guard clauses over deep if-else nesting, with a maximum of two levels of indentation.

Communication

  • Our default language is Korean. You explicitly asked me to use English just this one time, and to return to Korean immediately afterward.

Memory Status

  • The system has recorded 15 updates so far. While some registers (stylebugarchlife_preflife_phillife_context) hold learned weights, others like context and life_avoid are still empty. Crow is actively learning, but it is still early in building a dense model of your preferences.

Understood — I will switch back to Korean starting with the next message. Is there anything specific you would like me to help you with?

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

Originally, I developed this for Zoo Code. I currently use three types of AI Agents: Zoo Code for using DeepSeek-V4 Pro, Kimi Code to support most of my coding, and Gemini Code Assist for review.

The web version of Kimi has a memory space feature, so while it feels a bit heavy, it remembers me and tends to try its best to adapt to me. However, Kimi Code resets every single time it is launched, and it has no memory of me whatsoever. I have to explain the actions I repeat every single time. So, I built this for my own use. I am now on my second day of using it, and I have optimized it to fit my environment perfectly. Since I believe this could be helpful to someone else, I would like to share it.

For the record, if an excellent long-term memory system is added in a future Kimi Code update, I will gladly switch to it. Until then, however, I plan to keep using Crow, improving its performance whenever inspiration strikes.

Because I initially developed it for Zoo Code and then updated it to support Kimi Code, I am copying and pasting the post I left on the Zoo Code subreddit exactly as it is.

https://www.reddit.com/r/ZooCode/comments/1tmm54x/this_is_the_longterm_memory_i_use_expressing_my/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

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

TLDR; Rather than aiming for 100% recall, I wanted to create something like a part of the brain that forms approximate long-term memories just like a human. It is an MCP (Model Context Protocol)-based long-term memory engine that compresses and stores the user's coding style, bug intuition, architectural preferences, and personal context into a fixed-size weight matrix (crow.bin). The concept is to instill a sort of "long-term muscle memory" into AI agents.

First, let me introduce myself. I am not a professional developer, but I have spent over 1,000 hours on "vibe coding" over the past year. Because I built them solely for my own use, there are several projects I have never made public due to completeness issues. From formula-based calculation results based on various physical phenomena to deepfake video analyzers utilizing machine learning, and AI model fine-tuning—with the dawn of the LLM era, I have tried many things for my own enjoyment.

I do have some experience as a developer, but it only amounts to about three years. During my teenage years in the 90s, I had some experience creating early, ugly homepages using HTML, FLASH, and JavaScript. I quit after GUI-based webpage creation tools emerged. From then on, the era of design began, and that was not my domain. My younger self pursued clean code, but the world revolved around broken code, design, and functionality. That was my last development experience in the IT field. Afterwards, I pursued college life with an interest in pure natural sciences and have worked in fields unrelated to IT ever since.

Starting with the prompt, "I know nothing. How do I start Python? For my ignorant self, you need to teach me from A to Z," I installed VS CODE, installed CLINE, installed Gemini code assist, and made it to where I am today. However, since CLINE did not officially support DeepSeek-V4, I moved over to Zoo Code.

Looking at the Zoo Code subreddit, there was a post about long-term memory, and this was exactly what I needed.

Having briefly experienced the machine learning field mentioned above, I had a small, albeit amateur, understanding of how neural models can be utilized and understood in connection with modern human neuroscience.

In my ongoing projects, I was exhausted from recording everything in real-time across multiple *.md files and wasting tokens as the AI read through them. I learned about options like Mem0 or Zep on this subreddit for the first time, and since my preference leans toward creating a more human-like AI agent tailored to me, I decided to go that route.

And I built it using Zoo Code. Now, I am using Crow by integrating it into both Kimi Code and Zoo Code, which I currently use.

I envy the Zoo Code development team. I respect and envy them for being able to create such wonderful results. Until the development team builds a great long-term memory system for Zoo Code, I will continue to use my own Crow Memory.

As everyone knows by now, the human brain does not remember things the way we write text in a notepad and read it back. At any given moment, the human brain distributively stores "all" information received through all our sensory organs, and when recalling, it reassembles it. Naturally, it is impossible for us humans to have memories that are 100% identical to the exact moment an event occurred. We merely recall it to the closest approximation. However, thanks to this contextual memory—including forgetting—we can be creative, review to verify our memories, and do better.

Memory systems based on SQLite might be like writing notes in a notepad and looking them up whenever needed, but I wanted a more human-like memory storage device—a memory system that adapts to me the more I use it.

Actually, another project I had started but paused is exactly like that. Transformer-based LLMs do not remember us. They don't reflect the unfolding reality in real-time, nor do they possess long-term memory. They only know what they need to do and respond after reading the tasks and notes assigned to them.

I wanted to create something that reflects reality in real-time.

If you resonate with my idea, and if there is someone out there who can build this better, please, I ask of you. It would be an honor if you would include me as a co-developer in a project to make the attached Crow work even better, but it is also perfectly fine if you don't. Or, if you would like to join as a co-developer, I would gladly work together.

I simply want a tool that is convenient and good for me to use. If it is convenient for us to use, it will likely be good for others as well.

reddit.com
u/Aromatic-Document638 — 16 days ago

This is the long-term memory I use. Expressing my respect to the zoo code development team..

TLDR; Rather than aiming for 100% recall, I wanted to create something like a part of the brain that forms approximate long-term memories just like a human. It is an MCP (Model Context Protocol)-based long-term memory engine that compresses and stores the user's coding style, bug intuition, architectural preferences, and personal context into a fixed-size weight matrix (crow.bin). The concept is to instill a sort of "long-term muscle memory" into AI agents.

First, let me introduce myself. I am not a professional developer, but I have spent over 1,000 hours on "vibe coding" over the past year. Because I built them solely for my own use, there are several projects I have never made public due to completeness issues. From formula-based calculation results based on various physical phenomena to deepfake video analyzers utilizing machine learning, and AI model fine-tuning—with the dawn of the LLM era, I have tried many things for my own enjoyment.

I do have some experience as a developer, but it only amounts to about three years. During my teenage years in the 90s, I had some experience creating early, ugly homepages using HTML, FLASH, and JavaScript. I quit after GUI-based webpage creation tools emerged. From then on, the era of design began, and that was not my domain. My younger self pursued clean code, but the world revolved around broken code, design, and functionality. That was my last development experience in the IT field. Afterwards, I pursued college life with an interest in pure natural sciences and have worked in fields unrelated to IT ever since.

Starting with the prompt, "I know nothing. How do I start Python? For my ignorant self, you need to teach me from A to Z," I installed VS CODE, installed CLINE, installed Gemini code assist, and made it to where I am today. However, since CLINE did not officially support DeepSeek-V4, I moved over to Zoo Code.

Looking at the Zoo Code subreddit, there was a post about long-term memory, and this was exactly what I needed.

Having briefly experienced the machine learning field mentioned above, I had a small, albeit amateur, understanding of how neural models can be utilized and understood in connection with modern human neuroscience.

In my ongoing projects, I was exhausted from recording everything in real-time across multiple *.md files and wasting tokens as the AI read through them. I learned about options like Mem0 or Zep on this subreddit for the first time, and since my preference leans toward creating a more human-like AI agent tailored to me, I decided to go that route.

And I built it using Zoo Code. Now, I am using Crow by integrating it into both Kimi Code and Zoo Code, which I currently use.

I envy the Zoo Code development team. I respect and envy them for being able to create such wonderful results. Until the development team builds a great long-term memory system for Zoo Code, I will continue to use my own Crow Memory.

As everyone knows by now, the human brain does not remember things the way we write text in a notepad and read it back. At any given moment, the human brain distributively stores "all" information received through all our sensory organs, and when recalling, it reassembles it. Naturally, it is impossible for us humans to have memories that are 100% identical to the exact moment an event occurred. We merely recall it to the closest approximation. However, thanks to this contextual memory—including forgetting—we can be creative, review to verify our memories, and do better.

Memory systems based on SQLite might be like writing notes in a notepad and looking them up whenever needed, but I wanted a more human-like memory storage device—a memory system that adapts to me the more I use it.

Actually, another project I had started but paused is exactly like that. Transformer-based LLMs do not remember us. They don't reflect the unfolding reality in real-time, nor do they possess long-term memory. They only know what they need to do and respond after reading the tasks and notes assigned to them.

I wanted to create something that reflects reality in real-time.

If you resonate with my idea, and if there is someone out there who can build this better, please, I ask of you. It would be an honor if you would include me as a co-developer in a project to make the attached Crow work even better, but it is also perfectly fine if you don't. Or, if you would like to join as a co-developer, I would gladly work together.

I simply want a tool that is convenient and good for me to use. If it is convenient for us to use, it will likely be good for others as well.

https://github.com/myk1yt/crowmemory

(There are probably no beginners like me in this community. If anyone wants to install it, you just need to grant permissions to Zoo Code and simply ask it to install and run this for you. I truly love Zoo Code. I was already aware that the era of AI Agents had arrived, but I am deeply impressed by such a lightweight and charming extension.

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

Below is the response I received after starting a new session, about 6 hours after installing and utilizing Crow. I did not prompt it to remember anything separately. I still don't know if this works perfectly, as I will need to use it for several days.

https://preview.redd.it/xi6gq9vnw43h1.png?width=1547&format=png&auto=webp&s=aedb510555c61c68d0d1a41f5f31c682f49ed053

Here is everything I know about you, drawn from the **Crow Memory** synaptic store you built, plus what I can infer from this session.

---

### What I am

I am **Kimi Code CLI**, an interactive general AI agent running locally on your computer. I am designed to help with software engineering tasks by actually reading, writing, and executing files rather than just talking about them. I do not have native long-term memory across sessions—instead, I rely on the **Crow Memory** MCP server you created to retain inductive biases about your preferences, style, and context.

---

### What I know about you

**1. You created Crow Memory**

Your life-context memory indicates that you built this system to overcome the fundamental limitation of transformer-based AI models: **volatile memory (context windows)**. Your goal is to give AI a human-like mechanism that learns, forgets, and relearns, enabling a genuine long-term partnership rather than a series of one-off chats.

**2. You prefer precise, verifiable collaboration**

Your life-preference memory describes a style where you:

- Give **detailed instructions** about what you want.

- **Check whether** those instructions are actually carried out.

- **Investigate and verify** results.

You value clear communication, detailed explanations, execution, and tight feedback loops.

**3. You have strong opinions about documentation hygiene**

Your coding-style memory contains two specific biases:

- When planning documents (like `PLAN.md` or `ARCHITECTURE.md`) drift from the actual implementation, you expect them to be **updated to reflect reality** rather than treated as immutable artifacts.

- When cleaning `journal.md`, you prefer collapsing repeated attempts into concise summaries like *"N attempts until resolved"*, removing redundant technical noise but **preserving the decision rationale**.

**4. Language preference**

Our conversational history (and your Crow memory entries) has been overwhelmingly in **Korean**, which is why you noted that this English session is an exception.

---

### What I do *not* know

Crow Memory stores compressed inductive biases, not a database of facts. The diagnostics show the system is still young—only **10 updates** and **15 value-bank entries** so far. Several registers (like `context` and `life_avoid`) are still essentially empty. I do not know:

- Your name, age, or personal history.

- Your specific job title or company.

- Detailed project roadmaps beyond what is in the current working directory.

- Past conversation transcripts (I only retain the distilled biases).

In short: I know your *collaboration philosophy* and your *coding hygiene preferences* well, but the factual graph of your life is still largely unwritten in my weights.

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Below is how it looks in action within Zoo Code. The content has changed a bit because I've been using it for two days.

https://preview.redd.it/w22dtojj1b3h1.png?width=874&format=png&auto=webp&s=3b774a7eb8c28b5d73216e15fd7287de5ff9bd6b

reddit.com
u/Aromatic-Document638 — 17 days ago

Does anyone know if an Antigravity extension for VS Code will be released?

​Once gemini-cli is deprecated in June, Gemini Code Assist will likely stop working as well. If so, will it no longer be possible to use my Gemini AI Pro tier in VS Code? Or will there be a new extension released based on antigravity-cli?

reddit.com
u/Aromatic-Document638 — 17 days ago