u/LifeTelevision1146

Hey all,

I've recently taken a dip into the trading side of crypto for the last 6 months (wasn't trading earlier, just assimilating, various assets). I've read and heard horrifying stories about how interaction with one tainted wallet, which in turn had interacted with a tainted asset or dex, got their wallets frozen.

So this wallet risk keeps a lot of people awake at night. So I tried a few free tools which miss things or give vague risk scores without an explanation. Am I being too paranoid?

So what do you guys do before sending a funds? Just trusting the other party? Or telegram chats? Or is there something I'm missing?

Cheers

Hey all,

I've recently taken a dip into the trading side of crypto for the last 6 months (wasn't trading earlier, just assimilating, various assets). I've read and heard horrifying stories about how interaction with one tainted wallet, which in turn had interacted with a tainted asset or dex, got their wallets frozen.

So this wallet risk keeps a lot of people awake at night. So I tried a few free tools which miss things or give vague risk scores without an explanation. Am I being too paranoid?

So what do you guys do before sending a funds? Just trusting the other party? Or telegram chats? Or is there something I'm missing?

Cheers

Hey all,

I've recently taken a dip into the trading side of crypto for the last 6 months (wasn't trading earlier, just assimilating, various assets). I've read and heard horrifying stories about how interaction with one tainted wallet, which in turn had interacted with a tainted asset or dex, got their wallets frozen.

So this wallet risk keeps a lot of people awake at night. So I tried a few free tools which miss things or give vague risk scores without an explanation. Am I being too paranoid?

So what do you guys do before sending a funds? Just trusting the other party? Or telegram chats? Or is there something I'm missing?

Cheers

Ladies and Gents,

I've built an MCP-Server for local LLMs. Of course works on almost all SBCs.

Would love for you all to try and give me a feedback.

• It's local and sovereign.
• 3.3MB RAM footprint.
• All your data is stored locally.
• Compresses tokens 30X for storage and it's it automatically injected into your LLMs when they run.
• Continuous context.

It's open source, AGPLv3 for individuals and yes ARM version is there for download.

Addedly I also built an UI, for you to enjoy the MCP in action visually.

https://modgudr.com

Cheers

Hi All,

So I'm onto an experiment, crypto only lifestyle. What are the best places for me to buy merchandise using Monero?

Top priority at the moment, tech merchandise.

Cheers

Hi All, What memory architectures are being used by agent teams? And how efficient are they?

I've been running Modgudr in production and wanted to share the design, because I think there's a real gap in how most agent frameworks handle memory.

The problem with RAG for financial agents:

RAG retrieves by semantic similarity. For a chatbot, that's fine. For an agent managing real capital, it means:

• Stale information can surface if it's semantically similar to the current query.
• There's no mechanism to detect when a retrieved fact contradicts something established more recently.
• Temporal authority is invisible, the vector store doesn't know that last week's risk posture superseded last month's.

What Modgudr does instead?

There are three staged before anything enters the memory.

• Recall: check new information against existing memory.
• Verify: if there's a conflict, flag it, resolve it, record the resolution reason permanently.
• Commit: write only verified, non-contradictory facts to the knowledge graph, with source + G1–G5 confidence grade.

Session start injects a compact AAAK-compressed context block (typically 200 tokens or lesser, up to 30× compression) so your agent picks up exactly where it left off, without retrieval lottery.

Why this matters for on-chain agents?

Governance agents need to remember prior vote rationale so they can't be fed a re-framed version of a previously rejected proposal. Risk agents need to know why a parameter was adjusted, not just that it was. Execution agents need to learn from failure across sessions, not just within one.

Integration:

It's an MCP server. If you're on ElizaOS, one config entry:

{
  "plugins": ["@fleek-platform/eliza-plugin-mcp"],
  "settings": {
    "mcp": {
      "servers": {
        "modgudr": {
          "type": "sse",
          "url": "http://localhost:7432"
        }
      }
    }
  }
}

Any MCP compatible client works. Binaries for Linux (x86_64, ARM64) and Windows. 3.3MB RAM footprint.

AGPLv3 open source. Commercial licence for proprietary embedding.

Full writeup on the architecture and three DeFi use cases:

https://modgudr.com/blog/defi-ai-agent-memory-verified-mcp/

Source and downloads:

https://modgudr.com

Happy to answer questions on the AAAK compression format or the verification gate implementation.

A few days ago I posted about Modgudr, a local-first memory layer for LLMs. A lot of you asked how to actually use it with your models day-to-day without writing JSON commands into a terminal.

>So I built Clotho.

Clotho is a simple UI that sits in front of Modgudr and Ilamcetcenni (my sensory memory tool) and connects them to whatever local model you're already running. That's it. No new model, no new infrastructure, just a front door to the memory stack I've already built.

**What it does:**

* Auto-detects your local model runner on startup. Ollama, LM Studio, Jan, GPT4All, Koboldcpp, LocalAI. You don't configure anything, it just finds what's running. * Connects to Modgudr and Ilamcetcenni automatically so every conversation is remembered, verified, and injected into the next session. * Lets you upload images, audio, video, and documents directly to Ilamcetcenni so your model can remember what it sees and hears, not just what you type. * Shows you the live memory tape on the right side of the screen with grades (G1 through G5) so you can see exactly what your model knows and how confident it is. * First-run wizard walks you through getting everything started, including an option to have Clotho launch Modgudr and Ilamcetcenni for you automatically.

It's designed for people who don't want to touch a terminal after setup. If you can run Ollama, you can run this.

Same deal as everything else I've built: open source, AGPLv3, free for individuals, runs entirely on your machine.

Download: [https://modgudr.com/clotho\](https://modgudr.com/clotho)

(Linux, Windows, and ARM builds available)

**The full family is so far**:

* Modgudr, language memory * Ilamcetcenni, sensory memory * Clotho, the UI that ties them together

**Still under construction**:

1. Mac version (working on getting my hands on a Mac).

2. Therivu: a router (small LLM) + injector for smarter context management, between multiple LLMs.

3. Telurai integration in V2.

As always, genuinely love feedback, criticism, and feature ideas. What would make this actually useful in your daily workflow?

Cheers

A few days ago I posted about Modgudr, a local-first memory layer for LLMs. A lot of you asked how to actually use it with your models day-to-day without writing JSON commands into a terminal.

>So I built Clotho.

Clotho is a simple UI that sits in front of Modgudr and Ilamcetcenni (my sensory memory tool) and connects them to whatever local model you're already running. That's it. No new model, no new infrastructure, just a front door to the memory stack I've already built.

What it does:

• Auto-detects your local model runner on startup. Ollama, LM Studio, Jan, GPT4All, Koboldcpp, LocalAI. You don't configure anything, it just finds what's running.
• Connects to Modgudr and Ilamcetcenni automatically so every conversation is remembered, verified, and injected into the next session.
• Lets you upload images, audio, video, and documents directly to Ilamcetcenni so your model can remember what it sees and hears, not just what you type.
• Shows you the live memory tape on the right side of the screen with grades (G1 through G5) so you can see exactly what your model knows and how confident it is.
• First-run wizard walks you through getting everything started, including an option to have Clotho launch Modgudr and Ilamcetcenni for you automatically.

It's designed for people who don't want to touch a terminal after setup. If you can run Ollama, you can run this.

Same deal as everything else I've built: open source, AGPLv3, free for individuals, runs entirely on your machine.

Download: https://modgudr.com/clotho

(Linux, Windows, and ARM builds available)

The full family is so far:

• Modgudr, language memory
• Ilamcetcenni, sensory memory
• Clotho, the UI that ties them together

Still under construction:

1. Mac version (working on getting my hands on a Mac).

2. Therivu: a router (small LLM) + injector for smarter context management, between multiple LLMs.

3. Telurai integration in V2.

As always, genuinely love feedback, criticism, and feature ideas. What would make this actually useful in your daily workflow?

Cheers

AI/LLMs are intellect driven for most part and less intelligence driven.Intellect is a direct result of belief and intelligence is a direct result of knowing. I've been thinking, future durable intelligence may not belong to the most powerful systems. It may belong to the most preservable systems.

I've been working on a tool called Modgudr that gives LLMs a persistent, verified memory. Everything runs locally and your data never leaves your machine.

What it does:

• Persistent Memory: Remembers conversations across sessions, so you don't have to re-introduce yourself.
• Verification: Checks facts before storing them, and rejects anything outdated or contradictory.
• Compression: Squeezes context down ~30x without losing meaning (3.3MB RAM footprint).
• Sovereign: 100% local first, open source(AGPLv3) and free for individuals.

This isn't a startup or a SaaS. This is a passion project born out of the need for AI that remembers me, without selling my data to someone else. I'm not trying to make money, I just built something useful and I'm sharing it.

About: https://modgudr.com/about

Link: https://modgudr.com

Link: https://modgudr.com/ilamcetcenni

I'd genuinely love your feedback, criticism, or ideas for improvement. What memory features do you wish local LLMs had?

Under Construction:

1. MAC version of both tools.
2. Therivu - A Router + Injector

Cheers