• – In total, this period saw a whopping 76,837 Moons burned.

    The trend continues, with Week 65 already recording nearly 100K Moons burned in just the first two days—an exciting tease for what’s to come in future updates.

    ### The Moon Burn Leaderboard

    With burning, comes rankings. On Arbitrum One, about 637,000 Moons have been sacrificed to date, valued at $69,175. These aren’t just any Moons: they’re sourced from the open market, emphasizing their value and the community’s dedication.

    Arbitrum Nova takes the prize though, with around 3.06 million Moons burned, valued at an impressive $332,310. Clearly, Moons aren’t just tokens but valuable assets within this ecosystem.

    ### Moon Decline or Controlled Burn?

    Since the start of 2025, there’s been a 1.07% decrease in Moon supply—from 79,436,210 Moons in circulation on January 1st to 78,582,364 Moons by July 22nd. Arbitrum Nova accounts for 314,517 of these burns, with Arbitrum One not far behind at 512,329 Moons. It’s a controlled burn, signaling a healthy, self-regulating ecosystem.

    ### Join the Lunar Adventure

    As Moons burn, the rCryptoCurrency community continues to evolve. Embracing this journey, you’ll find not only financial intrigue but enjoyable engagement and governance participation. Whether you’re hoarding or burning, every Moon tells a part of this ongoing story.

    Stay tuned for the next update. Until then, keep earning, burning, and voting—a digital odyssey awaits!

  • Untitled Post
  • Hey, this is my second post about my Solana MEV project. I’m using Jito.

    Lot of people on the last post commented that without being the leader validator I can’t to frontrunning attacks. And, they’re not totally right. I’ll explain why later, but let’s get into my issue. Here are two issues I have:

    1. Sometimes, when my attack goes through, I get in a problem that does this:

    1. Buying: Successful.
    2. Selling: Err.

    or just buying gives err.

    The error (i think) is because of the Associated Token Account that NEVER got initialized.

    Look at this TX:

    https://solscan.io/tx/2HdZVK4Q7PtjbiBkjwHP6LxCtMo2rSoe9kAGKxHUoseBetPrt1oLShpMSj3nnXXA3VbwZZ7VvSiJCfPBuQpaY3fY

    How can I initialize a Token Account?

    2. Second issue:

    Sometimes my TX does this:

    1. Buying goes through and gets validated by Jito (and on my console output gives me the successful message, status 200)
    2. Then when selling, TX gets created, and somehow it says it got validated (status 200), but on solscan.io i never see that TX. Maybe that happens because I did set as blockhash slot to expiry 10?

    I checked back the Jito tip and it’s correct, did someone encounter my same problem, when doing sniping, automated txs.. ?

    Let’s get back where I was explaining why doing MEV on Solana without being a validator is possible.

    Using Rust and Jupiter, I do everything in around 80ms or less. Then, I send the TX directly to Jito (that sends directly to the validator). Using the highest tip (99 percentile) it gets me the highest probability of my TX that goes through. I even talked with experts from Jito, who do this for living, and they told me it is possible but they can’t guarantee your code will get you to a profitable bot.

    A TX usually needs around/more than 400ms. Enough time using paid services like gRPC, private RPC endpoints, …

  • Hey r/LocalLLaMA,

    Just wanted to share some exciting news for anyone here who’s into deep, long-form roleplaying. The team behind [Astrsk](https://astrsk.ai), a desktop app for RP that’s been in development for about six months, has just announced they are going **fully open source** under the GPL license!

    As a fan of the project, I think this is a huge deal for the community.

    **The most important link first:** [https://github.com/astrskai/astrsk](https://github.com/astrskai/astrsk)

    [demo](https://reddit.com/link/1m868na/video/zk1ui4ctytef1/player)

    **So, what is Astrsk and why is it interesting?**

    At its core, Astrsk is a UI for RP, but its main differentiator is the **agentic workflow**. I’ve been following it, and the concept is very cool because it moves beyond a simple prompt-response loop.

    To make this concrete, let’s look at the default workflow it comes with, called **SAGA**. It’s a four-step pipeline that mimics how a human Game Master thinks, breaking down the task of generating a response into logical steps.

    Here’s how it works:

    1. **Step 1: The Analyzer Agent**
    * **The Job:** This is the GM’s logical brain. It looks at what your character just did and analyzes it against the current game state.
    * **In Practice:** It answers the questions: “Is the player’s action possible? What are the immediate consequences based on game rules or a dice roll?” It validates the action and determines the outcome.
    2. **Step 2: The Planner Agent**
    * **The Job:** This is the creative storyteller. It takes the Analyzer’s output and designs the narrative response.
    * **In Practice:** It decides how NPCs will react to the player’s action (e.g., with anger, surprise, or a counter-move). It plans the scene, sets the emotional tone, and prepares the key information for the next agent.
    3. **Step 3: The Actor Agent**
    * **The Job:** This is the performer. It takes the Planner’s script and turns it into the actual text you read.
    * **In Practice:** It writes the scene narration and performs the detailed dialogue for one main NPC, giving them a distinct voice and personality. Other NPCs are handled through the narration, keeping the focus clear.
    4. **Step 4: The Formatter Agent**
    * **The Job:** This is the final editor.
    * **In Practice:** It takes the text from the Actor and cleans it up with simple markdown. It automatically wraps actions in italics, dialogue in “quotes”, and adds **bold** for emphasis, making the final output clean and easy to read without changing the content.

    This pipeline approach allows for incredible consistency and detail. And since you can assign different models to different agents (a key feature!), you could use a large, powerful model for the creative Planner and a faster, smaller model for the structured Analyzer.

    **How does it compare to the greats like SillyTavern / Agnaistic?**

    From what I’ve seen, while projects like ST/Agnaistic are amazing for chat-based RP, Astrsk seems to aim for a different goal. It feels less like a chat interface and more like a tool for collaborative storytelling, almost like having an AI Dungeon Master powered by a framework of agents.

    **Key Features:**

    * **Agent-based generation:** The core of Astrsk, designed for more coherent and long-term storytelling.
    * **Sleek, Customizable UI:** A really polished interface where you can tweak settings directly in the app. No more digging through config files to change things.
    * **Per-Agent Model Assignment:** This is a killer feature. You can assign a different LLM endpoint to each agent.
    * **True Cross-Platform Support:** The team provides native builds for Windows, macOS, and Linux. This means you can just download and run it — no need to be an engineer or fight with dependencies to get started.
    * **Backend Agnostic:** Connects to any OpenAI-compatible API, so it works with your existing setup (Oobabooga, KoboldCPP, etc.).

    **The Open Source Move**

    According to their announcement, the team wants to build the project out in the open, getting feedback and contributions from the community, which is fantastic news for all of us. The project is still young, but the foundation is solid.

    I’m not affiliated with the developers, just a user who is really excited about the project’s potential and wanted to share it with a community that might appreciate the tech.

    Definitely worth checking out the [https://github.com/astrskai/astrsk](https://github.com/astrskai/astrsk), especially if the idea of an agentic approach to RP sounds interesting to you. The team is looking for feedback, bug reports, and contributors.

    Cheers!

  • * Comments will be sorted by newest first.

     

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