• Built a portfolio risk engine from scratch – optimized for PM workflows for equity-oriented portfolios, deployable on Mac or Windows, and structured for scale.

    Parallelized architecture with modular components. No legacy code. Connects directly to your existing portfolio positions (whether that’s an excel file or a database).

    Key Features:

    • Forecasted Risk: VaR, CVaR, multi-horizon EWMA/GARCH/EGARCH vol forecasts, marginal & forecast risk contributions. Suitable for fat tails.

    • Realized Risk: max drawdown, VaR, CVaR, up/down captures, tracking error, rolling metrics, correlation matrix, vol contribution.

    • Factor Exposure: traditional factors like quality/value/size, and custom themtic factor decomposition (via proxy construction & regression)

    • Position Sizing: Volatility-based position sizing with forward-looking risk constraints. Can add whatever sizing methodology you wish (like risk parity).

    Built using Cursor + Claude Sonnet (state of the art AI coding platform) to accelerate development—AI handled code scaffolding and test harnesses, I provided direction and owned the math and investment logic.

    Targeted at small-to-mid-sized funds and PMs without internal quant teams. DM if you want to see it in action or walk through how it could integrate with your stack.

  • 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!

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  • Anyone know if there’s a best practice for this in the professional finance world? I can visually test for normality easily, but I’m now running into situations where visually testing is not appropriate.

    This algorithm has been performing well just assuming a normal distribution for certain things, but I’ve recently realized that at least one of the datasets that I’m making this assumption on is actually at least bi-modal.

  • The study of primate teeth and their fossilized counterparts continues to be a promising area for ecological and climate research. As techniques in isotopic analysis advance, we can expect these ancient dental records to paint even more detailed pictures of past climates.

    So, the next time you think about teeth, remember that they’re not merely for chewing. In the world of science, they’re keys to unlocking the mysteries of our planet’s past. Whether you’re a climate enthusiast, ecological researcher, or a curious reader, primate teeth offer a fascinating glimpse into a time long before us.

  • – **Don’t Forget Metadata**: Along with the title, ensure your meta descriptions are engaging and feature-rich with keywords.

    – **Long-tail Keywords**: They might not fit in the title but could be excellent in descriptions or blog content related to your launch.

    By integrating these strategies, your digital product can achieve higher visibility, draw in target users, and set a firm foundation for success. Remember, SEO isn’t just about seeing your product rise to the top of search results; it’s about creating meaningful connections with your audience, right from the get-go. So dive into your keyword research and start crafting that compelling, SEO-optimized title today!