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I moved from chatgpt to Claude a few weeks ago and once thing I’ve noticed is that I run the chat limit way faster (pro). I feel like I’m just demolishing chats as I can hit the context limit on roughly one chat a day on pro while ChatGPT would take me probably close to a week if I’m really pushing in that specific chat. Though it does forget stuff at times it’s easier to nudge a reminder or paste in the specific context/doc again vs load up all the context again especially if you really loved how it was writing.

It’s fine for me because I’ve reached a point where jumping chats is fine since I mainly work with projects now.

But If I had started my business with Claude then I don’t think I would’ve been as far along as I am as the ai really does change its tone the longer you talk to it.

Another inconvenience is that when working with longer docs Claude gets confused and doesn’t change stuff etc. which also forces a new chat.

So for me ChatGPT is better for longer docs and more stable while Claude gives high quality bursts if you’re willing to work with running out of context and some editing errors with artifacts.

Just curious about how you all are handling the limits etc. or if this is all just me lol

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# Border Patrol Wants Advanced AI to Spy on American Cities | A U.S. Border Patrol “Industry Day” deck also asks for drones, seismic sensors, and tech that can see through walls

It’s not just individuals who need to step up. Platforms hosting this content must shoulder responsibility too. Social media giants are increasingly investing in AI tools themselves to weed out false content. But it’s an ongoing battle.

The saga of Senator Lee serves as both a warning and a call to action. It’s crucial to sharpen our skepticism and better control how information is proliferated online. Until then, we must all keep our eyes peeled. In a world where appearances can deceive, knowing how to see through them is more essential than ever.

The Jane Street episode is a valuable classroom for traders at all levels. It underscores the importance of agility in financial strategies and the constant readiness to adapt to market signals. Even well-oiled strategies need evaluation in real-time, anticipating market moves that could disrupt even the most meticulous plans.

So next time you’re studying the market, whether you’re a novice or a seasoned player, consider the dynamics of hedging with a fresh perspective. Remember, the art of balance and timing in the trading world can make or break even the big names.

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Exploring these intricate market dynamics helps us appreciate the fascinating dance between strategy and market reality. Let this serve as a reminder that behind every market move, there’s a rich tapestry of decision-making.

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!