Berlin Brown and Software Development

  Darks Souls 1 and 2 are great games

 Yes, absolutely — the idea of building and training AI models using distributed computing , especially with tiny or resource-constrained devices like Raspberry Pis , is possible in concept and increasingly viable with the right technology stack. There are a few real-world technologies and approaches that relate to what you're describing. 🔹 What You’re Describing: You're imagining a world where: A small, local device (like a Raspberry Pi) can train or fine-tune a small model. These devices are networked , forming a distributed system . They collaboratively train a model or share knowledge (like a federated system). This is an intersection of: Distributed Computing Federated Learning TinyML Edge AI 🔧 Technologies and Concepts to Look Into 1. Federated Learning A decentralized approach to training models where each device trains on its own data locally, and only updates (not raw data) are sent to a central server or aggregator. Used by : G...

 https://github.com/thousandbrainsproject ... Reverse engineering the neocortex 🧠 to revolutionize AI 🤖. An open-source initiative.

 I asked this: 1. Better Search and Retrieval (RAG) Holy Grail: Turn every piece of company knowledge into something easily searchable, understandable, and actionable. LLMs are increasingly being integrated with retrieval-augmented generation (RAG) systems, which combine a company's internal documents (PDFs, wiki pages, logs) with conversational LLMs. Goal: Create “company copilots” — tools that answer employee/customer questions using internal knowledge. 🧠 2. Enterprise-Grade Agents and Automation Holy Grail: Use AI agents to autonomously take action on behalf of employees or customers. Example use cases: Automate Tier-1 and Tier-2 support tickets Handle complex workflows in finance, HR, legal, IT Companies want agents that are safe, traceable, and reliable , not just “smart.” 💬 3. Natural Language Interfaces to Software Holy Grail: Let employees use natural language to interact with business systems (SQL databases, CRMs, analytics, etc.). Think: ...

 Here are responses from Chat GPT.  Just random questions and responses from chatgpt.com What's Already in the Model? When you ask me a question, I first try to answer it based on what I already "know." I was trained on a huge amount of data up until mid-2024, which includes: Wikipedia Gaming wikis (like Fextralife and others) Walkthroughs Forums (like Reddit, GameFAQs) Guides and FAQs So for a lot of questions—especially about older games like Dark Souls 1 (2011)—the answer is already in the model. That means I can often recall details like: Boss names Soul drops Recommended level ranges Item and spell descriptions Lore connections If that had been enough, I would’ve just given you the info directly. 🌐 Step 2: Using the Web (Real-Time Search) In your case, the question was very detailed—you wanted: All bosses & mini-bosses Who give 4,000+ souls Exact soul amounts Recommended player level to fight them Even though I remembered some of that from training, it wasn’t en...