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Products that do what OOTWOracle does

The daily AI oracle for psychedelic medicine intelligence

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  12. 12AO

    Hi! I’m Nathan: an ML Engineer at Mozilla.ai: I built agent-of-empires (aoe): a CLI application to help you manage all of your running Claude Code/Opencode sessions and know when they are waiting for you. - Written in rust and relies on tmux for security and reliability - Monitors state of cli sessions to tell you when an agent is running vs idle vs waiting for your input - Manage sessions by naming them, grouping them, configuring profiles for various settings I'm passionate about getting self-hosted open-weight LLMs to be valid options to compete with proprietary closed models. One…

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  14. 14CA

    ChatCBT is an AI-powered cognitive behavioral therapist for your local Obsidian notes. You have the choice to use OpenAI, or a 100% local model with Ollama for total data privacy. When you're done with your conversation, ChatCBT can automatically summarize the chat into a table listing your negative beliefs, emotions, categories of negative thinking, and reframed thoughts. This way you can start to recognize patterns in your thinking and begin to rewire your reactions to disturbing circumstances. Conversations are stored in markdown files on your local machine, ensuring privacy and…

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  15. 15AS
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    Much of my work right now involves complex, long-running, multi-agentic teams of agents. I kept running into the same problem: “How do I keep these guys in line?” Rules weren’t cutting it, and we needed a scalable, agentic-native STANDARD I could count on. There wasn’t one. So I built one. Here are two open-source protocols that extend A2A, granting AI agents behavioral contracts and runtime integrity monitoring: - Agent Alignment Protocol (AAP): What an agent can do / has done. - Agent Integrity Protocol (AIP): What an agent is thinking about doing / is allowed to do. The problem:…

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    Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…

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