https://www.novopathmedical.com/ - evidence-based guidance of diet, exercise, and mental health via SMS for end users. Clinician-guided feedback to bots.
https://github.com/rush86999/atom - ai agent workforce. Harder than expected. There are so many issues with sync with 3rd party apps. Need to launch the first one, so this is getting put on the back burner. landing page: https://atomagentos.com/
You would think git history should be the first thing an agent would look at, as they make so many mistakes before they get to the correct answer. They don't.
I haven't measured, but documenting bug fixes and architecture seems to help, along with TDD patterns, including integration tests.
I would probably add it to Claude.md to look for all of the above when tackling a new bug.
A strong checks and balances without influence of bias, relationships, and politics can be implemented using a 2-way blind system where:
1. decision makers (of sound judgement) are not aware of any identifiable information related to any users on whom the decision will be made, nor of each other.
2. Users are not aware of the decision makers who will decide on them, nor of each other.
Possibly AI can play a role here, but a strong system of checks & balances would be a prerequisite for this.
The justice system would definitely benefit from this.
I would create a custom <canvas> component that integrates into your IDE or create a plugin and add AI accessibility via logs. I 'm doing something similar to my current app that I'm building: https://github.com/rush86999/atom/blob/main/docs/CANVAS_AI_A...
I do understand what you're saying, but that's impossible to resonate with real-world context, as in the real world, each person not only plays politics but also, to a degree, follows their own internal world model for self-reflection created by experience. It's highly specific and constrained to the context each person experiences.
Game theory, at the end of the day, is also a form of teaching points that can be added to an LLM by an expert. You're cloning the expert's decision process by showing past decisions taken in a similar context. This is very specific but still has value in a business context.
Basically the conclusion is LLMs don't have world models. For work that's basically done on a screen, you can make world models. Harder for other context for example visual context.
For a screen (coding, writing emails, updating docs) -> you can create world models with episodic memories that can be used as background context before making a new move (action). Many professions rely partially on email or phone (voice) so LLMs can be trained for world models in these context. Just not every context.
The key is giving episodic memory to agents with visual context about the screen and conversation context. Multiple episodes of similar context can be used to make the next move. That's what I'm building on.
The biggest problem is internal knowledge and external knowledge systems are completely different. One reason internal knowledge is different it is very specific business context and/or it's value prop for the business that allows charging clients for access.
To bridge this gap, the best approach is to train agents to your use case. Agents need to be students -> interns -> supervised -> independent before they can be useeful for your business.
Marketing line: Atom is your conversational AI agent that automates complex workflows through natural language chat. Now with Computer Use Agent capabilities, Atom can see and interact with your desktop applications, automate repetitive tasks, and create visual workflows that bridge web services with local desktop software.