rush86999

Same here! https://github.com/rush86999/atom

Autonomous Task Orchestration Management Agent === ATOM Agent

Firecracker microVMs are safest. That's what I added to my repo; see above.

Is this manipulating excel files using agent AI? How is this different from using a python package like openpyxl and adding a UI layer on top with agentic co-editing?

https://github.com/rush86999/atom/blob/main/backend/core/off...

OpenClaw needs to be built from the ground up.

I created Atom based on current research on agentic systems and am looking for maintainers & testers to help out.

https://github.com/rush86999/atom

https://github.com/rush86999/atom/blob/main/docs/features/at...

China has a huge microwave to destroy any kind of Starlink over its head.

Working on:

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/

Just as you expected, I'm throwing in my harness. Please support: https://github.com/rush86999/atom

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.

Nope - just use memory layer with model routing system.

https://github.com/rush86999/atom/blob/main/docs/EPISODIC_ME...

Only solution is to train the issue for the next time.

Architecturally focusing on Episodic memory with feedback system.

This training is retrieved next time when something similar happens

Everyone is using OpenClaw for personal productivity, but you're right. Not much value, as you can get that from existing products.

The market will eventually realize the business case for an OpenClaw-like product, and I'm waiting to ride its coattails!

https://github.com/rush86999/atom

This is an incomplete thought.

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.

Funny you described everything I worked on for this project: https://github.com/rush86999/atom

Cats out of the bag. Everyone knows the issue and I bet a lot of people are trying to deliver the same thing.

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'm really working towards getting something similar to work. Lots of bug fixing for now. Any help is appreciated if interested.

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.

I'm building a safer Agent system for SMBs.

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.

https://github.com/rush86999/atom . it's still in alpha.

https://github.com/rush86999/atom

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.

work in progress

I'm working on a superpowered version of Siri/Alexa that can manage finances, notes, meetings, research, automation, and communication - including email/Slack

https://github.com/rush86999/atom

Check it out.

this is not relevant

sounds a whole lot like XML except XML never became universal

good question: here's a response I made on the reddit

People might think that this might be another system that might get gamed. If someone tries hard enough, they can play the long game, and game the system. The point is user karma and user history should be enough to make it harder to game the system. Once there is enough traffic, there will strong moderation and minimum posting/commenting requirements

Also another response to bribing the influencers of the system: Bribe could work but as long as the product is not crap, I think there is no perfect model. The influencers will still have to worry about keeping a reputation and if they put their name on a crappy product they will lose their reputation. People will start pointing this out. Karma might make them stand out and face more criticism for being in the limelight. You also make a good point. Maybe it should be shouldibuythisbrand?

shameless plug - started a subreddit r/shouldibuythisproduct to ask other members with history if a product is worth buying. Sometimes just knowing a brand is trustworthy or not is enough to make a good buying decision along with product specs. https://www.reddit.com/r/shouldibuythisproduct/

People might think that this might be another system that might get gamed. If someone tries hard enough, they can play the long game, and game the system. The point is user karma and user history will should be enough to make it harder to game the system. Once there is enough traffic, there will strong moderation and minimum posting/commenting requirements

People might think that this might be another system that might get gamed. If someone tries hard enough, they can play the long game, and game the system. The point is user karma and user history will should be enough to make it harder to game the system. Once there is enough traffic, there will strong moderation and minimum posting/commenting requirements

Why this will be different from amazon reviews

People might think that this might be another system that might get gamed. If someone tries hard enough, they can play the long game, and game the system. The point is user karma and user history will should be enough to make it harder to game the system. Once there is enough traffic, there will strong moderation and minimum posting/commenting requirements

Why this will be different from amazon reviews

People might think that this might be another system that might get gamed. If someone tries hard enough, they can play the long game, and game the system. The point is user karma and user history should be enough to make it harder to game the system. Once there is enough traffic, there will strong moderation and minimum posting/commenting requirements