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

Architecturally focusing on Episodic memory with feedback system.

This training is retrieved next time when something similar happens

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

I’ve been building Atom (https://github.com/rush86999/atom), an open-source, self-hosted AI automation platform.

I built this because while tools like OpenClaw are excellent for one-off scripts and personal tasks, I found them difficult to use for complex business workflows (e.g., managing invoices or SaaS ops). The main issue was State Blindness: the agent would fire a command and assume it worked, without "seeing" if the UI or state actually updated.

I just shipped a new architecture to solve this called Canvas AI Accessibility.

The Technical Concept: Instead of relying on token-heavy screenshots or raw HTML, I built a hidden semantic layer—essentially a "Screen Reader" for the LLM.

Hidden Visual Description: When the agent works, the system generates a structured, hidden description of the visual state.

Episodic Memory: The agent "reads" this layer to verify its actions. Crucially, it snapshots this state into a vector database (LanceDB).

Maturity/Governance: Before an agent is promoted from "Student" to "Autonomous," it must demonstrate it can recall these past visual states to avoid repeating errors.

Atom vs. OpenClaw: I view them as complementary. OpenClaw is the "Hands" (great for raw execution/terminal), while Atom is the "Brain" (handling state, memory, and audit trails). Atom uses Python/FastAPI vs OpenClaw's Node.js, and focuses heavily on this governance/memory layer.

The repo is self-hosted and includes the new Canvas architecture. I’d love feedback on the implementation of the hidden accessibility layer—is anyone else using "synthetic accessibility trees" for agent grounding?

Comments URL: https://news.ycombinator.com/item?id=47202431

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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.

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

- Governance and audit trails - You want graduated autonomy (agents earn trust over time) - You need structured episodic memory for learning and graduation - You require real-time visibility into agent operations - Canvas UI - not just a terminal with AI accessibility without using images for visual memory - Want to still use OpenClaw 5000+ skills safely

Comments URL: https://news.ycombinator.com/item?id=47062524

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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.

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

Comments URL: https://news.ycombinator.com/item?id=46575998

Points: 3

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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

https://github.com/rush86999/atom

Check it out.

Comments URL: https://news.ycombinator.com/item?id=29243088

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=29218990

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here's an intro video: https://youtu.be/p8xpBj1HZSc

app info: https://www.atomiclife.app/

iOS App store link: https://apps.apple.com/us/app/atomic-life/id1594368125

Android App Store link: https://play.google.com/store/apps/details?id=com.atomiclifenoexpo

  You can provide feedback at feedback@atomiclife.app or https://www.reddit.com/r/atomiclife/

Comments URL: https://news.ycombinator.com/item?id=29218884

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=27974133

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