Agentic Human Shared Attention Module ASHA

This is a self-reported project named Agentic Human Shared Attention Module ASHA, submitted to the OpenAI 2026 hackathon by a single founder, Peter Albrecht. The description states it is a "model-independent vision and teaching harness" that enables AI agents to observe, focus, learn desktop tasks from human demonstrations, and reuse them as tools.

The project appears to be an experimental or proof-of-concept tool for AI agent interaction with desktop environments using vision and UI automation. It claims to support multiple models (hence "model-independent") and uses local development tools including .NET, C#, Node.js, OpenAI API, and Windows OCR.

Key commercial due-diligence read: There is no evidence of revenue, customers, traction or product-market fit. The project is described as a hackathon submission with no indication of commercial viability or adoption. The single most important open question is whether this represents a viable technical approach to agent-based desktop automation that could scale beyond a prototype.

Confidence level: Very low — based entirely on self-reported evidence, with no external validation or demonstration of product functionality.

The description states:

"A model-independent vision and teaching harness that lets AI agents see, focus, learn desktop tasks from human demonstrations, and reuse them as tools."

This suggests a system designed to enable AI agents to:

• See desktop environments (via vision capabilities)
• Focus on relevant UI elements
• Learn desktop tasks through human demonstration
• Reuse learned tasks as tools

It is described as a "harness" — implying it's a framework or platform for building such capabilities rather than a finished product.

The author also states:

"Built with (author-declared): .net, c#, codex, gpt-5.6, lm-studio, local, model, node.js, openai-api, svg, typescript, ui-automation, windows, windows-ocr"

This indicates a technical stack involving Windows-based UI automation, OCR, and integration with OpenAI APIs, likely for vision and language processing.

Inference: The product appears to be an experimental framework or prototype for enabling AI agents to interact with desktop environments through human demonstration. It is not evidenced as a commercial product or service.

The description states:

"A model-independent vision and teaching harness that lets AI agents see, focus, learn desktop tasks from human demonstrations, and reuse them as tools."

This is a self-stated positioning of the project. It claims to be a general-purpose tool for teaching AI agents how to perform desktop tasks, with no dependency on a specific AI model.

There is no evidence of prior versions or claim evolution — this is a single statement from one author in a hackathon submission.

Claim vs Fact: The description states that it is a "model-independent" system. This is a claim, not a fact, and there is no evidence to confirm whether this is technically true or how it would work in practice.

The description does not state who the target customer is or what the ideal customer profile (ICP) might be.

Not evidenced: No information on:

• Who would use this system
• What industries or roles it targets
• Whether it's for developers, enterprises, or end-users

The description does not mention any business model or pricing strategy.

Not evidenced: No indication of:

• Revenue streams
• Pricing tiers
• Monetization approach
• Subscription or one-time models

The author states the following tools were used:

".net, c#, codex, gpt-5.6, lm-studio, local, model, node.js, openai-api, svg, typescript, ui-automation, windows, windows-ocr"

This suggests a technical stack that includes:

• UI automation for Windows environments
• OCR (Windows OCR)
• Integration with OpenAI APIs
• Local development tools

Inference: The system is likely built to run locally or in a controlled environment, possibly using AI models via API. It may be designed to work with desktop applications and UI elements.

There is no evidence of:

• Deployment architecture
• Scalability
• Cloud integration
• Performance metrics

The description states that this project was submitted to the OpenAI 2026 hackathon, and that it is a single-person effort by Peter Albrecht.

There is no evidence of:

• Customers or users
• Revenue or monetization
• Product adoption
• Market traction
• Iteration history or versioning

Not evidenced: No signs of product-market fit, user feedback, or commercial viability.

The description does not mention any competitors or how this project relates to existing solutions in the AI agent or desktop automation space.

Not evidenced: No information on:

• Direct competitors
• Market positioning
• Differentiation from existing tools
• Industry trends or benchmarks

• Single founder: The project is built by one person, which raises questions about scalability and long-term maintenance.
• Hackathon submission: This is a prototype, not a commercial product — no evidence of real-world use or traction.
• No demonstration or proof-of-concept: There is no video, demo, or working example provided.
• Unverified claims: The claim of being "model-independent" and enabling agents to "see, focus, learn, and reuse tasks" is not substantiated.
• Technical feasibility: Without a clear architecture or implementation details, it's unclear how this would function at scale.

1. What specific desktop environments or applications does ASHA support?
2. How does it handle model independence — what are the technical mechanisms involved?
3. Is there any working prototype or demo available for review?
4. What is the intended use case for this system, and who would benefit from it?
5. How does it integrate with existing AI agent frameworks or platforms?
6. Are there any plans to commercialize or scale this beyond a hackathon project?