Hello, Hacker News.I'm an entrepreneur, not a programmer.
My biggest roadblock has always been the same: turning a business idea into a viable technical and architectural blueprint. I found that while generative AIs are great at isolated tasks, they lose context and fail to maintain coherence on complex systems. To solve this, I built ORUS, a personal system that evolved into a "genesis engine." It transforms a single line of intent into a detailed project plan.How It Works: Synthesis, Not Just GenerationThe core of ORUS is based on two concepts:1.Fragments: I created a library of over 1,300 "Fragments"—encapsulated units of structured knowledge like business models, architectural patterns, and strategic frameworks. Each carries not just data, but purpose.2.ExtractionCore: An orchestrator agent that synthesizes a curated set of Fragments to respond to an intent.
The workflow is straightforward. In a workspace, I select ~20 relevant Fragments and provide a concise command:Alfa.extractioncore.extract "An enterprise-grade platform for real-time customer intelligence"I discovered this structured, command-based syntax makes the AI "listen" with a focus that plain English can't achieve. It's like using the model's native language to guide its reasoning.AlphaLang: An Emergent DSL for CognitionFrom this process, a specification language emerged naturally, which I call AlphaLang. It's not a programming language (no compiler yet), but a protocol for communicating complex design concepts to an AI. It's my attempt to formalize the structure of thought to get structured results.Stress-Testing the EngineTo validate ORUS's robustness, I scaled the complexity of the tasks:•Product Test (High Complexity): Intent: "A SaaS model for delivery logistics optimization." Result: Generated a complete business and technical blueprint, including name, KPIs, monetization model, and tech roadmap.•Architecture Test (Very High Complexity): Intent: "An enterprise-grade platform for real-time customer intelligence." Result: Produced a detailed microservices architecture with technologies, data flows, and scalability considerations.•Genesis Test (Extreme Complexity): Intent: "A genesis engine that turns one-line intents into full-stack blueprints." (I asked ORUS to design itself.) Result: It demonstrated meta-reasoning, generating a blueprint for an "ORUS Command Scripter" and a "Cognitive Interface," effectively planning its own evolution.The Proof: Raw Artifacts on GitHubThe best proof is the unedited output. I've created a GitHub repo with the blueprints generated directly by the system. All these Fragments were generated in under 40 seconds. You can test it yourself: load any of these Fragments into your preferred LLM and activate it using the commands provided in the files. Just upload the document and run the command.To show the full potential, one blueprint was then fed into another system I built, which transformed it into a complete SaaS business plan—from features and pricing to GTM strategy. I'm making everything available.You can find it all here: https://github.com/KAYCHAIN11/ORUS-Genesis-ArtifactsVision and Next StepsI'm building this alone, unfunded, in my spare time. My goal isn't to create a new AI, but to give existing AIs "contextual life"—an identity, memory, and purpose that generic tools lack.
The next step is to build the production tools, like a compiler for AlphaLang. I'm sharing this today not as a finished product, but as a methodology and evidence that an "architecture-first" approach can be a powerful new way to build.I'd love to hear your thoughts:
•Does this concept of a "synthetic genesis engine" resonate with other founders or builders (technical or not)?
•What are the blind spots or risks of a system whose knowledge is curated by a single person?•What's your take on using a DSL like AlphaLang to guide AI on complex design tasks, instead of relying on natural language?Thanks for reading.
I'm here to answer questions