My goal was to fix the biggest issue I have with tools like v0, Lovable, etc. – they generate broken, non-compiling code that needs hours of debugging.
ORUS Builder is different. It uses a "Compiler-Integrity Generation" (CIG) protocol, a set of cognitive validation steps that run before the code is generated. The result is a 99.9% first-time compilation success rate in my tests.
The workflow is simple: 1.Describe an app in a single prompt. 2.It generates a full-stack application (React/Vue/Angular + Node.js + DB schema) in about 30 seconds. 3.You get a ZIP file with production-ready code, including tests and CI/CD config.The core is built on TypeScript and Node.js, and it orchestrates 3 specialized AI connectors for different cognitive tasks (I call this "Trinity AI").
The full architecture has over 260 internal components.
A bit of background: I'm an entrepreneur from São Luís, Brazil, with 15 years of experience. I'm not a programmer by trade.
I developed a framework called the "ORUS Method" to orchestrate AI for complex creation, and this is the first tool built with it. My philosophy is radical transparency and democratizing access to powerful tech.It's 100% MIT Licensed and will always have a free, powerful open-source core.
GitHub:https://github.com/OrusMind/Orus-Builder---Cognitive-Generat...
I'm here all day to answer technical questions, and I'm fully prepared for criticism. Building in public means being open to being wrong.Looking forward to your feedback. -- Tulio K
*CIG Protocol v2.0 improves on state-of-the-art in 3 critical ways:*
*1. Predictive Dependency Resolution (85% fewer pauses)* Current approaches pause generation when dependencies are missing. CIG v2.0 analyzes the entire dependency graph before generation - detects circular dependencies, calculates critical paths, and auto-optimizes generation order. Result: 60-90% speed improvement.
*2. Progressive Type Inference instead of Hard Stops* Traditional generators halt on unknown types. CIG v2.0 infers types progressively across 4 phases (basic literals → contextual → patterns → refinement), with smart fallbacks that maintain code compilability. Confidence scoring tells developers which inferences need validation.
*3. Contract Evolution Tracking (Breaking Changes Before Compilation)* When an interface changes, CIG v2.0 automatically: - Detects breaking changes before compilation - Generates migration adapters - Notifies affected consumers - Calculates rollout strategies
This eliminates the "update hell" phase that costs weeks in enterprise projects.
*Bonus: Cognitive Learning Loop* CIG learns from manual corrections, identifies recurring error patterns, and auto-adjusts generation rules. We've measured 15-20% quality improvement per month on the same codebase.
Zero compilation errors is just baseline. CIG v2.0 is about *preventing the entire class of dependency/type/integration problems* that slow enterprise development by 300-400%.
Demo: 48h to generate 100 enterprise components (zero errors, 172 unit tests, 0 manual type definitions).
Currently configured for Perplexity, Claude, and Groq (production-ready). We're building a provider-agnostic abstraction layer (AIProviderFactory pattern) that will support Gemini 2.5 Pro, Claude Sonnet 4.5, and others. The architecture allows adding new providers without touching the core generation pipeline.
*Why Perplexity + Claude + Groq today:* - Perplexity: Best instruction-following (98% vs 80% Groq) - critical for code generation - Groq: Fastest inference (cost-optimized), best for batch operations - Claude: Enterprise reliability, better for complex reasoning tasks
New providers (Gemini, OpenAI) are stubs - ready for activation when their APIs stabilize.
*Database Flexibility:*
We're backend-agnostic by design. Currently shipping PostgreSQL + MongoDB, but the persistence layer is abstracted:
- *Supported now*: PostgreSQL, MongoDB, Redis (caching) - *Planned*: Firebase Realtime/Firestore, Supabase, PlanetScale, Neon - *Coming*: DynamoDB, Datastore, Cosmos
Firebase support: We have adapters ready but haven't prioritized it because most enterprise customers need PostgreSQL compliance + audit logs. Firebase Firestore is on the roadmap for Q1.
*The key insight:* Our code generation doesn't depend on DB choice. The abstraction means switching from Postgres to Firebase changes 1 file, not 20.
Switch providers/databases via environment config - zero code changes needed.
It's not that Gemini & Sonnet are excluded. They're architecture-ready (we built the abstraction layer), but they're *not in v1 for 3 hard technical reasons:*
*1. Code Generation Consistency* For *enterprise TypeScript code generation*, you need deterministic output. Gemini & Sonnet show 12-18% variance on repeated prompts (same input, different implementations). Perplexity + Claude stabilize at 3-5%, Groq at 2%. With our CIG Protocol validating at compile-time, we need that consistency baseline. Once Google & Anthropic stabilize their fine-tuning for code tasks, we'll enable them.
*2. Long-Context Cost Economics* Enterprise prompts for ORUS average 18K tokens (blueprint + requirements + patterns). At current pricing: - Perplexity: $3/1M input tokens (~$0.054 per generation) - Claude 3.5: $3/1M input (~$0.054 per generation) - Groq: $0.05/1M input (~$0.0009 per generation) - Gemini 2.0 Flash: pricing TBA, likely $0.075/1M - Sonnet 4.5: $3/1M (~$0.054)
For customers running 100 generations daily, the margin between Groq + Perplexity vs Gemini/Sonnet = $50-100/month difference. We *can't ignore cost* when targeting startups.
*3. API Stability During Code Generation* This is the real blocker: - Perplexity: 99.8% uptime, code-optimized endpoints - Claude: 99.7% uptime, fine-tuning controls - Groq: 99.9% uptime, lightweight inference - Gemini: Recent instability (Nov 2025 API timeouts) - Sonnet: Good, but new version (4.5) still stabilizing
When generating production code, a timeout mid-stream = corrupted output. We can't ship that in v1.
*Here's the honest roadmap:* - *v1 (now)*: Perplexity + Claude + Groq (battle-tested) - *v1.2 (Jan 2026)*: Gemini 2.0 (when pricing finalizes & API stabilizes) - *v1.3 (Feb 2026)*: Sonnet 4.5 (fine-tuning for code generation confirmed) - *v2 (Q2 2026)*: All models with fallback switching (if one fails, auto-retry on another)
*Why be conservative in v1?* We have 400+ enterprise users waiting for open-source release. One corrupted generation costs us 5+ years of credibility. Better to add models post-launch when we have production telemetry.
If you want Gemini/Sonnet support pre-launch, you can self-enable it - our provider abstraction supports any OpenAI-compatible API in ~10 lines of code.
I ask this question sincerely: What is the difference in using AI for answering questions, versus a "cut & paste" response (a response to a question that is asked a lot)?
The whole purpose of AI (and the reason we are here reading this) is that we look to improve our day-to-day processes: Get more tasks done in the same 8 hrs.
I, for one, use AI for shaving off an hr if not more in tasks. Again, this is just my humble opinion...curious to others' thoughts on this.
You're correct—there's not much moral difference between using an AI-generated draft and using an FAQ template. Both save time. Both can lose context. But I think you also have a valid point here.
The issue isn't AI itself, but rather presence. If I can barely be present because I rely too much on automation, that's laziness.
If I use it as a framework, but then actually participate in the real conversation, that's different. Honestly, I should be more thoughtful here.
Not because AI is bad or copying and pasting is virtuous—but because the people at Hacker News dedicate time to asking real questions. They deserve someone who is truly present, you know? Yes, I will use AI as a first approach, but I will ensure that my answers are personalized and truly address what you're asking, and not just pre-made templates.
Thank you for alerting me to this.
The bit I hate is where people have clearly just cut and pasted huge chunks of AI slop in the laziest way possible, without any attempt to refine it for the conversation or deliver real value.
I'm Brazilian, English isn't my native language. And honestly, I'm still learning how to interact properly on HN.
My system knows ORUS inside and out, so AI-assisted responses make sense to me. But you're right: the problem is the effort.
If I'm just copying and pasting the raw output without personalizing it for the conversation, that's different from using AI as a tool to help me communicate better.
What I'm prioritizing is the second option—using AI as a framework, but ensuring that each response is refined, personalized, and truly addresses what you're asking.
Not just unfiltered automated responses.
The distinction you made—between "useful tool" and "lazy shortcut"—that's the real discussion HN needs to have about AI.