DEV Community: Aurakl Crevo

Everyone's Talking About AI Writing Code — But Few Are Asking If It’s the Right Code

Aurakl Crevo — Fri, 24 Oct 2025 11:23:41 +0000

Everyone’s obsessed with how fast AI can write code.

But after the hype fades, one uncomfortable truth remains:

Speed means nothing if the AI keeps writing the wrong code.

Welcome to the "Vibe Coding" era — where developers prompt their way to entire projects, only to end up with unmaintainable chaos.
The real challenge of AI programming isn’t about generating more code.
It’s about bringing engineering discipline back into the process — and that starts with one thing: the spec.

From Vibe Coding to Specification Collapse: The "Growing Pains" of AI Programming

In 2023, ChatGPT gave us our first taste of AI's coding magic. A short description in plain English, and seconds later, runnable code appeared. The developer community went wild.

In 2024, tools like Cursor and Claude Code took this experience to the extreme. "Vibe Coding" became the new buzzword—programming by "feel." You write a prompt, and the AI generates an entire feature, or even a whole project. This "what-you-think-is-what-you-get" experience made indie devs and startups cheer: "My productivity just 10x'd!"

But the honeymoon phase didn't last.

The moment you use Vibe Coding to build your first real prototype, the problems flood in:

Context is lost: The AI forgets what you asked for last time. Every chat feels like it has amnesia.
Code is inconsistent: The component it builds today looks completely different from the one it built yesterday.
It's impossible to maintain: A week later, you can't even understand the logic the AI generated.
Teamwork is hard: Your AI assistant and your teammate's AI assistant are "speaking different languages."
Changing requirements is a disaster: One small change to a core feature, and the entire codebase needs to be refactored by hand.

This isn’t a tool failure — it’s a process failure.

Vibe Coding is just "improve programming." It's great for fast prototypes and exploring ideas, but in a real engineering project, it's like building a skyscraper with no blueprint.

You might build it fast, but it could fall down at any second.

Specification-Driven Development (SDD): The Engineering Method for AI

There's an old wisdom in software engineering: "Before you write the code, think about what you're trying to do."

In the old days, we wrote requirement docs, design docs, and API specs, then we started coding. But those docs often became a "formality"—just a checkbox. They were written, thrown in a drawer, and the code and the docs slowly drifted into different worlds.

Specification-Driven Development (SDD) flips this relationship:

The code serves the spec, not the other way around.

In an SDD workflow:

The spec is the single source of truth. All development work centers on a shared, versioned specification.
The spec is executable and drives the code. It’s precise enough to generate code and to re-generate it when requirements change.

SDD is especially important in the age of AI, because:

AI needs clear context: Vague prompts give you random results. Clear specs give you predictable results.
Specs can be versioned and reused: The team can share one set of specs to make sure the AI's output is consistent.
Specs close the gap between idea and code: It ensures zero information is lost between "what you want" and "what the AI builds."

But this leads to a huge problem: Who is going to write all these specs?

Writing a complete software design spec can take a team of PMs, architects, and engineers weeks. In a fast-moving startup, that's a cost that's just too high.

This is the exact problem Crevo was built to solve.

Built originally as an internal tool for AI dev teams, Crevo now opens this workflow to everyone.

Crevo: The AI Accelerator for Specification-Driven Development

Crevo is an AI-powered service (based on the MCP protocol) that generates software design documents. Its core mission is simple:

Make writing specs as easy as a conversation, and make Spec-Driven Development a reality for everyone.

Core Feature: Generate Enterprise-Grade Design Specs in 30 Minutes

With Crevo, you just need to:

Describe your product idea (in 200-500 words of plain English).
Crevo automatically generates 7 types of professional design documents:
- User Stories
- Product Requirements Document (PRD)
- System Architecture Design
- Business Flow (Process Design)
- API Design
- Database Design
- Development Plan

Why these 7 documents?

This isn't just about "checking boxes." It's about giving the AI a full, 360-degree understanding of the project.

Document Type	What the AI Understands	How It Guides the Code
User Stories	Who is the user? What do they need?	Generates user-focused feature logic.
PRD	Feature boundaries, priorities, quality.	Defines the scope and "done" criteria.
Architecture	Tech stack, layers, deployment.	Chooses the right frameworks and patterns.
Business Flow	State changes, error handling.	Generates state machines, handles edge cases.
API Design	Interface rules, data formats.	Directly generates RESTful/gRPC code.
Database Design	Data models, relationships, indexes.	Generates the schema and ORM code.
Dev Plan	Task breakdown, dependencies.	Guides the order of development.

Each layer of the spec gives the AI a different set of rules, making sure the final code actually matches what you wanted.

Tech Innovation: More Than Just "Sticking Prompts Together"

Crevo's technology is far more advanced than just "calling GPT to make a doc."

Context-Aware Generation: Keeping all 7 docs in perfect sync.

Crevo uses a session-based system to manage the entire process. When it generates the "Architecture Design," it automatically includes the context from the "User Stories" and "PRD" you already approved. This makes sure your tech choices match your business needs.

How it works: Full chat history, passing context between steps, and AI models that understand the rules from the previous docs.
Multi-Model Teamwork: Using the best AI for each job.

Not all AI models are good at the same things.

PRD Writing: Needs Claude 3 Opus (for stronger logic).
Architecture Design: Needs GPT-4 Turbo (for deeper tech knowledge).
API Design: Needs Gemini Pro (for better structured output).

Crevo lets you set the "best AI expert" for each step, ensuring top quality for every document.
MCP Integration: Generate specs right inside Claude Desktop.

Crevo is built on the Model Context Protocol (MCP). This means you can call Crevo directly from your Claude Desktop:

Set up the Crevo MCP Server in Claude Desktop.
Describe your product idea in the chat.
Claude automatically calls Crevo to generate the specs.
Once the docs are made, Claude can use those specs to start writing the code.

This is true "Conversational SDD"—spec generation and code generation happen in the same workflow.

Why Crevo is the "Best Starting Point" for SDD

Let's compare the options:

Method	What It Generates	The Problem
Writing by Hand	Complete specs	Takes too long (weeks), very high cost.
ChatGPT	A single document	No context, docs don't match each other.
Claude Code (direct)	Just code	Code has no spec layer, hard to maintain.
Crevo	A complete spec system	Fast, consistent, and maintainable.

Crevo's Unique Value:

Speed: 30 minutes vs. 2-4 weeks for traditional specs.
Quality: Pro-level templates + AI expertise.
Consistency: All 7 docs are born from the same context.
Iterative: Supports updating docs and picking up where you left off.
Engineering-Ready: Native MCP support for a seamless AI workflow.

Going Further: Crevo + CLAUDE.md = The Full AI Dev Setup

In the SDD community, there's a popular practice: create a.ai/CLAUDE.md file in your project's root to tell the AI assistant how to behave.

Markdown

# CLAUDE.md## Development Rules-Use TDD-Follow DDD architecture patterns-Error handling must be complete-Code must include comments## Tech Stack-Backend: Go + Kratos-Database: PostgreSQL- Cache: Redis

CLAUDE.md tells the AI how to work (coding style, architecture rules).
Crevo's docs tell the AI what to build (features, API specs, data models).

Your Full Setup:

project-root/
├── .ai/
│   ├── CLAUDE.md          ← AI's "rules and skills"
│   ├── PRD.md             ← From Crevo (What to build)
│   ├── ARCHITECTURE.md    ← From Crevo (How to structure)
│   ├── API_DESIGN.md      ← From Crevo (The interfaces)
│   └── DB_DESIGN.md       ← From Crevo (The data model)
├── src/
└── tests/

Your New Workflow:

Use Crevo to generate the complete spec documents.
Set upCLAUDE.md to define your development rules.
In Claude/Cursor, give a simple prompt:

"Please read all the spec documents in the .ai/ directory and generate the initial project code."
The AI builds high-quality code based on both the specs and the rules.
When requirements change, just update the Crevo specs, and the AI refactors the code.

From Vibe Coding to Spec-Driven: This is the Path We Must Take

In 2023, we were amazed that AI could write code.

In 2024, we learned to prototype fast with Vibe Coding.

In 2025, we need Spec-Driven Development to bring AI programming into the age of engineering.

Crevo isn't here to replace your AI coding tools. It's here to give them a*"brain"*—the clear, complete, and executable specs they need.

When Cursor asks what feature you want, you have a PRD from Crevo.
When Claude needs to understand the architecture, you have an architecture doc from Crevo.
When you need to refactor your code, you just update the spec, and the AI re-generates the code.

This is the true future of AI programming: Humans focus on ideas and decisions. AI handles turning those specs into code.

Specs First, Code Later.

Let's enter the age of AI engineering together!

Resources

🔗 Official Site: https://crevo.aurakl.ai

📖 Docs: https://crevo.aurakl.ai/docs

💬 Community: Join our Discord https://discord.gg/ECXJ5z44

Sign up now and get 100 free credits to try it out!

Claude Code + Crevo: Achieving Efficient & High-Quality SDD (Spec-Driven Vibe-Coding)

Aurakl Crevo — Tue, 21 Oct 2025 13:28:43 +0000

I. How to Configure Claude Code with Our Crevo MCP Tool

Objective: To enable Claude Code to access Crevo via MCP (for reading/writing the 7 key documents exported by Crevo, triggering Crevo's generation/update interfaces, etc.).

Working Configuration

Recommended (CLI) Method

# Add Crevo's MCP service to Claude Code
claude mcp add --transport http aurakl-crevo https://crevo-mcp.aurakl.ai/mcp?key=your-api-key

aurakl-crevo is the name for the MCP server being added (which can be referenced in Claude Code).
Replace your-api-key with the API Key obtained from your Crevo console.

Or, Create **.mcp.json**** in the Project Root**
Place .mcp.json in the project root (for project-level scope) or in the global config (for global scope):

{
  "mcpServers": {
    "aurakl-crevo": {
      "serverUrl": "https://crevo-mcp.aurakl.ai/mcp?key=your-api-key"
    }
  }
}

Verification

claude mcp list

II. How to Configure Crevo's Two Slash Commands: /crevo and /update

Create the /crevo command

mkdir -p ~/.claude/commands
echo "Use Crevo to create the complete seven key documents (User Stories, PRD, System Architecture Design, Business Process Design, Database Design, API Design, Development Plan)." > ~/.claude/commands/crevo.md

After executing, simply type in the Claude Code chat box:

/crevo User Management System

Claude Code will automatically call the Crevo MCP service to generate the complete seven documents.

Create the /update command

echo "Automatically update the Crevo document system (including PRD, Architecture, API, Test Cases, etc.) based on the latest development results." > ~/.claude/commands/update.md

Usage:

/update User Management System v1.2

Crevo will automatically sync the design documents based on Claude Code's development execution, achieving an intelligent, closed-loop update of the specifications.

III. How to Use Crevo to Create Documents

Crevo is responsible for turning product goals into "structured specifications". Below are copy-pasteable example snippets (real, exportable Markdown / OpenAPI / SQL) that can be generated by Crevo for use in Claude Code.

Document Overview (using a "User Management System" as an example)

.spec/
├── user_story.md
├── prd.md
├── architecture.md
├── process_design.md
├── database.md
├── api_spec.md
└── dev_plan.md

User Stories
PRD (Product Requirements Document)
System Architecture Design
Business Process Design (Process / Flow)
Database (DB) Model (Schema / Migration)
API (OpenAPI Spec)
Development Plan (dev_plan)

Example 3.1 — User Stories **docs/user_story.md**

# User Stories

## US-001: User Registration
As a: Unregistered user
I want: To register an account by submitting my email and password
So that: I can log in and use the platform's features

Acceptance Criteria:
- Frontend provides an email/password registration form
- Email format validation, password must be at least 8 characters
- Send an activation email

Example 3.2 — PRD **docs/prd.md**

# PRD: User Management Service

Objective:
- Provide account registration, login, user profile management, and role-based access control (RBAC).

Scope:
- Email/Password registration, OAuth login
- User Profile CRUD
- Role & permission management
- Audit logging and security monitoring

Non-Goals:
- SSO integration (can be considered in a future iteration)

Example 3.3 — System Architecture **docs/architecture.md**** (Excerpt)**

Components:
- API Gateway
- Auth Service (JWT, OAuth)
- User Service (profile, preferences)
- Role Service (RBAC)
- DB: PostgreSQL + Redis (cache)
- CI/CD: GitHub Actions / Jenkins

Example 3.4 — Business Process **docs/process_design.md**** (Mermaid)**

sequenceDiagram
  User->>Frontend: submit(email, password)
  Frontend->>API: POST /api/v1/register
  API->>AuthService: validate, hash password
  AuthService->>DB: insert user
  AuthService->>EmailService: send activation email
  User->>EmailService: click activation link
  EmailService->>DB: set is_active = true

Example 3.5 — DB **docs/database.sql**

CREATE TABLE users (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  email VARCHAR(255) NOT NULL UNIQUE,
  password_hash TEXT NOT NULL,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT now(),
  is_active BOOLEAN DEFAULT false
);

CREATE TABLE roles (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  name VARCHAR(100) NOT NULL UNIQUE
);

CREATE TABLE user_roles (
  user_id UUID REFERENCES users(id),
  role_id UUID REFERENCES roles(id),
  PRIMARY KEY (user_id, role_id)
);

Example 3.6 — API **docs/api_spec.yaml**** (OpenAPI Snippet)**

openapi: 3.0.3
info:
  title: User Management API
  version: "1.0.0"
paths:
  /api/v1/register:
    post:
      summary: Register user
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/RegisterRequest'
      responses:
        '200':
          description: registration accepted
components:
  schemas:
    RegisterRequest:
      type: object
      required:
        - email
        - password
      properties:
        email: { type: string, format: email }
        password: { type: string, minLength: 8 }

Example 3.7 — Dev Plan **docs/dev_plan.md**

# Dev Plan: user-management

iterations:
- id: iter-01
  goal: basic registration & login
  tasks:
    - t-101: implement users table and migrations
    - t-102: implement POST /api/v1/register
    - t-103: create unit tests for register flow

- id: iter-02
  goal: RBAC & profile management
  tasks:
    - t-201: implement roles table and user_roles mapping
    - t-202: implement role assignment API
    - t-203: implement user profile CRUD

IV. Creating Claude Code SubAgents Based on Development Needs

The Development Plan (Dev Plan) generated by Crevo will be parsed by Claude Code into a series of development tasks.
Each task corresponds to a SubAgent, which is defined as a Markdown file in the .claude/agents/ directory.

SubAgents are not automatically generated from the documents, but are created based on the task type (development, testing, code review, audit, scheduling, etc.).
This approach allows Claude Code to execute tasks by assigning roles.

SubAgent Example Collection

dev-agent.md

# Development Agent
Responsible for completing specific code development based on the design documents generated by Crevo.

## Input
- prd.md
- api_spec.md
- db_design.md

## Behavior
1. Read the tasks for the current iteration
2. Generate or update the corresponding code files
3. Output the code files and commit log

test-agent.md

# Test Agent
Automatically generate and execute test cases based on the API design and user stories.

## Input
- api_spec.md
- user_story.md

## Behavior
1. Generate Jest test files
2. Run tests and record results
3. Output test report to `reports/`

review-agent.md

# Code Review Agent
Review the latest code commits for security, style consistency, and potential logic issues.

## Input
- Latest commit log
- Security guidelines document

## Behavior
1. Perform static analysis and security review
2. Output a review report with remediation suggestions

audit-agent.md

# Audit Agent
Monitor system changes, dependency updates, and sensitive configurations.

## Behavior
- Check dependency changelogs
- Output compliance report

scheduler-agent.md

# Scheduler Agent
Responsible for scheduling the execution order and dependencies of SubAgents.

## Behavior
1. Read `dev_plan.md`
2. Execute independent tasks concurrently
3. Aggregate all results and report back to Crevo

This way, Claude Code's MCP layer can dynamically assign tasks to different SubAgents based on Crevo's development plan and context, achieving modular execution.

V. Executing Iterative Development and Generating Code

Once the seven documents and SubAgents are ready, simply execute:

#claude
Please help me complete the first iteration as planned in dev_plan.md

Claude Code will:

Read the Iteration 1 tasks from dev_plan.md;
Plan development tasks by referencing the iteration plan and the relevant content within the 7 key documents;
Call the scheduler-agent to schedule;
Assign tasks to dev-agent, test-agent, etc.;
Automatically generate code and run tests;

VI. Crevo's Specification-Driven Vibe Coding

Crevo's core philosophy is that all development tasks must originate from a 'Specification' (Spec).
The Claude Code and MCP architecture turn these specs into the contextual source of truth, and all of the AI's actions take place within this 'semantic field (Vibe)'.

Feature Comparison:

Traditional AI Coding	SDD Vibe Coding
One-shot generation, lacks contextual consistency	Continuous collaboration based on complete specs and context
Manual debugging and manual fixes	Automatic iteration, testing, and document synchronization
Code and documentation are separate	Documentation as the semantic source for code
No feedback loop	Complete design-implement-validate-update cycle

VII. Advantages of SDD Vibe Coding

Efficient Alignment: Product, architecture, development, and testing are all aligned on the same structured document, reducing meetings and repetitive clarifications.
Reproducible and Auditable: Every action by a SubAgent (generation, commit, test) has logs, PRs, and CI records.
Automated Closed Loop: Forms a continuous optimization cycle, from requirements to deployment and back to monitoring data flowing into the docs.
Reduced Human Error: The mapping from spec to implementation is deterministic: OpenAPI → code skeleton; User Stories → test cases.
Suitable for Scaled Teams: Ideal for large projects with multi-team collaboration that require clear interfaces and contracts.
Supports Chinese/English Users: Crevo's multi-lingual output + Claude Code's universal agent design allow for barrier-free collaboration for international teams.

Generate a Complete Tech Spec in 5 Minutes: The Small Team's Playbook for a Fast Project Kickoff

Aurakl Crevo — Mon, 20 Oct 2025 06:11:11 +0000

While Everyone Else Is Stuck Agonizing Over PRDs, Smart Teams Are Already Shipping Code

I. The Small Team's Documentation Dilemma: Where Did All the Time Go?

As a product manager or tech lead on a startup team, does this scene feel painfully familiar?

Monday Morning Meeting:

Boss: "We need to move fast on this new project. I want to see a demo by next week!"
You: "Got it. I'll get the requirements and technical specs sorted out this week..."
Boss: "Can't the docs go faster? We're on a tight deadline!"

Wednesday Afternoon:

You finally finish the PRD and send it to the engineering team for review.
Developer: "This requirement isn't clear. There's no system architecture design. How are we supposed to build this?"
You: "Right. I'll add the architecture document..."

Friday Afternoon :

You've worked late into the night and finally hammered out the architecture doc.
Backend Dev: "Where's the API specification? And the database design?"
You: "..." (You, internally collapsing)

The Following Monday:

Boss: "So, where's that demo? You said we'd see it this week!"
Team: "We're still waiting on the docs. We can't start."

This story plays out every single day. Based on our research, small teams spend an average of*40% of their project kickoff time* just on documentation.

Worse, this process is plagued by:

❌Inconsistent Quality: PMs don't know the deep technical details, so the tech docs are unprofessional.
❌Document Mismatches: The PRD, architecture doc, and API docs are separate and frequently contradict each other.
❌Wasted Time on Revisions: A small change in requirements means every single document has to be manually updated.
❌Low Team Efficiency: The endless back-and-forth on docs creates massive communication overhead.

The result? Your team is completely burned out before the project even begins.

II. The Secret to Doubling Your Efficiency: Let AI Handle the Documentation

What if I told you that you could generate a complete, professional set of software design documents in just 5 minutes?

This isn't a fantasy. It's what we're doing right now.

Comparison Metric	Traditional Manual Way	The AI-Driven Way
Time Cost	3 - 7 days	5 - 10 minutes
Document Scope	Usually just a PRD & simple diagram	A complete 7-document set
Document Quality	Depends on individual experience	Professional templates + AI
Consistency	Prone to contradictions	Context-aware, logically consistent
Revision Cost	Must update each doc one by one	One-click update, auto-sync

III. Crevo: Your AI Technical Documentation Specialist

Crevo is an AI-powered tool that automatically generates software design documentation. Based on your product description, it can create a complete technical solution, from requirements to architecture.

Core Capabilities

One-Stop Document Generation

Just describe your product in one sentence, and Crevo can automatically generate 7 types of professional documents:

📋User Stories: User roles, user needs, feature lists.
📄Product Requirements (PRD): Product overview, functional & non-functional requirements.
🏗️System Architecture Design: Tech stack, system layers, deployment architecture.
🔄Business Process Design: Flowcharts, state machines, error handling.
🔌API Design: RESTful API specifications, endpoint definitions, data formats.
💾Database Design: Data models, table structures, index design.
📅Development Plan: Task breakdowns, milestones, resource allocation.
Intelligent Contextual Understanding

Crevo doesn't just spit out isolated files. It understands the relationships between them:

✅Consistent Context: Features from the PRD are reflected in the architecture design.
✅Inherited Decisions: The database design will reference the API design.
✅Smart Inference: It recommends the right tech stack based on your business needs.

3. Flexible Customization

🎯Targeted Generation: Only need an API doc? No problem.
✏️One-Click Updates: Need to change a requirement? One command updates all related docs.
🔄Version History: Full version control support to roll back anytime.

4.Multi-Model AI Support

Crevo integrates leading AI models to ensure the highest quality:

OpenAI GPT-4: Strong all-around, ideal for complex system design.
Anthropic Claude: Logically rigorous, perfect for technical architecture.
Google Gemini: Broad knowledge base, great for cutting-edge tech selection.
Ollama (Local Models): Privacy-first, ideal for sensitive projects.

IV. Live Demo: A Complete Project Spec in 5 Minutes

Let's walk through a real-world case to see Crevo in action.

Scenario: Developing an "Intelligent Health Management App"

Your Input (30 seconds):

"I want to develop an intelligent health management App.

Main features include:

Users record daily exercise, diet, and sleep data
AI analyzes health status and provides personalized advice
Supports setting health goals and reminders
Community sharing and friend interaction features"

Crevo's Output (2 minutes later):

Step 1: User Stories Generated

Step 2: PRD Generated

Based on the stories, Crevo generates a detailed PRD:

Step 3: System Architecture Design Generated

Crevo automatically recommends a technical solution:

Steps 4-7: Continuous Generation...

Business Process Design: User registration flow, data sync flow, AI analysis flow...
API Design: RESTful endpoint definitions, request/response examples...
Database Design: User tables, health data tables, relationships...
Development Plan: 4 development phases, 12-week timeline, resource allocation...

Total time: ~5 minutes ⏱️

During this entire process, all you did was:

30 seconds: Describe your product.
2-3 minutes: Wait for the AI to generate the first document.
1-2 minutes: Review, confirm, and tell it to generate the next one.
Repeat steps 2-3 to automatically generate the rest.

V. Real-World Cases: A Startup's Efficiency Revolution

Case Study 1: E-commerce Startup

Team: 5 people (1 PM, 3 Devs, 1 Designer)
Project: Social e-commerce platform
Pressure: Investors demanded an MVP launch within 1 month.

Before Crevo:

PM spent 1 week writing the PRD.
Tech lead spent 3 days supplementing the tech spec.
2 more days were wasted on back-and-forth revisions.
Total: 12 days of doc prep.

After Crevo:

PM generated the entire document set in 10 minutes.
The tech team spent 2 hours fine-tuning the docs.
They began setting up the dev environment*that same afternoon*.
Total: Half a day to finalize all docs.

The Result:

"We started development 10 days ahead of schedule and ultimately delivered the MVP 5 days early. Our investors were so impressed with our execution that they immediately fast-tracked our Series A funding."

— CEO of a social e-commerce company

Case Study 2: Tech Outsourcing Agency

Team: 10-person development agency
Business: Provides custom software development for clients
Pain Point: Writing new docs from scratch for every project was killing efficiency.

The Change After Crevo:

Project kickoff speed increased by 80% (from 2 weeks down to 2 days).
Documentation standardized: All projects now use a unified, professional template.
Client satisfaction soared: Professional tech specs built immediate trust.
Team capacity tripled: They could take on more projects in the same amount of time.

VI. Frequently Asked Questions (FAQ)

Q1: What's the quality of Crevo's generated documents?

A: Crevo uses top-tier AI models like GPT-4 and Claude 3, trained on a massive number of real-world projects. The generated documents are structurally complete, logically sound, and can be used directly for development. Of course, you can also fine-tune them as needed.

Q2: My project is very complex. Can Crevo handle it?

A: Yes. Crevo supports contextual understanding and multi-turn conversation. If the initial document isn't detailed enough, you can use the /update command to continuously refine it until you're satisfied.

Q3: Can I export the documents?

A: All documents are generated in Markdown. You can copy them directly into any tool (Notion, Confluence, etc.) or export them as PDF and Word files.

Q4: Is my data secure?

A: Crevo uses enterprise-grade security measures:

Data is encrypted in transit (TLS).
Private deployment (Enterprise Plan) is supported.
Your project data will never be used for other purposes.
Supports Ollama local models (works completely offline).

Q5: What if the generated document isn't what I expected?

A: You can:

Use the/update command to modify the doc.
Specify a different document type (e.g., generate only the API doc).
Provide a more detailed product description.
Contact our support team.

IX. Start Your Efficiency Revolution Now

The law of survival for small teams: iterate fast, execute efficiently.

In today's hyper-competitive market, speed is life. The teams that can validate ideas and launch projects the fastest are the ones who win.

Crevo isn't just a documentation tool; it's your*AI Technical Advisor*:

✅Liberate Productivity: Let your team focus on core development, not tedious docs.
✅Lower the Barrier: Even non-technical PMs can output professional tech specs.
✅Elevate Quality: AI ensures your documentation is professional and consistent.
✅Accelerate Iteration: Quickly generate multiple versions of a plan to compare and choose the best.

Try It Now

Visit:https://crevo.aurakl.ai/docs
Use promo code (valid 30 days) 3EUSTLMI for*50% off*.
Register an account, get your MCP Key.
Configure Claude Desktop in 5 minutes and get started.

X. A Final Thought

As entrepreneurs and developers ourselves, we know how precious time is.

Every single day of delay can mean:

Missing the market window.
Being beaten by a competitor.
Losing investor confidence.
Draining team morale.

But at the same time, we also know the importance of documentation:

Clear requirements are the foundation of a successful project.
Professional tech specs are the prerequisite for team collaboration.
Complete design documents are the key to quality assurance.

Crevo's mission is to ensure you never have to make the difficult choice between speed and quality.

We believe AI should be an efficiency amplifier for every team, not a privilege for a select few. With Crevo, we hope to empower more small teams to stand on the shoulders of giants—to turn their ideas into reality in the fastest, most professional way possible.

By Crevo, your next project starts in 5 minutes.

From Vibe Coding to Specification-Driven Development: Why AI Programming Must Evolve from "Magic" to Engineering

Aurakl Crevo — Sat, 18 Oct 2025 16:09:13 +0000

The slogan "AI, write code for me" has sparked an experiential revolution in the developer community over the past two years.

Suddenly, generating a runnable module with natural language wasn't a fantasy. You could write a single prompt and get a UI, an API, or even test cases. This gave rise to "vibe coding": turning development into a real-time dialogue with a model, radically compressing the distance between an idea and its implementation.

But when we shift our focus from "solo prototypes" back to "engineering projects," teamwork, and long-term maintainability, the fundamental flaws of vibe coding are laid bare.

At the same time, a more systematic, engineering-focused paradigm—Specification-Driven Development (SDD)—is emerging. With its superior control, verifiability, and scalability, SDD is becoming the essential path to bringing AI programming into production-grade applications. AWS's Kiro is a prime example of this practice, explicitly positioning "specifications" as the bridge from prototype to production.

Here, I'll break down—through problems, comparisons, evidence, and practical advice—why engineering-grade AI programming requires SDD, and I'll articulate Crevo's perspective on this practice.

I. The Charm and Core Flaws of "Vibe Coding"

The Charm (Why it's so popular)

Extremely Low Barrier: Natural language produces runnable results. This provides a rapid feedback loop, perfect for quick proofs-of-concept (POC).
High-Speed Prototyping: In the early stages, "vibing" can build a demo-ready prototype incredibly fast, saving significant manpower.

The Core Flaws (Why it's not for engineering)

Semantic Drift and Uncertainty: Models make heavy assumptions when interpreting vague needs. The output often misaligns with the team's intent, and it's difficult to ensure boundary conditions and error handling are covered.
Poor Interpretability/Auditability: A "vibe" output is often a "black box" code snippet. It lacks source attribution, design decisions, or acceptance criteria, making it nearly impossible for teams to review or audit.
Knowledge Ossification Risk: When code and design aren't synced into a machine-readable spec, that knowledge remains in human brains or gets scattered across PR comments, easily lost with team turnover.
Technical Debt and Maintenance Costs: It’s fast in the short term but creates massive, hidden technical debt in the long term, especially in large systems relying on auto-generated code.
Collaboration Conflict: Code generated by different team members, each "vibing" with a model, leads to fragmentation in style, interfaces, and test coverage if there's no unifying standard.

These aren't minor implementation details; they are a core threat to engineering sustainability. Real-world production systems cannot be maintained by "getting lucky with a prompt" again and again.

II. What is Spec-Driven Development (SDD)? The Core Value

The basic logic of SDD is simple but profound: Make the "specification" the central single source of truth for both collaboration and automation.

In an SDD workflow, natural language or product requirements don't drive code directly. Instead, they are*first*translated into structured, verifiable, and executable specifications (including requirements, acceptance criteria, API contracts, test scenarios, design points, etc.).Then, automated agents and/or humans produce code, tests, and deployments based on that spec.

Key Benefits of SDD:

Consistency and Verifiability: The implementation can be automatically checked against the spec. Tests are based directly on the spec's acceptance criteria, dramatically reducing errors.
Auditability and Traceability: Every implementation point has a spec reference. Changes are recorded, making it easy to trace decisions and ensure compliance.
Reusability and Modularity: Well-structured specs can be reused across multiple projects, improving engineering efficiency.
Team-Friendly Collaboration: The spec acts as a communication contract. Design, Product, Engineering, and QA can all share the same source of truth, reducing communication overhead.

These aren't abstract ideals; they are direct, practical requirements for maintainability, scalability, and compliance. As initial reviews of SDD on dev.to also note: SDD introduces an upfront specification cost, but it's the key factor that elevates AI-assisted development from a "prototyping tool" to a "production tool" in the long run.

III. Why Amazon is Betting on SDD as the Solution

AWS (and tools in its ecosystem, like Kiro) is using SDD as the practical answer to the chaos of vibe-driven development. Kiro's approach is worth watching because it turns this philosophy into engineered product features:

From Prompt to Spec:It translates natural language prompts into structured documents likerequirements.md,design.md, andtasks to serve as a baseline for generation.
Agents and Hooks: It uses agents to automatically execute tasks, run tests, and update the spec, keeping the implementation and the specification in sync.
Review and Guardrails: It allows users to approve critical changes before automation proceeds, preventing models from blindly altering production code.
Integrates with Existing Codebases: Kiro can read project context and infer constraints from existing code, improving the quality of its generated output.

Multiple media outlets and hands-on reviews have pointed out that Kiro is gaining traction because it solves the disconnect between vibe coding and engineering—it connects "rapid generation" with "engineering quality." Hands-on reports also state clearly: without specs, AI generation is difficult to scale to production; Kiro solves this through spec-driven execution.

In short: Kiro isn't against generation; it's here to give generation discipline.

IV. Addressing Common Objections: Is the "Upfront Cost" of SDD Unacceptable?

Objection 1: "SDD requires writing specs first, which will slow down development."

Response: This is a "short-term investment for long-term returns" choice. Proper SDD isn't about writing massive, dead documents. It's about using tools toautomate spec generation, turning that upfront labor into a one-time modeling cost that is then maintained by machines. Many organizations would rather invest a bit more upfront than pay the exorbitant maintenance costs in production. Furthermore, toolchains from GitHub and AWS are already moving to automate spec writing, lowering this barrier to entry.

Objection 2: "What if the model misunderstands the spec?"

Response: This is precisely the point of SDD. It's not about blindly trusting the model's understanding. It's about having the model output its understanding as verifiable units (clear acceptance criteria, examples, boundary tests), and then using automated validation to constrain the final result. This creates a closed loop: Intent → Spec → Generation → Validation → Correction. This is far more controllable than relying on prompt-trial-and-error.

V. Practical Advice: How to Introduce SDD into Your Workflow

Here are actionable steps for teams aiming to combine the "speed of vibe coding" with the "engineering quality of SDD."

Keep the "Vibe" Entry Point, but "Immediately Structure" the Output:

Allow developers to use free-form natural language to generate prototypes. But the tool (or platform) must automatically convert these conversations into structured spec drafts (with acceptance criteria and examples). This way, the prototype doesn't remain a one-off—it immediately enters a governed, spec-based loop. Kiro's model is a perfect example.
Enforce "Verifiable Spec Units":

Every feature must be accompanied by at least one executable acceptance test or clear boundary cases. The test is both part of the spec and an automatic verifier for the model's output.
Adopt Lightweight Spec Templates and Iterative Processes:

Don't make specs heavy, burdensome documents. Use modular templates (requirements, API contracts, example request/response, key use cases) and bring them into version control and your code review process.
Establish a Two-Way Sync: Implementation → Spec:

A change in the implementation should trigger a process to write back to the spec or at least flag a discrepancy (e.g., via CI checks or agent hooks). This ensures the spec is a "living document," not a "write-and-forget" one. Kiro's hook model is a practical application of this.
Empower the Team with "Review Mechanisms," Not "Blind Trust":

Retain human approval nodes in the production path (especially for critical modules, permissions, data access, etc.) to ensure safety and compliance. Let AI do the implementation, but let the team make the final decisions.

VI. Crevo's Design Stance

Crevo's goal is to combine the "convenience of natural language"with the"control of engineering specifications." Our design philosophy has three pillars:

Language as the Entry Point, Specs as the Core: Users can start their design and development process with natural language. But Crevo translates these conversations into structured, verifiable system descriptions (PRD-style smart drafts + API contracts + acceptance criteria), turning "casual prompts" into reviewable engineering facts.
Specs as Executable First-Class Citizens: Specs aren't just documents; they are executable objects that trigger automated generation, testing, and deployment. Every change must have a verifiable unit for spec-driven generation to become a sustainable engineering practice.
A Path for Progressive Adoption:We don't demand teams abandon their existing workflows overnight. Crevo supports a gradual process of "vibe → auto-spec → iterate," allowing teams to adopt SDD-style practices at their own pace, building trust as they go.

To put it more bluntly: Crevo is designed to package the "speed of vibe" with the "reliability of SDD."

VII. Conclusion: Why We Must Pay Attention to SDD Now

Vibe coding was a revolutionary experience, but it is not the engineering end-state. If we want to bring AI into large-scale production systems, we must embed specifications, validation, and synchronization into the generation process.

SDD provides AI programming with engineering semantics, a validation loop, and a team contract. It's the essential step to move from "tools" to "platforms" and "processes." Products like Kiro are solving the engineering problems created by "vibe" by making specs the core of their system.

My advice to developers and teams: enjoy the speed of vibe, but don't abandon the spec. Turn the specification into a "living asset" automatically maintained by tools, not a heavy paper document.

Only then can AI truly evolve from an "assistant" to an "engineering partner."

Try Crevo

Farewell to Vibe Chaos. Embrace SDD. Experience the right way to do AI programming.

Register Crevo Now and use the promo code (valid for 30 days) 3EUSTLMI to start your first "spec-driven" project and get 50% off.

Quick Start: https://crevo.aurakl.ai/docs
Official Website: https://crevo.aurakl.ai/

Original Link

https://medium.com/@trankhais63861/why-the-future-of-ai-coding-belongs-to-spec-driven-development-not-vibe-coding-9a7f5ee1200e

References / Further Reading

Daniel Sogl, "Spec Driven Development (SDD) — A initial review." on dev.to.
Kiro project / docs / blog — “The AI IDE for prototype to production”.
Hands-on articles and reviews: Reports from Dev class, The New Stack, etc., discussing the value and challenges of SDD in engineering.
Articles from GitHub / Microsoft / AWS on Spec-Driven toolchains and open-source kits, illustrating the industry's move to support SDD.

Why AI Coding Still Fails — and How Context Graphs Could Fix It

Aurakl Crevo — Wed, 15 Oct 2025 10:44:39 +0000

Large Language Models (LLMs) are amazing at writing code — until you start talking to them like a human.
You describe a feature, they code something. Then you say “actually, make it multi-user,” and the logic collapses. Why does this happen?

A new research paper just revealed the hidden reason — and it perfectly explains why Crevo was built the way it is. To let more people experience Crevo, share a 50% discount code 3EUSTLMI, valid for 30 days.

I. Why Does AI Programming Often Go Off the Rails?

In the world of AI-assisted programming, users rarely articulate all their requirements perfectly in one go. Instead, they clarify their needs through an iterative, multi-turn conversation.

However, recent research shows that the success rate of Large Language Models (LLMs) drops significantly in multi-turn, underspecified conversations.
This presents a serious challenge for AI programming tools — especially those built on a conversational development model.

II. The Research: Full Context Upfront Boosts Accuracy by 35%+

In a May 2025 paper titled “LLMs Get Lost in Multi-Turn Conversation” (arXiv:2505.06120), researchers tested over 200,000 simulated dialogues across 15 mainstream models, comparing single-turn vs. multi-turn performance on six generation tasks (including code generation and database queries).

Prompting Method	Description	Average Success Rate
One-Shot Prompting	Providing all requirements at once.	78%
Multi-Turn Refinement	Clarifying requirements step-by-step.	54%

They found that in multi-turn setups, LLM performance drops by 39% on average — mainly because of two issues:

Aptitude Decline: Even at its best, multi-turn performance lags ~16% behind one-shot setups.
Reliability Collapse: Error rates skyrocket due to inconsistent internal reasoning (up by ~112%).

“When an LLM receives the full context in the first prompt — including features, constraints, and interfaces — the correctness and structural integrity of the generated code improve dramatically.”

In short: LLMs don’t handle partial context well. They perform best when given the whole picture upfront.

III. What This Means for AI Programming

Developers often rely on “progressive clarification”:

Tell AI the goal.
Let it code something.
Then say “add this” or “change that.”

But if the model misunderstood something early on, subsequent corrections can’t fix the foundation.
Yet cramming all requirements into one giant prompt is impractical — too long, too costly, and too complex.

AI programming tools therefore need a balance between iterative convenience and contextual completeness.

This is where Crevo’s design comes in.

IV. Crevo’s Solution: Full Context, On-Demand Retrieval, and Iteration-Driven Prompts

1. The Core Idea: Layered Docs + Context Retrieval

Crevo organizes the entire development lifecycle into structured, retrievable layers:

Dimension	Content
User Stories	Goals and scenarios
PRD	Features, logic
System Architecture	Modules and dependencies
Database Design	Tables and constraints
API Specs	Input/output contracts
UX/UI	Mockups and flows
Dev Plan	Iteration priorities

When you begin a task like “implement user login”, Crevo automatically retrieves all related fragments — stories, DB schemas, APIs, UI, etc.
It then composes them into a complete, unified context for the model.

Even though you work iteratively, the model operates in a quasi-single-turn environment — always with full context.

2. Why This Works

Crevo’s approach aligns perfectly with the paper’s recommended “CONCAT strategy” — combining fragmented turns into one rich prompt.

This method ensures:

Precision + Completeness: Retrieves only relevant details, but all of them.
Stable Context: The model never “forgets” or misinterprets earlier data.
Error Recovery: New prompts reconstruct full context to avoid cascading errors.
Developer Efficiency: No more repeating background info.

3. In Practice

You say: “Build a user login endpoint.”
Crevo retrieves the login flow, DB schema, API spec, and UI mockup.
It builds a full prompt → generates backend code, tests, and docs.
You add: “Include captcha and ‘remember me.’”
Crevo reassembles the updated context and regenerates the complete version.

V. From Prompts to a Context Graph

Crevo’s philosophy isn’t “write better prompts” — it’s build better context.

By structuring all project information as a context graph, Crevo enables true project-level memory and global reasoning for AI.
The future of AI programming isn’t about crafting smarter prompts — it’s about engineering smarter context.

🧠 The next evolution of programming isn’t code-first, it’s context-first.

Original Link:

https://aurakl-lab.hashnode.dev/why-ai-coding-still-fails-and-how-context-graphs-could-fix-it

Further Reading:

LLMs Get Lost in Multi-Turn Conversation (arXiv:2505.06120)
Crevo Official Website
Tip: Use the invitation code 3EUSTLMI for 50% discount.

3 Pitfalls Most Developers Fall Into When Using AI for Code, and How I Fix Them with Crevo

Aurakl Crevo — Tue, 14 Oct 2025 16:07:45 +0000

I’ve been talking with a few developer friends about AI-assisted programming lately, and I've noticed an interesting pattern: almost everyone is using AI to write code, but 80% of them are falling into the exact same traps.

These pitfalls don’t just fail to boost productivity; they actually make projects messier. Today, I want to talk about these three common traps and how I've started using a tool called Crevo to solve them.

Pitfall #1: Jumping Straight to Code Gen, Ending Up in a Tangled Mess

A typical scenario:

You have an idea and you tell ChatGPT: "Write the shopping cart feature for my e-commerce site."

The AI quickly generates a block of code that seems to work. You copy-paste it, find a few bugs, and ask the AI to fix them. After that, you realize it's not compatible with your existing codebase, so you ask for another revision. This cycle repeats, and half a day later, your feature is just a pile of patches.

The root of the problem:

The AI has no idea about your system architecture, tech stack, or business logic. It's just guessing based on your one-sentence prompt.

It's like hiring a construction crew without giving them a blueprint and just saying, "Build me a modern-style house." You can imagine how that’s going to turn out.

Pitfall #2: Ignoring System Design, Focusing Only on Snippets

A typical scenario:

Your product manager says, "Let's add Stripe payments."

You immediately ask an AI to generate the payment integration code, plug in the Stripe SDK, and it looks like you're done. But after going live, you discover:

Webhook callbacks weren't handled properly, leading to lost orders.
The database schema didn't account for refunds, so you're missing fields.
There’s no idempotency check, causing customers to be double-charged.

The root of the problem:

An AI can generate code snippets, but it won't handle system-level architecture design or risk assessment for you. A complete payment feature involves business processes, data models, API design, error handling, and more. If you just stack AI-generated code without a system design document, you're heading for disaster.

Pitfall #3: Relying on Tribal Knowledge, Leaving Documentation Gaps

A typical scenario:

You're halfway through a project when a new developer joins the team. You spend thirty minutes explaining the business logic, but they're still completely lost.

There's no PRD, no architecture diagram, and no API documentation. All the critical information is in your head. The new hire has to guess by reading the code—and when they guess wrong, they code wrong, leading to rework.

The root of the problem:

AI can generate code, but it won't automatically create the comprehensive design docs and collaboration infrastructure your team needs. Software engineering is a team sport, not an act of individual heroism. Without documentation as a "common language," team efficiency plummets.

The Solution: Design First, Then Let the AI Generate Code on Rails

After hitting these walls, I started to reflect. The problem with AI-generated code is fundamentally a lack of a "design-first" approach.

So I started experimenting with a new workflow:

First, use AI to generate a complete set of design documents (user stories, PRD, architecture, APIs, etc.).
Then, use those documents to guide the AI in generating the code.

That's when I discovered Crevo, a tool that fit my new workflow perfectly.

What is Crevo? Generating 7 Types of Pro-Level Docs from a Conversation

Crevo is an AI documentation service built on the Model Context Protocol (MCP), specifically designed to solve the "missing documentation" problem.

Core Capabilities:

🤖 Conversational Generation: Just describe your product, and the AI automatically generates 7 types of professional documents.
📋 Full-Cycle Coverage: User Stories → PRD → Architecture Design → Business Process → API Design → Database Design → Development Plan.
🔗 Context-Aware Consistency: The output from one step becomes the input for the next, ensuring logical consistency.
🛠️ Seamless Integration: Built on the MCP protocol, it can be used directly within Claude Desktop.

A Real-World Example: Refactoring My SaaS Project with Crevo

Let me show you the power of Crevo with a real use case.

Scenario: Adding a "Team Collaboration" feature to my SaaS platform.

The Old Way (The Pitfall Way):

Jump straight to asking an AI to write the code.
Realize it's incompatible with the existing permissions system.
Modify the code, then modify it again, trapped in an endless loop.
End up with a mess of code that even I'm afraid to touch.

The Crevo Way:

Step 1: Generate User Stories

I told the Crevo chatbot:

"I want to add team collaboration features to my SaaS platform. Users should be able to create workspaces, invite team members with different roles (admin, editor, viewer), and manage permissions for shared resources."

Crevo automatically generated a detailed user story document, including:

User role definitions (Workspace Owner, Admin, Editor, Viewer).
A list of user stories (create workspace, invite members, assign roles, manage permissions, etc.).
A feature list with priorities.

Step 2: Generate the PRD

Continuing the conversation, Crevo generated a complete Product Requirements Document (PRD):

Product overview and core value.
Detailed functional requirements (workspace management, invitation flow, role permission matrix, resource sharing).
Non-functional requirements (performance, security, concurrency).
Constraints (tech stack, compliance like GDPR).

Step 3: Generate the System Architecture Design

Next, Crevo produced an architecture design document that defined:

Tech stack choices (backend framework, database, caching).
System layers (API, Business, Data).
Core component designs (permissions engine, workspace service, invitation service, notification service).
Key flow descriptions (permission checks, member invites, role changes).

Step 4: Generate the Business Process Design

Crevo then generated detailed business process diagrams and state machines for all key flows.

Step 5: Generate the Database Design

From there, Crevo generated a complete data model, including table schemas, field definitions, and relationships.

Step 6: Generate the API Design

Crevo automatically generated a RESTful API specification with endpoints, request/response formats, error codes, and auth methods.

Step 7: Generate the Development Plan

Finally, Crevo produced a detailed development plan with task breakdowns, milestones, and risk assessments.

With These 7 Docs, My Development Efficiency Doubled

Once I had this complete set of design documents, my workflow transformed:

I fed the architecture doc to the AI to generate the code skeleton → The code structure was clean and matched the design.
I fed the API doc to the AI to generate endpoint implementations → The APIs were standardized, and integration was smooth.
I fed the database doc to the AI to generate SQL and ORM models → The data model was complete with no missing fields.
I fed the business process doc to the AI to generate the business logic → Error handling was comprehensive and the logic was sound.

The results:

✅ Code quality improved dramatically, with almost no rework needed.
✅ Development speed increased by over 3x (because I wasn't constantly making fixes).
✅ Team collaboration became seamless; new members could get up to speed just by reading the docs.
✅ The project is highly maintainable, with a solid foundation for future iterations.

Crevo's 3 Core Advantages

1. Context-Aware Consistency: It Doesn't Forget

Crevo uses a session-based mechanism where each document generated becomes an input for the next. This solves the "short-term memory" problem of traditional AI tools. Every step builds on the last, ensuring a logically consistent whole.

2. Multi-Model Support: Automatically Picks the Best AI

Crevo has multiple AI models integrated (OpenAI GPT, Claude, Google Gemini, Ollama) and can be configured to use the best model for each document type. You don't have to switch manually; Crevo chooses for you, ensuring high-quality output every time.

3. Seamless Integration with Claude Desktop

Built on the Model Context Protocol (MCP), Crevo can be used directly inside Claude Desktop. Configuration is dead simple—just add one line to your config file:

{
  "mcpServers": {
    "crevo": {
      "url": "[https://crevo-mcp.aurakl.ai/mcp?key=YOUR_MCP_KEY_HERE](https://crevo-mcp.aurakl.ai/mcp?key=YOUR_MCP_KEY_HERE)"
    }
  }
}

After that, you can generate documents right from your chat, keeping your entire workflow in one place.

Who is Crevo for?

If you're one of the following, Crevo can be a game-changer:

Role	Core Pain Point	Crevo's Value
Product Manager	Not technical enough to write in-depth technical docs.	Automatically generates architecture, API designs, etc.
Tech Architect	Long documentation cycles at the start of a project.	Rapidly generates a full set of design docs.
Project Manager	Lack of standardized document templates.	Provides 7 professional templates to ensure quality.
Indie Developer	Not skilled at writing docs; limited time and energy.	Get pro-level docs at low cost to focus on coding.
Startup Team	No dedicated documentation role; doc quality is inconsistent.	Fills the documentation gap and boosts team collaboration.

Final Thoughts: In the Age of AI, Design Docs Are More Important Than Code

Many people assume that in the age of AI, documentation will disappear because "AI can write the code now."

But my experience has taught me the exact opposite: in the age of AI, design documents are more important than code.

Why?

Code is the 'how,' but design is the 'why.' An AI can generate implementation, but the architectural principles, business logic, and system boundaries require human thought. Design docs are the crystallization of that thinking.
Docs are the common language for teamwork. AI won't manage your team. But with clear documentation, team efficiency multiplies.
Docs are the map for your system. Without a map, you'll get lost in a jungle of code. With one, you always know where you are, where you're going, and how to get there.

So, stop asking an AI to blindly generate code. First, use Crevo to generate your design documents. Then, let the AI generate code on the right track. That is the right way to do AI-assisted development.

Try Crevo Now

🔗 Website: https://crevo.aurakl.ai
📖 Docs: https://crevo.aurakl.ai/docs
💬 Community: Join our Discord
Limited-Time Offer: Use the invite code 3EUSTLMI for 50% discount！