AI Tech Stack Advisor

Get personalized technology recommendations from an AI-powered senior software architect.

Access: /tools/clients/tech-advisor

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Overview

The Tech Stack Advisor is an interactive chat interface where you can discuss technology decisions for your projects. Powered by OpenAI GPT-4o-mini with streaming responses, it provides:

• Framework and language comparisons
• Database recommendations
• Architecture advice
• Scaling considerations
• Cost implications
• Hiring difficulty assessments

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How to Use

1. Ask a question - Type your technology question or describe your project
2. Get streaming response - See the answer appear in real-time
3. Follow up - Continue the conversation with clarifying questions
4. Save context - Conversation history persists in your browser

Suggested Questions

Start with these common questions:

• "I'm building a SaaS app - should I use Next.js or Remix?"
• "What's the best database for a high-traffic e-commerce site?"
• "React vs Vue vs Svelte - which should I learn first?"
• "Do I need a backend framework or can I use serverless?"
• "What tech stack would you recommend for a real-time chat app?"

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What It Provides

Technology Comparisons

Detailed pros and cons for framework/language choices:

• Performance characteristics
• Developer experience
• Ecosystem maturity
• Community support
• Learning curve

Database Recommendations

Guidance on data storage based on:

• Data structure (relational vs document vs graph)
• Query patterns
• Scale requirements
• Consistency needs
• Cost considerations

Architecture Advice

Structural recommendations covering:

• Monolith vs microservices
• Serverless vs containers
• API design (REST vs GraphQL vs gRPC)
• Event-driven patterns
• Caching strategies

Practical Considerations

Real-world factors including:

• Team expertise
• Hiring difficulty
• Time to market
• Maintenance burden
• Vendor lock-in risks

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Example Conversation

User:

I'm building a marketplace app where users can buy and sell vintage items. What tech stack would you recommend?

AI Tech Advisor:

Great project! For a vintage marketplace, here's what I'd recommend:

Frontend: Next.js 14 with App Router

• Server-side rendering for SEO (crucial for product listings)
• Image optimization built-in (important for vintage item photos)
• Incremental static regeneration for product pages

Backend: tRPC or Server Actions

• If staying in Next.js ecosystem, Server Actions reduce complexity
• Full type safety from database to UI
• Easy to add API endpoints later if needed

Database: PostgreSQL + Prisma

• Relational data perfect for users, products, orders, reviews
• Prisma gives you type-safe queries and easy migrations
• PostgreSQL handles complex queries for search/filtering

Search: Algolia or Meilisearch

• Full-text search essential for "vintage leather jacket 1970s"
• Algolia is managed (easier), Meilisearch is self-hosted (cheaper)

Payments: Stripe Connect

• Built for marketplaces with buyer/seller splits
• Handles compliance, disputes, payouts

Image Storage: Cloudflare R2 or AWS S3

• R2 has no egress fees (significant savings for image-heavy sites)

Would you like me to go deeper on any of these choices, or discuss specific trade-offs?

User:

What about authentication? And would you change anything if we need to launch in 4 weeks?

AI Tech Advisor:

Authentication: Clerk or NextAuth.js

For a 4-week timeline, I'd strongly recommend Clerk:

• 10-minute setup with Next.js
• Built-in social logins (Google, Apple, Facebook)
• User management dashboard included
• Handles email verification, password reset, MFA

NextAuth.js is free but requires more configuration time.

4-Week Launch Adjustments:

1. Skip custom search initially - Use PostgreSQL ILIKE queries, add Algolia in v2
2. Skip seller onboarding - Start invite-only with manual seller setup
3. Use Stripe Checkout - Redirect to Stripe instead of embedded checkout
4. Vercel deployment - Zero DevOps, just push and deploy
5. Skip mobile app - Make the web app responsive, native apps later

This gets you to market fast. The architecture still scales - you're not taking technical debt, just deferring features.

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Technical Details

AI Persona

The AI acts as a senior software architect representing a consultancy:

• Deep expertise in web, mobile, cloud, and AI/ML
• Practical recommendations with trade-off analysis
• Honest about when NOT to use certain technologies
• Soft CTAs for consultation when appropriate

Conversation Memory

• Last 10 messages sent with each request
• Full history stored in browser localStorage
• Conversation persists across page refreshes
• Clear chat button to start fresh

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Features

Real-time Streaming

Responses appear word-by-word as generated, providing:

• Immediate feedback
• Better perceived performance
• Natural conversation flow

Markdown Rendering

Responses include formatted:

• Code blocks with syntax highlighting
• Bold and italic text
• Bullet points and numbered lists
• Links and references

Fullscreen Mode

Expand to full viewport for:

• Distraction-free conversations
• Better readability
• More context visible

Keyboard Shortcuts

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What to Ask

Good Questions

• "Should I use X or Y for [specific use case]?"
• "What's the best stack for [project type] with [constraints]?"
• "How do I architect [feature] at scale?"
• "What are the trade-offs between [option A] and [option B]?"
• "Is [technology] mature enough for production?"

Provide Context

Better answers come from sharing:

• Project type and industry
• Team size and expertise
• Timeline and budget
• Scale expectations
• Specific constraints

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Limitations

• Not a substitute for requirements analysis - General guidance, not detailed specs
• Rapidly changing landscape - AI knowledge has cutoff date
• No project-specific context - Doesn't know your codebase
• Opinions vary - Different architects may recommend differently

For project-specific architecture decisions, consider booking a consultation.

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Privacy & Security

• Conversations sent to OpenAI API
• Chat history stored in browser localStorage only
• No server-side storage of conversations
• No tracking or analytics on chat content
• Clear chat removes all local data
• GDPR compliant processing

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Use Cases

• Developers exploring new technologies
• Founders validating technical decisions
• CTOs getting second opinions on architecture
• Students learning about technology trade-offs
• Agencies researching options for client projects