// Yao AI Development Partner - Core handler example
// This shows how Yao processes developer requests

package main

import (
    "context"
    "fmt"
    "github.com/yaoapp/yao"
    "github.com/yaoapp/yao/ai"
)

func main() {
    // Initialize Yao with AI capabilities
    yao.Init()
    
    // Create AI development partner instance
    partner := ai.NewPartner(
        ai.WithModel("gpt-4"),
        ai.WithContext(context.Background()),
        ai.WithMaxTokens(1000),
    )
    
    // Example: Ask AI partner for code help
    request := "Write a Go function to sort integers"
    
    response, err := partner.ProcessRequest(request)
    if err != nil {
        fmt.Printf("AI partner error: %v\n", err)
        return
    }
    
    // Display AI-generated code
    fmt.Println("AI Partner Response:")
    fmt.Println(response.Code)
    
    // The 'magic' - it's basically a wrapper around existing AI APIs
    fmt.Println("\n// Note: This is essentially an API wrapper with extra steps")

The AI Development Partner: Your New Passive-Aggressive Coworker

Let's be clear about what an "AI development partner" actually means in 2025. It's not Skynet writing your microservices architecture. It's not even a particularly smart autocomplete. It's essentially a chatbot that's read enough Stack Overflow threads to sound like it knows what it's talking about, while actually just rearranging boilerplate code you could have copied from the documentation.

Yao's GitHub description reads like every tech startup's pitch deck: "all-in-one," "enables developers," "with AI as a development partner." Translation: "We took several existing concepts, wrapped them in Go, and added an AI chatbot because that's what gets funding these days." The 7,472 stars suggest either genuine interest or that many developers have been tricked by the sparkle emoji in the title.

Why Your AI Partner Will Gaslight You About Your Own Code

The fundamental problem with AI "partners" in development is the same problem with every corporate team-building exercise: forced collaboration with entities that don't actually understand the problem. Your AI partner doesn't know your business requirements. It doesn't understand your technical debt. It certainly doesn't remember that time the database migration failed at 2 AM because of that one weird edge case.

What it does do is:

• Suggest solutions that work perfectly in theory but fail spectacularly in production
• Explain your own code back to you with the confidence of a senior engineer who's actually just guessing
• Generate documentation that sounds comprehensive but misses all the important details
• Create the illusion of productivity while actually adding technical debt

The All-in-One Trap: Because Monoliths Are Back (Apparently)

Remember when we spent the last decade breaking apart monoliths into microservices? Remember when we realized that distributed systems are hard, but at least they're not giant balls of mud? Well, Yao is here to remind us that history doesn't just repeat itself—it does so with AI assistance.

An "all-in-one application engine" sounds convenient until you realize it means:

• Vendor lock-in disguised as convenience
• Learning yet another framework's idiosyncrasies
• Debugging through layers of abstraction when things go wrong
• Depending on a single open-source project's maintainers not getting bored and abandoning it

The irony is delicious: we've spent years building tools to make development more modular and composable, only to have startups repackage everything into new monoliths with AI glitter sprinkled on top.

The Go Language Choice: Performance or Bandwagon?

Yao is written in Go, which at least suggests someone thought about performance. Go is the language choice that says "we care about concurrency" while actually meaning "we wanted to use something trendy that isn't Rust." It's a perfectly reasonable choice for a framework, but let's not pretend it's revolutionary in 2025.

The real question is: does writing a framework in Go automatically make it better? Or is it just another case of tech stack fashion determining architecture? Remember when everything had to be in Node.js? Then everything had to be in Rust? Now we're in the "Go for backend, TypeScript for frontend, and Python for AI" phase of the cycle. By 2027, we'll probably be writing everything in a new language called "ZigRustScript" that compiles to WebAssembly running on blockchain.

The AI Hype Cycle: From "Revolutionary" to "Standard Feature" to "Why Is This Still Here?"

Yao represents phase 2.5 of the AI hype cycle: the "AI as a feature" phase. Phase 1 was "AI will replace all developers." Phase 2 was "AI will augment developers." Phase 2.5 is "we'll call our chatbot a 'partner' and hope nobody notices it's just a fancy autocomplete."

What's particularly amusing about this trend is how quickly "AI-powered" has become the new "cloud-native" or "blockchain-enabled"—buzzwords that get slapped on products to make them sound innovative while adding questionable actual value.

Consider what "AI as a development partner" actually delivers versus what it promises:

• Promised: Intelligent code suggestions based on context
• Delivered: Autocomplete that sometimes suggests relevant functions
• Promised: Understanding of business requirements
• Delivered: Pattern matching on variable names
• Promised: Proactive problem detection
• Delivered: Linting with extra steps

The Real Test: Will Anyone Actually Use This in Production?

The GitHub stars are impressive, but let's talk about what really matters: will anyone build actual business-critical applications with Yao? Or will it join the graveyard of "interesting open-source projects" that get forked, starred, and then abandoned when developers realize they'd rather use established tools that don't require explaining their architecture to a chatbot?

The pattern is familiar:

1. New framework emerges with ambitious claims
2. Gets trending on GitHub/Hacker News
3. Early adopters build toy projects
4. Someone tries to use it for something real
5. Discovers the limitations, missing features, or bugs
6. Either contributes fixes (becoming an unpaid maintainer) or abandons it
7. Framework either matures or joins the 99% of open-source projects that never reach 1.0

Yao's success will depend not on its AI features, but on the boring, unsexy stuff: documentation, stability, community, and whether it actually solves real problems better than existing solutions.