Navigating the Bleeding Edge: From Svelte 5 Struggles to a Resilient AI Development Playbook

When AI Development Hits a Wall

In my recent articles, I’ve shared how Google Gemini and Code Assist transformed my development workflow. Building the Bug Hunter Toolkit in weeks, architecting complex features for The Shadow Cortex (TSC) - AI felt like a superpower.

Then reality hit. Hard.

My latest deep dive into TSC’s development revealed a critical flaw in my AI collaboration approach. What started as a productivity boost became a debugging nightmare that nearly derailed weeks of progress.

The culprit? Gemini Code Assist repeatedly defaulting to Svelte 4 syntax instead of adhering to Svelte 5’s newer “runes” syntax. This wasn’t just a minor syntax issue - it created a toxic mix of old and new code patterns within the same files.

The Nightmare of Mixed Syntax

Picture this: You’re building a complex data grid component, confidently letting AI generate boilerplate code. Everything compiles. Everything looks correct. Then you load the application and encounter infinite loops that make your browser freeze.

The problem wasn’t immediately obvious. Svelte 4’s reactive statements ($: ) mixed with Svelte 5’s runes ($state(), $derived()) created reactivity conflicts that were nearly impossible to trace. Variables would trigger updates that triggered more updates, spiraling into infinite loops that crashed the development server.

Here’s what a typical mixed-syntax component looked like:

<script>
  // Svelte 5 syntax
  let { data, columns } = $props();
  let currentPage = $state(1);
  
  // Svelte 4 syntax creeping in via AI
  $: totalPages = Math.ceil(data.length / pageSize);
  $: filteredData = data.filter(item => /* ... */);
  
  // More Svelte 5
  let sortedData = $derived(() => {
    return filteredData.sort(/* ... */);
  });
</script>

The $: reactive statements would trigger every time data changed, but the $derived() function was also watching the same dependencies. This created competing reactivity systems within a single component.

Beyond Syntax: The Confabulation Problem

The Svelte syntax issue was just the tip of the iceberg. I also encountered what I call AI “confabulation” - instances where the AI generated logically flawed code while appearing completely confident.

One particularly frustrating example: I asked Code Assist to implement a search query parser. It confidently generated a function that looked sophisticated but had a fundamental flaw in its logic - it would match partial strings incorrectly, leading to false positives in search results.

The code was syntactically perfect, properly typed, and even included helpful comments. But it was subtly, dangerously wrong.

The Great AI Confrontation

After the fourth time Gemini Flash 2.5 mixed Svelte 4 syntax into my carefully planned Svelte 5 components, I found myself doing something I never expected: arguing with an AI.

My chat history became a series of increasingly frustrated corrections:

“No, use $props() not export let”

“You’re doing it again - this is Svelte 5, not Svelte 4”

“I specifically said NO reactive statements with $:”

Is this healthy for me to be arguing with AI? I caught myself thinking during one particularly heated exchange. But the pattern was clear - despite explicit instructions, Gemini kept reverting to its Svelte 4 training.

So I did what any reasonable developer would do: I demoted it.

The Demotion Decision

I created a new rule in my workflow: Gemini Flash 2.5 was no longer allowed to write Svelte components. Period.

The new hierarchy became:

• Gemini Chat: Architecture and planning only
• Gemini Code Assist: Go backend code and TypeScript utilities
• GitHub Copilot with Claude Sonnet 4 model for frontend coding: All Svelte 5 components (at least until AI training catches up)

It felt strange demoting an AI, but the results were immediate. No more mixed syntax. No more infinite loops. No more three-day debugging sessions.

The Cost of Over-Reliance

After three days of debugging what should have been a straightforward feature, I did some sobering math:

• Time saved initially: ~8 hours of boilerplate generation
• Time lost debugging: ~24 hours of frustrating detective work
• Net productivity impact: -16 hours
• Psychological cost: Questioning every AI suggestion moving forward

This wasn’t just a technical setback - it was a fundamental shift in my relationship with AI development tools. The honeymoon phase was over.

Starting Fresh: A Battle-Tested Strategy

Instead of continuing to untangle the mess, I took a step back and redesigned my entire AI collaboration approach. This wasn’t just about fixing code - it was about fundamentally changing how I interact with AI tools that don’t always respect boundaries.

Sometimes you have to be the manager, even when your “employee” is artificial intelligence.

The new strategy is built on discipline, explicit constraints, immediate verification, and most importantly - knowing when to take tasks away from AI entirely.

1. Salvaging What Works

First, I meticulously extracted the architectural pieces that were sound regardless of syntax issues. Core Go backend logic, database schemas, and high-level component structures were preserved. The framework-specific implementation details were what needed rebuilding.

This reinforced an important lesson: architectural decisions are still primarily human work. AI excels at implementation, not design.

2. Test-Driven AI: My New Religion

Every new feature now begins with test-driven development (TDD) - not just as a coding methodology, but as an AI interaction strategy.

Before any production code gets written, I define:

• Clear unit tests for backend logic (Go)
• Component-level integration tests for frontend behavior (SvelteKit)

These tests serve as the undeniable “source of truth.” If AI-generated code breaks a test, it gets rejected immediately. No exceptions, no “I’ll fix it later.”

3. Explicit Rules for AI Chat (Project Manager Mode)

When using Gemini’s chat interface for planning, my prompts now include:

Explicit Version Declarations:

"Plan this feature using Svelte 5 with Runes syntax, GoLang 1.22, TypeScript 5.x, SvelteKit 2.x"

Negative Constraints:

"DO NOT use Svelte 4 reactive statements like `$:`
AVOID `export let` for props - use `$props()` instead
NEVER mix Svelte 4 and Svelte 5 syntax patterns"

Output Format Requirements:

"Provide file-by-file implementation details with exact function signatures and required imports"

4. Stringent Rules for Code Assist (Execution Mode)

When switching to Gemini Code Assist in the IDE:

Hyper-Specific Prompts: Instead of: “Create a data grid component” Now: “Implement the DataTable component using Svelte 5 runes syntax only, with props defined via $props() and state via $state()”

Immediate Verification: Every code chunk gets reviewed immediately. If it causes test failures or introduces mixed syntax, it’s back to the prompt with more explicit constraints.

The Results: Bulletproof Development

This refined approach has transformed my development experience. The new rules might seem rigid, but they’ve eliminated the debugging nightmares while preserving AI’s productivity benefits.

Recent features for TSC have been implemented cleanly, with consistent syntax and reliable behavior. More importantly, I can trust the code that gets generated because it’s been validated at every step.

Key Takeaways

For AI-Assisted Development:

• Always specify exact framework versions in prompts
• Use negative constraints to prevent common AI mistakes
• Implement TDD to catch AI hallucinations immediately
• Mixed syntax is worse than no AI assistance
• Don’t be afraid to “demote” AI when it can’t follow instructions

For Working on the Bleeding Edge:

• Newer frameworks require more explicit AI guidance
• Test everything immediately - don’t accumulate untested AI code
• Your role shifts from coding to architecting and validating
• AI tools need training on YOUR specific constraints
• Sometimes arguing with AI is a sign you need to take control

For Maintaining Sanity:

• It’s okay to restrict AI to tasks it actually does well
• Creating AI “job descriptions” can prevent frustration
• Manual coding isn’t failure - it’s strategic decision-making

Looking Forward

My journey with The Shadow Cortex continues, but now with a battle-tested approach to AI collaboration. This experience taught me that working effectively with AI isn’t about finding the perfect tool - it’s about developing the discipline to guide these powerful but imperfect assistants.

The bleeding edge will always have sharp corners. The key is learning to navigate them without getting cut.

Building complex applications with AI assistance? I’d love to hear about your experiences - both the wins and the failures. Connect with me on LinkedIn to continue the conversation.