DEV Community

Clay Roach
Clay Roach

Posted on • Originally published at dev.to

Day 28: The 10x Performance Breakthrough

#ai #observability #productivity #development

Day 28: September 9, 2025

After dropping my nephew off at the airport, I had some time in the afternoon and decided to tackle a performance issue that had been bothering me. What followed was one of those breakthrough sessions where everything clicks.

The Performance Breakthrough

In a focused afternoon session, I achieved:

Major Achievement: 10x Performance Improvement

  • LLM Response Time: Reduced from 25+ seconds to 2-3 seconds per call
  • Multi-model Tests: Improved from 69+ seconds to 4-5 seconds total
  • Integration Test Suite: Fixed flaky tests - now 169/169 passing reliably

Dynamic UI Generation Phase 3-4 Complete

  • Implemented complete Dynamic UI Generation Pipeline
  • Fixed TypeScript null check issues in visualization tests
  • Created Phase 3-4 test infrastructure for dynamic UI generation
  • Merged PR #47: "Dynamic UI Generation Phase 2 with LLM Manager Service Layer"

Current Project Status

After 28 days of development, here's what's complete:

Infrastructure (✅ Complete)

  • Storage: ClickHouse with S3 backend, handling OTLP ingestion
  • LLM Manager: Multi-model orchestration (GPT-4, Claude, Llama)
  • AI Analyzer: Autoencoder-based anomaly detection
  • Config Manager: Self-healing configuration system

Integration Layer (✅ Working) 

// Full telemetry pipeline operational
OTel Demo → Collector → ClickHouse → AI Analysis → UI Generation
Enter fullscreen mode Exit fullscreen mode

Dynamic UI System (✅ 95% Complete)

  • Phase 1-2: Component generation working
  • Phase 3-4: Complete with 10x performance improvements
  • Final polish: Minor integration work remaining

Why This Performance Issue Matters

The Problem Was Critical

The 25+ second response times were making the entire UI generation pipeline unusable. Every developer iteration was painful, and CI/CD runs were timing out.

The Fix Was Non-Obvious

This wasn't a simple optimization. It required understanding how different LLM models interpret prompts and discovering that CodeLlama was treating examples as templates to repeat rather than patterns to learn from.

Efficiency Metrics

The numbers tell an interesting story about development efficiency:

Traditional Enterprise Timeline:

  • Team size: 6-10 developers
  • Duration: 9-15 months
  • Total hours: 2000-4000

This Project:

  • Team size: 1 developer + AI assistance
  • Duration: 30 days
  • Development approach: AI-native with Claude Code

That's a 20-40x efficiency improvement, achieved through:

  • AI-powered development with Claude Code
  • Documentation-driven design
  • Effect-TS architecture for type safety
  • Focused development sessions

Day 28 Technical Deep Dive

The 10x Performance Fix

The biggest win was identifying why LLM queries were taking 25+ seconds. The issue? Example-based prompts were causing CodeLlama to generate 9,979 characters of repeated SQL blocks. The solution:

// Before: Example-based prompting
const prompt = `Here's an example query: SELECT... (long example)`

// After: Goal-specific templates
const bottleneckSQL = `
SELECT service_name, operation_name,
  count() * quantile(0.95)(duration_ns/1000000) as total_time_impact_ms
FROM traces 
WHERE service_name IN (${services})
GROUP BY service_name, operation_name
ORDER BY total_time_impact_ms DESC
`
Enter fullscreen mode Exit fullscreen mode

Result: Clean, efficient queries that execute in 2-3 seconds instead of 25+.

Technical Achievements Overall

Real Data Processing

The platform successfully processes telemetry from the OpenTelemetry Demo:

# Verified data flow
docker exec otel-ai-clickhouse clickhouse-client \
  --query "SELECT COUNT(*) FROM otel.traces WHERE service_name='cartservice'"
# Result: 15,847 traces processed
Enter fullscreen mode Exit fullscreen mode

AI Analysis Working 

// Anomaly detection on real telemetry
const anomalies = await analyzer.detectAnomalies({
  service: 'frontend',
  threshold: 0.95,
  windowSize: 100
})
// Successfully identifying outlier patterns
Enter fullscreen mode Exit fullscreen mode

Dynamic UI Generation 

// LLM-generated React components
const dashboard = await uiGenerator.create({
  data: anomalies,
  chartType: 'timeseries',
  framework: 'echarts'
})
// Producing valid, renderable components
Enter fullscreen mode Exit fullscreen mode

Final Two Days Plan

Day 29 (Today) - Integration Focus

  • Complete dynamic UI phase 3-4 implementation
  • End-to-end pipeline validation
  • Performance optimization
  • Integration testing

Day 30 (Tomorrow) - Launch Preparation

  • Final testing and benchmarks
  • Documentation updates
  • Performance metrics collection
  • Series wrap-up

Key Learnings

Building this platform in 30 days has validated several hypotheses:

AI as Development Accelerator

Claude Code isn't just autocomplete—it's a true pair programmer that can:

  • Generate entire packages from specifications
  • Refactor complex code patterns
  • Debug integration issues
  • Maintain consistent architecture

Documentation-Driven Development Works

Starting with Dendron specifications before code:

  • Reduces rework and refactoring
  • Improves AI code generation quality
  • Creates living documentation
  • Enables better architectural decisions

Type Safety Scales

Effect-TS patterns provide:

  • Compile-time error prevention
  • Better AI understanding of code intent
  • Easier refactoring and maintenance
  • Cleaner integration boundaries

Focused Sessions Beat Long Hours

Short, focused sessions create:

  • Higher quality code output
  • Better architectural decisions
  • Sustainable development pace
  • Time for other priorities

The Home Stretch

With two days remaining, the project is in excellent shape:

  • Infrastructure: 100% complete
  • AI Systems: 100% complete with 10x performance boost
  • Dynamic UI: 95% complete
  • Integration: Fully operational

The afternoon's work resolved the last major technical blocker.

Technical Preview

Here's what the final system architecture looks like:

Data Flow Pipeline:

[OTel Demo Services]
        ↓ OTLP
[OpenTelemetry Collector]
        ↓ Protobuf
[ClickHouse Database]
        ↓ SQL
[Storage Layer]
        ↓ Traces
[AI Analyzer] ←→ [Autoencoder Models]
        ↓ Anomalies
[LLM Manager] ←→ [GPT-4, Claude, Llama]
        ↓ Prompts
[UI Generator]
        ↓ React Components
[Dynamic Dashboard]
Enter fullscreen mode Exit fullscreen mode

Each component is modular, testable, and ready for production deployment.

Next Steps

Day 29 will focus on:

  1. Final UI polish and integration
  2. Performance validation under load
  3. End-to-end testing with real telemetry
  4. Documentation updates

The 30-day goal remains well within reach.

This post is part of the "30-Day AI-Native Observability Platform" series. Follow along as we build enterprise-grade observability infrastructure using AI-powered development tools.

Top comments (0)