Back to Projects

Strayl.devStrayl Logo

Active

AI-Powered Logging & Debugging Platform

Sep 2025–Present

Strayl.dev
Live Demo View Product
The Problem

Developers spend hours debugging production issues. Traditional logging tools lack AI capabilities and require manual investigation. Finding root causes in logs is time-consuming.

The Solution

AI-powered logging platform with tiny SDK (Node, Python) that sends structured logs. Features AI debugging engine, semantic log search, MCP server integration, and one-click auto-fix that generates pull requests automatically.

Key Innovation:

  • •AI Debugging Engine - automatically analyzes logs and generates code fixes
  • •Tiny SDK - drop-in logging for Node, Python with zero configuration
  • •Semantic Log Search - find anything by meaning, not exact keywords
  • •MCP Integration - works with Cursor, Claude Code, Windsurf, Codeium
  • •One-Click Auto-Fix - creates fully-tested pull requests automatically
  • •Structured Logging - replace console.log() with AI-ready telemetry
Results

30+ Active Users

No marketing, organic growth

$10K Daytona Credits + $1K AWS Credits

Fix production issues in seconds — with one click

└─ 30+ Active Users (pre-seed stage)

└─ 60% retention rate (week 1)

└─ One-click fixes for production issues

└─ Semantic search across all logs

Tech Stack

Frontend

  • ├─React
  • ├─TypeScript

Backend

  • ├─Python
  • ├─Node.js
  • ├─MCP Server

AI & Integrations

  • ├─AI Debugging Engine
  • ├─Semantic Search
  • ├─Model Context Protocol (MCP)

Infrastructure

  • ├─Structured Logging SDK
  • ├─Real-time Dashboard
What I Learned

Challenge 1: Real-time log processing at scale

Problem: Real-time log processing at scale

Solution: Efficient structured logging with minimal overhead

Learning: Structured data enables better AI analysis

Challenge 2: Accurate AI-generated fixes

Problem: Accurate AI-generated fixes

Solution: Context-aware analysis using production data

Learning: Real production data improves fix quality

Challenge 3: Integration with multiple coding agents

Problem: Integration with multiple coding agents

Solution: MCP protocol for universal compatibility

Learning: Standard protocols enable broader adoption

Roadmap

Q4 2025

  • ├─Expand SDK support (Go, Rust)
  • ├─Enhanced AI debugging accuracy
  • ├─Team collaboration features

Q1 2026

  • ├─Launch pro tier
  • ├─Advanced analytics dashboard
  • ├─Custom AI model training

You might also like:

ML-Based PID Auto-Tuning

Machine Learning PID Parameter Optimization

ISSAI Research Project

Linear Actuator Controller with PID Control