AI is writing the code.
Who’s watching the runtime?

AI writes the code, configures the infra, triggers the deploy. When something breaks — you’re starting from zero. No context, no mental model, no idea where to look.

Dstl8 is the feedback loop between AI-generated code and runtime reality.

Dstl8 Free Trial

Closing the Runtime Feedback Loop

ControlTheory Dstl8: the feedback loop between AI-generated code and runtime reality.

Continuous Runtime Distillation

Dstl8 watches your runtime continuously — catching regressions, unexpected behavior, and unknown-unknowns before they snowball. Not periodic checks. Not dashboards you have to stare at. Always-on, proactive feedback that keeps pace with AI-generated code.

Full-Stack Runtime Intelligence

Supabase, Vercel, Railway, app logs, K8s events, CloudWatch, OTLP streams — Dstl8 correlates issues across your entire deployment chain, not siloed by source. Powered by Möbius AI for root cause, impact assessment, and fix recommendations.

In-Context Runtime Feedback

Dstl8 feeds runtime insights directly into Claude Code, Cursor, Codex, and your existing tools. No context switching. No knowledge tax. No rabbit holes. And it compounds — every signal makes the next diagnosis faster.

Works across your runtime deployment chain

ControlTheory Dstl8 integrates with runtime platforms and your favorite AI coding tools to continuously distill telemetry and deliver always-on analysis that keeps up with the speed, volume, and emergent behaviors of AI-generated code. Not periodic checks — continuous assurance. Dstl8 correlates issues across and between deployment chains — the opaque vibe-stack infrastructure that’s quick to set up but impossible to debug manually.

Learn more about our integrations…

Community testimonial logos for ControlTheory and Gonzo

TUI tool for log analysis. It looks cool and seems
really good. It even has a heatmap.
It can also receive logs in real-time via
OpenTelemetry. It’s super modern. Yay yay!

It’s exactly what I’ve been dreaming about as the
ideal UX for logs analysis in Uncloud CLI/TUI.

This is a great tool and it immediately became
part of my toolset.

I decided to give it a shot. It’s really nice! One of
the things I always loved about datadog’s log
analysis tool was its ability to surface log patterns.

Great tool ! awesome job!

Dstl8 distillation flask icon (distill logs into incident insights)
Gonzo terminal UI screenshot with log heatmap and pattern extraction
Dstl8 logo (ControlTheory observability distillation)

ControlTheory Dstl8 surfaces emergent patterns automatically – even when nothing was configured to watch for it. Built for engineering teams shipping AI-generated code, Dstl8 emergent observability discovers unknown behaviors in dev/staging before they become production incidents. Powered by Möbius AI, our three-layer architecture that distills telemetry, identifies patterns, and answers “what’s wrong and why” in real time.

Surface Emergent Patterns Automatically
Understand Impact Immediately
Get Answers, Not Dashboards
Surface. Understand. Answer.
See What We Surface
Gonzo quick start icon for real-time log analysis TUI
Gonzo in-terminal log analysis view (live charts and patterns)
Gonzo logo (open-source log analysis TUI)

Gonzo – built by ControlTheory – is an open source, real-time log analysis TUI inspired by k9s. Analyze log streams with beautiful charts, AI-powered insights, awesome filtering – all from your terminal.

Real-Time Analysis
Interactive Dashboard
Advanced Filtering
AI-Powered Insights
More Gonzo

Built for High Velocity Teams

Engineering Teams Using AI Coding Tools

AI-generated code creates the most acute monitoring gap – this is where ControlTheory shines.

Cursor, Claude Code, Copilot users
Deploying 5-10x faster than traditional development
Creating novel patterns that pre-configured monitors miss
Need to catch emergent behaviors in dev/staging
SRE and DevOps persona (find problems faster from log data)

Fast-Moving Engineering Teams

Even without AI tools, modern development velocity outpaces manual monitor configuration.

Teams deploying multiple times daily
Microservices with complex interactions
Distributed systems with emergent behaviors
Any environment where change > monitoring capacity
Developer persona (incident summaries, timeline, root cause context)