Find what broke.
Across every deployment.
Dstl8 streams logs from Vercel, Supabase, Kubernetes, and 50+ sources, then uses Möbius AI to tell you what failed, why, and where — with evidence from your actual logs. Built for teams shipping AI-native code.
14 day trial · No card required · Full access
⭐ 2,625 GitHub stars on Gonzo · ★★★★★ “brew install, pointed at our K8s cluster, Möbius was answering ‘why is this service degrading’ in 90 seconds. Felt like cheating.” — Platform Engineer, Series B AI infrastructure startup
brew install control-theory/dstl8/dstl8
AI ships in 30 seconds.
Debugging takes 3 hours.
Your stack wasn’t built for the volume — or the failure modes — of AI-generated code.
Unknown unknowns
Dashboards catch failures you’ve already seen. AI-generated code breaks in ways nobody wrote an alert for — novel service interactions, unexpected dependencies, agent loops that drift without throwing. Your alerts stay silent while production degrades.
10x the logs, same two eyes
Claude Code, Cursor, and Copilot ship code 10x faster — and generate 10x more log volume. Pipelines fragment across Vercel, Supabase, Kubernetes, OTel. Finding the one line that explains the failure means correlating six dashboards by hand at 2am.
Reactive, not proactive
Your observability tells you something broke. It can’t tell you what, why, or where to look first. By the time the page fires and you’ve opened the laptop, customers have already noticed. Debugging is 90% finding, 10% fixing — and finding is where you lose the night.
Dstl8 finds what broke before your customers do. Built for engineering teams shipping AI-generated code, Dstl8 catches unknown behaviors in dev and staging — the novel failure modes your dashboards weren’t configured to watch for. Powered by Möbius AI, our three-layer architecture distills telemetry, identifies patterns, and answers “what’s wrong and why” in real time.
Works with your stack. Don’t rip anything out.
ControlTheory 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…
Built for high velocity teams
Teams shipping with AI coding tools
AI-generated code creates the most acute monitoring gap — novel failure modes your dashboards were never configured to catch. Dstl8 closes the gap by surfacing unknown behaviors in dev and staging, before they page you in production.
Teams deploying multiple times a day
Modern velocity outpaces manual monitor configuration — every deploy introduces behavior nobody wrote an alert for. Dstl8’s Möbius AI watches the system as it actually runs, not as it was configured to run.
Start Here
See what’s actually happening.
Connect your deployment chain. Surface emergent patterns. Get root cause analysis with fix recommendations — right in your editor.
↻ Intelligence that compounds — every runtime signal makes the next one sharper.
Dstl8 — Supabase runtime analysis
Open Source
Not ready for Dstl8? Start with Gonzo.
Free, open source log analysis TUI. Real-time charts, pattern detection, AI-powered insights — right in your terminal. No account, no config.
brew install gonzo
Get started
Install & Configure Dstl8 in Under 2 Minutes.
Try the Dstl8 CLI and TUI for continuous runtime feedback. Install it, add sources, connect the MCP server into Claude Code, and more.
brew install control-theory/dstl8/dstl8
dstl8 signup
curl -fsSL https://install.dstl8.ai/script/dstl8-cli | sh
npx dstl8
nix run github:control-theory/dstl8
Download from GitHub Releases
Quick Start
# 1. Install the CLI
brew install control-theory/dstl8/dstl8
# 2. Create a Dstl8 account (or `dstl8 login` if you already have one)
dstl8 signup
# 3. Add a source so logs flow in
dstl8 sources add vercel
# 4. Connect your AI agent, auto-detects MCP-compatible clients on your machine and configures them
dstl8 install -all
dstl8 install claude-code
Add Sources
# Add Sources
dstl8 sources add kubernetes
dstl8 sources add cloudwatch
dstl8 sources add vercel
dstl8 sources add supabase
dstl8 sources add otlp
dstl8 sources add github