Multiplayer

👀 How great would it be to push record and never ask “what happened?” again?

But for real.

With backend data, automatically correlated with frontend end screens.

BUT self-healing doesn’t work without visibility.

You can’t fix what you can’t explain.

And AI can’t propose meaningful fixes without runtime context.

That’s where Multiplayer comes in. 😉

Self-healing today is less “magic algorithm” and more coordination of solutions, tools, and technologies to observe, reason, fix, deploy.

The result?

🧠 Smart debugging
💥 Faster recovery
🚢 More shipping, less guessing

...

• ML-powered root cause analysis → identify patterns, detect failures early

• Gen-AI assistants → propose fixes from real bugs

• Automation/orchestration tools → execute mitigations (via APIs, workflows, bots)

The implementation is the hardest part because it’s not a single tool or technique. It’s a stack:

• Observability platforms → surface anomalies

• Runtime capture systems → full-stack session recordings of the system state and user actions

...

AI tools brought a resurgence of interest in self-healing because they offer something the old methods didn’t:

☑️ flexible, intelligent recovery paths
☑️ predictive behavior
☑️ remediation based on real system data

The concept borrows from ideas in fault-tolerant and autonomous systems, aiming to make applications more resilient, especially in complex or distributed environments.

Useful, but brittle. And very manual.

“Self-healing” originally meant clever, rule-based, hardcoded automations:

⁃ Restart a crashed service
⁃ Switch to a backup DB
⁃ Retry a failed request

Think: graceful degradation, resilient architecture, automated failover systems.

What is self-healing code?

It’s not a buzzword. It’s a real concept, one that’s been around for decades.

But with AI now in the mix, it's evolving fast. Let’s break it down 🧵👇

With full context and prompt-optimized input, your AI can suggest accurate, system-aware fixes.

The solution is simple: receive better AI fix suggestions.

To do that, send your AI IDE full-stack session recordings: frontend screens, backend traces, metrics, logs, and full request/response content and headers.

Add to that that debugging AI-generated code feels 10x harder because you didn’t write it.

When you write code yourself, you build a mental model. You know the shape of the data, the edge cases, the intent behind each line.

With AI-generated code you inherit logic without understanding it.

The model doesn’t know your architecture, runtime state, or the exact execution path.

It doesn’t have any awareness of intent or business rules.

In short: the code may look like it works, but that’s not the same as it actually working.

There are many reasons why AI coding assistants can introduce bugs:

• No system context
• Vague prompts
• Outdated or hallucinated APIs, libraries, methods…
• Happy-path logic only
• No business awareness
• …

Vibe debugging emerged naturally alongside the rise of vibe coding.

It highlights the difficulties of debugging AI-generated code without fully understanding it.

In short: create 20,000 lines in 20 minutes, spend 2 years debugging

What is vibe debugging?

If you’ve used an AI coding assistant, you’ve probably done it, even if you didn’t call it that.

Let’s unpack this very unofficial, very real debugging pattern 🧵👇

It pays to be proactive, especially when data captured at the right moment (and across the full stack) can shave hours off your MTTR.

Every missing trace, every unlabeled log, every ‘unknown’ in a dashboard … these are debts that will compound during your next production incident.

99% of debugging time is just trying to get the system to confess.

Building an internal API integration?

Here’s an example of how you can:

✔️ Chain internal API calls to model real workflows

✔️ Prototype code logic inline

✔️ Add interactive custom visualizations using HTML/CSS/JS

If your system goes down and the first step is 'open five dashboards,' you don’t have a debugging workflow.

You have a scavenger hunt.

We’re not saying your system is the problem.

But we’re also not not saying it.

😍

YouTube video by TryMultiplayer How left-shifted observability speeds up debugging

Worth your time, especially if your team is building (and debugging) complex systems: youtu.be/1TLatomEi_A

Missed our live session on faster debugging workflows?

Check the recording to learn:
🧠 Why observability needs to move left
🐛 How to debug with full-stack context (and no guesswork)
🤖 What AI can actually do to fix bugs

How left-shifted observability speeds up debugging From bug reported to bug fixed

✔️ What it takes to go from symptom to fix, fast

Join now → my.demio.com/ref/OVOB4CgV...

🔴 We’re live!

Tune in to see:

✔️ Full-stack debugging in action
✔️ How to feed real runtime context into your AI IDE
✔️ What it takes to go from symptom to fix, fast

How left-shifted observability speeds up debugging From bug reported to bug fixed

Last chance to attend → my.demio.com/ref/OVOB4CgV...

Today’s the day!

🐞 Bug reports don’t have to mean Slack threads, log spelunking, and guesswork.

Join us for a live walkthrough of how left-shifted observability + AI-assisted debugging can speed up your entire workflow.

📅 July 23 | 2PM ET | 11AM PT | 8PM CEST

How left-shifted observability speeds up debugging From bug reported to bug fixed

Free to attend → my.demio.com/ref/OVOB4CgV...