The 10x engineer has never been the person who coded the fastest. This is now more true than ever. And, it should reframe how you think about AI productivity.

The problem: AI generates code faster than teams can review (and debug) it. The constraint: AI tools need complete visibility into runtime context, not sampled fragments, to more accurately generate code or assist with debugging. The solution: 👇

This is how engineering leaders triage production issues: 👇 What this matrix doesn't show: the hidden cost of triaging blind.

AI tools boost velocity but erode deep system knowledge. Debugging and system understanding are the next challenge. Great article by Stephane Moreau: open.substack.com/pub/blog4ems...

Teams are rushing to add AI debugging to their observability stacks. But if the underlying data is: • Aggressively sampled • Missing payloads • Scattered across disconnected tools Adding AI on top just means faster access to incomplete data. Fix the data problem first.

Imagine you're looking for a specific email, but: ‣ Your inbox has 100,000 of them ‣ They're all labeled "Email" ‣ There's no search function ‣ Some emails are in Gmail, some in Outlook, some in Yahoo That's what AI agents face when trying to debug your system. 🧵

If your observability data isn't correlated across frontend and backend (or you're missing critical data due to sampling or lack of instrumentation) adding AI on top won't fix it. It'll just give you faster access to incomplete information. AI debugging is only as good as the data you feed it.

The best engineers were never the ones who wrote code fast or with “clever” solutions. The gap between top and bottom performers continues to widen.

There a debugging bottleneck few talk about: the hours engineers spend reconstructing what happened in production because critical context is missing. For example: • What payload did we send? • What did the external API return? • Which headers were set? • What did the middleware modify?

Which pie chart is your team living in? This is the difference between 3 hours of context switching and 10 minutes of clarity. Bad debugging = manual correlation across scattered tools. Good debugging = auto-correlated runtime context in one place.

Not all session replays are built for the same job. 📊 Product analytics tools answer questions about user behavior. 🪲 Debugging tools need to answer questions about system behavior. When bugs span APIs, services, and data layers, engineers need replays that correlate user actions to backend data.👇

Building developer tools means constantly stress-testing your own assumptions. If you signed up for Multiplayer and bounced during onboarding, understanding why is incredibly valuable to us. We’re offering a $50 gift card for a short conversation about your experience (15–20 min).

Question for teams using LogRocket: how much time do you spend jumping between tools to connect frontend issues to backend problems?

1/ Looking back at 2025, what stands out the most isn’t one single thing. It’s how a very small team managed to ship our product and achieve our goal: making debugging faster, less fragmented and less manual. And, they did it without sacrificing their sanity.

For engineering teams: what percentage of bugs in your app are purely frontend vs. backend or integration issues? As systems become more complex, partial visibility creates friction across support and engineering. This 👇 highlights why end-to-end context is becoming table stakes for debugging.

Teams design distributed systems and then try to debug them with workflows built for monoliths.👇 That’s why I’m such a believer in session-based visibility: pulling every layer of the system into one full stack session recording so engineers can investigate, debug, and fix issues faster.

I wish I could laugh, but it’s an uncomfortable truth most executives overlook. We obsess over churn rates and retention metrics, yet rarely measure how much inefficient debugging and support workflows cost the company (and who they actually serve).

Fullstory is fantastic for understanding user behavior. But debugging technical issues? That usually needs more than a replay of the UI. For engineering teams that need deeper visibility there's Multiplayer. dev.to/rohan_rajpal...

Raise your hand if this describes your technical support 👇 👀 If this feels familiar, I’d love to hear what’s working (or not) in your support flow. Ping me in the comments or DMs. My take: the real problem isn’t the bug. It’s the missing context.

User: What’s the benefit of switching to @multiplayer.app ? Me: 👇

We tend to talk about innovation as if it comes from sudden sparks of genius: one person, one idea, one breakthrough. But most progress doesn’t look like that. It’s rarely a lightbulb moment. It’s actually more like a relay race. 🧵

End-user support is supposed to be simple: a user reports a problem, the team fixes it, everyone moves on. In reality, it’s usually a marathon of manual steps, tool-switching, and endless back-and-forth. What’s tricky is that this marathon often ends with a (deceptively) positive outcome: 🧵👇

Looking for AI-related ice breakers for tomorrow’s MCP demo night in NY. Would this do? 😉

Guess the team at Anthropic could’ve used a Multiplayer session replay for that one 👀

Capture a full stack session recording once, use it for debugging, testing, support, feature development, and AI prompts. Here's everything you can do in Multiplayer 👀 👇

The best way to ‘win’ at Product Hunt is… not to chase upvotes? 🤔

Product hunt is dead. Here’s what happened as soon as we launched Multiplayer 👇. But here’s also why we did it anyway 🧵

We didn't launch on one of the biggest Product Hunt launch days of the last few weeks to “win”. We launched to share why full stack session recordings matter.

Our first post on Hacker News 🤩 This feels like a rite of passage. news.ycombinator.com/item?id=4537...

Debug faster, validate fixes, and even feed the whole story into your AI IDE. Multiplayer’s full stack session recordings allow you to seamlessly capture and resolve issues or new develop features - without breaking your flow.

We need to stop treating documentation as a static pile of text. The future is documentation as an experience: dynamic, contextual, and part of the workflow. Otherwise, the vicious cycle of outdated docs will never end.

I had the chance to speak at API World 2025 last week on a topic I’ve seen frustrate teams for years: API documentation. Because let’s face it: most developers don’t want to read static docs. Outdated Postman collections and PDF references slow everyone down.

When we build something at @multiplayer.app, we aim for insanely great quality. For example: we’ve been building a sandbox so you can test Multiplayer without signing up for a free trial. Seems straightforward, right? But in practice, there’s a lot to think through 👇

A GTM tip for anyone building for developers: If every feedback call is positive, it means you are only talking to fans and you are not learning enough. Growth comes from talking to people who are struggling with some aspect of your product because its likely they are not the only ones.

There’s a difference between searching the entire game to review a play and seeing it from the player’s helmet cam. --- That’s the difference between observability tools and Multiplayer.

Multiplayer doesn’t try to show you everything. It shows everything that matters for a single bug, test run, or user session. It’s not about coverage; it’s about clarity.

If your AI can write code but can’t help fix bugs, you’re only solving half the problem.

“Works on my machine” isn’t just a joke. It’s a symptom of broken workflows.

❝An information retrieval system will tend not to be used whenever it is more painful and troublesome for a customer to have information than for him not to have it.❞ 👇 That’s why we focused on getting devs *all* the data for a bug, in a single timeline.

If every outage still ends with 'can someone repro this?', you don’t have a reliable test case (👀👇). You have a one-off incident waiting to happen again.

Every minute your engineers spend reconstructing context is a minute they’re not solving problems.

You’re debugging a user-reported slowdown in your platform. The logs look fine. Metrics show a latency spike. But you’re stuck. Unsure if the issue is in your database, a flaky third-party API, or somewhere in your internal service calls.

It’s not “debug once, fix forever.” Unless you’re doing it right, it’s “debug once, waste hours, document never, repeat indefinitely.”

Traditional observability: investigate the crime scene. Multiplayer: catch the bug in the act, with the receipts. It’s time debugging felt like part of the dev workflow—not an incident report.

MTTR is more than a metric. It’s a symptom. Too many silos. Too many tabs. Too little context. Multiplayer helps teams fix what matters: the debugging experience itself.

A man with a watch knows the time. A man with two is never sure. Now imagine: 3 dashboards 4 alerting systems 5 time zones 👋 Welcome to distributed debugging.

One of the biggest bottlenecks in debugging is simply capturing the issue with the right frontend AND backend context. 🪲 Debug smarter, not harder.

Debugging distributed systems is brutal. Reproducing bugs, syncing across teams, and writing tests from memory. It all slows down engineering. With this new Multiplayer feature, with one click you can turn a debugging session into a test script your team can run, edit, and reuse.

No one wants to integrate with a platform that feels like reverse-engineering a puzzle. If you want users to stick around make your integrations easy to explore, run, and share.

As a woman founder in tech, these numbers are frustrating, but not surprising. We’ve known these stats for a while. And that’s exactly why representation in early-stage funding matters so much.