Multiplayer
@multiplayer.app
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Full stack session recording. End-to-end visibility in a single click. Capture once, use for debugging, testing, support, feature development ... Try it for free: multiplayer.app
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multiplayer.app
Full stack session recordings: record. code. fix. repeat.
multiplayer.app
AI: ‘Bug fixed.’

Production: 💀

Senior engineer: opens Multiplayer session replay in silence, to send to the AI.
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5️⃣ In short: An AI Engineer’s job is to make AI useful.

And whether you love or hate the title, it’s here to stay.

AI Engineering is what separates “playing with prompts” from building production AI.
multiplayer.app
4️⃣ So what do AI Engineers actually build?

Internal tools, customer-facing features, integrations with LLM APIs and context systems, chat interfaces, and automation pipelines.

They spend their time on glue code, orchestration, and context management.
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3️⃣ AI is no longer a crutch.

Back in 2023, using AI tools in your workflow was “cheating.”

If you leaned on a copilot too much, you weren’t a “real engineer.”

Fast-forward to 2025: using AI (or at least exploring it) seems to be table stakes.
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2️⃣ Think of it like this:

Just as DevOps bridged dev and ops, AI engineering bridges app development and machine learning.

The AI Engineer connects APIs, vector databases, prompts, context managers, and frontend UIs, building cohesive, intelligent features.
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1️⃣ First, a quick reality check.

An AI Engineer isn’t a research scientist or ML PhD.

They’re the bridge between traditional software and applied AI. In short, the people who integrate LLMs into real products, systems, and workflows.

They don’t train models. They use them.
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🧵 What is an “AI Engineer,” really?

It’s one of the hottest job titles of 2025 but also one of the most misunderstood. Let’s unpack what the role actually means (and why it matters).
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Why record *everything* when 90% of it doesn’t matter?
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Also… Remote recording is now in beta. Silently enable recording of user experiences, ensuring nothing gets lost in translation from vague bug reports.
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You can also enable continuous recording which keeps a lightweight rolling buffer that auto-saves frontend + backend exceptions and errors the moment they appear.

No repro steps. No missed bugs.
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Yes, you can absolutely capture on-demand session replays.

Manually start / stop to capture the exact reproduction steps

BUT…
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Most session replay tools force a tradeoff:

🎥 Record on demand → you miss what happens outside the window.
📼 Record everything → you drown in noise and costs.

We took a different approach 🧵
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Sometimes the frontend data isn’t enough.

Sometimes (okay, always) you also want to know what happened in the backend.
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“Is this the real bug? Or it’s just fantasy?” 👈 Strong start to my chat with Freddie Mercury about our latest bug.

Luckily, Multiplayer caught the whole performance: frontend, backend, and the error’s big solo.
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The Multiplayer API key authenticates your application when sending session recording data to Multiplayer.

The API key ensures that session recordings, traces, logs, and other data are securely tied to your account.
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👀 Did you know that with Multiplayer you can:

👉 Use a single API authentication key for both frontend and backend data,

OR

👉  Generate separate API authentication keys for frontend and backend if you want stricter separation or different access scopes
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(3) Notebook: add the API requests that reproduce the flow + assertions for expected outcomes.

(4) AI via MCP: “Generate a patch to satisfy this recording’s notes”

(5) Run the notebook to validate the fix; attach results to the PR.

(6) Keep the recording + notebook as living documentation.
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Here’s an example of a concrete SDD workflow with Multiplayer:

(1) Capture a session where the bug or desired behavior appears.

(2) Annotate the exact UI state, API call, and trace span with requirements.
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Specs don’t have to be static documents anymore.

With Multiplayer, they can be:

✅ Captured from real behavior
✅ Annotated with requirements
✅ Run as executable notebooks
✅ Fed directly into AI tools for accurate fixes & features
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A full stack session recording + annotations fits perfectly with this shift:

✔️ Frontend screens + backend traces
✔️ Requests/responses + headers
✔️ Notes, sketches, requirements

Executable with Notebooks, shareable with your team, and consumable by AI through our MCP server.
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So why the renewed attention now?

Because AI needs structured context.

A good spec → fewer hallucinations, more accurate code, tests, and features.

Without it, copilots and agents are left guessing.
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Spec-driven development flips the script:

‣ The spec is the source of truth
‣ It’s machine-readable and executable
‣ From one spec, you can generate docs, SDKs, tests, and implementations

Think OpenAPI, gRPC protos, GraphQL SDL.
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Traditionally, specs were an afterthought.

Teams wrote them once, then let them drift while “the real work” happened in code.

Code became the source of truth. Specs became stale PDFs or wikis.