🚀 Introducing Observability Architect GPT — Your Copilot for All Things Telemetry

I’ve been tinkering with AI‑assisted workflows for months, and today I’m thrilled to announce Observability Architect GPT, now live in ChatGPT. Think of it as a drop‑in teammate that speaks fluent metrics, logs, and traces — and never sleeps.

Why build it? Because every time someone asks, “How should I instrument my code for distributed tracing?” or “How do I shard Kafka topics for 10 TB/day of logs?” Instead of copy‑pasting the same guidance, I encoded my playbooks into a dedicated GPT so anyone can get high‑quality answers instantly.

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What can you do with it?

• Green-field design – Generate architecture diagrams and component lists for a brand‑new telemetry pipeline (e.g., OpenTelemetry → Kafka → Flink → ClickHouse).

• Instrument on the fly – Spit out ready‑to‑run code snippets for tracing a Spring Boot service, a Node.js Lambda, or a Rust sidecar.

• Capacity & cost modeling – Forecast storage and egress costs from “We’ll emit 20k spans/sec with 10 attributes” to a dollar figure in seconds.

• SLO & alert tuning – Suggest error‑budget policies and alert thresholds that balance reliability and on‑call sanity.

• Root‑cause brainstorming – Walk through failure scenarios (high p99 latency, noisy GC, Kafka backlog) and propose investigative queries in PromQL, LogQL, or SQL.

• Learning & mentoring – Explain, in plain English or deep‑dive RFC‑style, why tail‑based sampling works, how eBPF profiling differs from JFR, or whether to batch or stream your transforms.

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How to try it

1. Open ChatGPT

2. Search for “Observability Architect” in the GPT store

3. Start a conversation: “Design a multi‑tenant metrics pipeline for 5M time‑series/sec.” Watch it go to work!

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Join the feedback loop

I’ll keep refining the model with real‑world questions and new patterns. Have a crazy use‑case? Found a blind spot? DM me on LinkedIn — I’d love to fold your edge cases into the next update.

Until then, happy instrumenting!