January 5, 2026
Dashboards, drama, and déjà vu
Observability's past, present, and future
Engineers say they’re drowning in data and begging for tools that actually help
TLDR: An engineer’s essay says modern monitoring tools created a data flood but not clarity. Commenters split between calling for AI that fixes outages automatically, insisting old-school stats and discipline matter more, and joking about time-travel debugging—proof that teams want less noise, faster answers, and sleep.
Observability—the tools that help teams see what their apps are doing—got a reality check in Sherwood’s new post. Engineers swarmed the comments to shout: it’s 2026 and this still feels like homework. One pro confessed setup is the real boss fight, saying it took weeks just to make sense of their data. Another, a data specialist, cheered the critique: we’re great at blasting out signals, not so great at turning them into answers.
From there the thread split into camps. The “do your math” crowd insisted no tool replaces statistics; you still need brains, not just buttons. The futurists went full sci‑fi, with one deadpan suggestion to “just enable time‑travel recording in production” and call it a day. And the automation maximalists demanded less firehose, more insight: dream scenario is AI that wakes up before you do, spots the bug, and ships the fix.
There was love for newcomers like Honeycomb, but also side‑eye at overflowing dashboards, flaky alerts, and the eternal on‑call grind. The vibe? We’ve paid the bill, but we still can’t see the problem. Whether the answer is better math, bigger AI, or literal time travel, the crowd wants relief—now. Vendors, consider yourselves on notice. Seriously. Please.
Key Points
- •Early 2010s cloud-native complexity made traditional logs and metrics insufficient for debugging.
- •Distributed tracing emerged around 2010 and became widely adopted to analyze service interactions.
- •Observability, popularized by Twitter’s engineering team, became a recognized discipline and product category.
- •Teams over-invested in observability tooling and processes, making it an end rather than a means.
- •Despite widespread platforms like Datadog, Grafana, and Sentry, observability remains labor-intensive and often ineffective.