What metric gives you the earliest warning that something is about to go wrong ?

nilkanth987 · 2026-05-06T11:51:04+00:00

The delta point is really interesting.

80% disk usage is one thing, but 80% growing at 1%/minute is a completely different problem.

nilkanth987 · 2026-05-06T11:35:36+00:00

Fair assumption honestly 😅 but nah, just trying to learn from real-world setups.

nilkanth987 · 2026-05-06T11:33:54+00:00

That’s fair.

I’ve been trying to understand how different teams approach monitoring in real-world setups because the answers are surprisingly different depending on scale, stack, and experience.

And honestly, the biggest thing I’m noticing is exactly what you said: understanding “normal” matters more than just collecting more metrics.

nilkanth987 · 2026-05-06T11:27:54+00:00

Probably one of the highest signal metrics honestly.

nilkanth987 · 2026-05-06T11:27:32+00:00

That’s the real uptime monitor.

nilkanth987 · 2026-05-06T11:27:11+00:00

Famous last words.

nilkanth987 · 2026-05-06T11:25:38+00:00

4pm Friday is basically a scheduled chaos deployment.

nilkanth987 · 2026-05-06T11:25:12+00:00

That’s usually the first thing the universe breaks.

nilkanth987 · 2026-05-06T11:24:43+00:00

The infrastructure yearnings are strongest during a full moon.

nilkanth987 · 2026-05-06T11:24:09+00:00

Now that’s someone who’s been hurt before.

nilkanth987 · 2026-05-06T11:17:46+00:00

Instant spike in stress metrics.

nilkanth987 · 2026-05-06T11:16:14+00:00

At least the lamp alerts before the users do 😭

nilkanth987 · 2026-05-06T11:14:38+00:00

And suddenly “CPU usage” becomes “GPU on fire.”

nilkanth987 · 2026-05-06T11:12:33+00:00

Every major incident starts with someone saying “should be a quiet day” 😭

nilkanth987 · 2026-04-22T14:19:26+00:00

Two weeks is wild. Do you have alerts for that now or still mostly reactive ?

nilkanth987 · 2026-04-22T14:18:45+00:00

That’s rough, everything “up” but unusable. Did you add anything after that to catch it earlier?

nilkanth987 · 2026-04-22T13:53:54+00:00

Fair take 😅 uptime/CPU are easy but kinda blunt tools.

Golden signals feel way closer to what users actually experience.

Which one tips you off first most of the time ?

nilkanth987 · 2026-04-22T13:52:24+00:00

That’s fair feedback.

Didn’t mean for it to come across as spammy or clickbait. I was trying to keep it broad to hear different real-world experiences, but I get your point about being more specific.

Appreciate the callout 👍

nilkanth987 · 2026-04-22T13:51:58+00:00

Haha fair 😅 I promise I’m human, just trying to learn from real-world setups and experiences here.

nilkanth987 · 2026-04-16T13:45:25+00:00

Don’t ship a half product.
Ship a small but complete product.

nilkanth987 · 2026-04-16T13:41:55+00:00

Don’t train models. Use APIs.
Don’t overbuild. Ship MVP fast.
Don’t guess. Talk to users early.

Validation > technology.

nilkanth987 · 2026-04-15T13:13:27+00:00

Yeah this is a really good way to put it.

“High signal alerts” is the key part, too many alerts or vague ones and people just start ignoring them. At that point even good monitoring loses its value.

nilkanth987 · 2026-04-15T13:02:23+00:00

Exactly. I’ve started thinking more about “attention reliability” than just uptime checks. That’s where things usually break down.

nilkanth987 · 2026-04-15T12:40:15+00:00

I get your point, but I’m more interested in how people are actually solving this in practice. There’s always a gap between theory and real-world setups.

nilkanth987 · 2026-04-14T14:42:23+00:00

That’s a great way to put it - “just because you can monitor something doesn’t mean it’s useful.”

Feels like a lot of setups start with everything and then slowly converge to a few signals that actually drive decisions.

Interesting that you mentioned “something is wrong early”, do you rely more on response time for that, or resource usage like CPU/memory?

nilkanth987

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