We just crossed 250k downloads. We have a free tier and a paid tier, but one thing we’re thinking a lot about right now is how do we keep people coming back.
Its a Freelance collaboration Platform, Would love to hear from other founders/freelancers:
What makes you keep coming back to a platform after the initial signup?
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This is the rough checklist I try to follow:
1. Confirming the real impact
Before going deep, I check if this is actually affecting users or just an alert firing.
Is it one service?
One region?
One customer?
Internal only?
Full outage?
2. Reading the alert properly
Not just the alert name, but what actually changed.
Latency?
Error rate?
CPU?
Memory?
Pod restarts?
Queue depth?
Availability?
3. Checking what changed recently
This is usually one of the fastest paths.
New deploy?
Config change?
Feature flag?
Infra change?
Dependency update?
Scaling event?
4. Looking at the main service metrics
I usually check the basic signals first:
Error rate
Latency
Traffic
Saturation
CPU/memory
Retries
Queue length
Dependency failures
5. Checking logs with a specific question
Logs can become a rabbit hole.
I try not to just search randomly. I usually ask:
What errors started after the alert?
Which errors increased suddenly?
Are the errors from this service or a dependency?
6. Using traces to confirm the path
If traces are available, I check where the request is actually slowing down or failing.
Is it the app?
Database?
Cache?
External API?
Another internal service?
7. Checking Kubernetes/infrastructure signals
For Kubernetes incidents, I check:
Pod restarts
CrashLoopBackOff
OOMKilled
Pending pods
Node pressure
Failed probes
Throttling
Ingress/service issues
Recent events
8. Checking blast radius
This helps avoid overreacting or underreacting.
Is it isolated?
Is it spreading?
Is it tied to one cluster, node pool, region, tenant, or dependency?
9. Checking if this happened before
This is underrated.
Old incidents, Slack threads, tickets, postmortems, and runbooks can save a lot of time.
A lot of incidents are not completely new. They just look new under pressure.
10. Writing the first useful hypothesis
Before fixing, I try to write one simple sentence:
“This is probably related to ___ because ___ changed and ___ signal supports it.”
That forces the investigation to become clearer instead of just chasing dashboards.
For me, observability is useful only when it reduces investigation time.
More dashboards do not automatically mean better incident response.
Curious how others do this.
What do you usually check first during an incident: metrics, logs, traces, deploy history, Kubernetes events, or past incidents?
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