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Sentry · LinkedIn Post Flow

3 posts designed for Netlify's LinkedIn presence – thought leadership that naturally surfaces Netlify's value to Sentry's engineering and product leaders. Publish in sequence over 2 weeks.

How to use: Publish Post 1 on Day 1, Post 2 on Day 5, Post 3 on Day 10. The @Sentry mention creates visibility with their team without a cold DM. Copy any post to clipboard and paste directly into LinkedIn.

Post 1 of 3

The best time to catch a regression is before it hits production. The second best time is immediately after.

@Sentry is exceptional at the "second best time" — alerting you the moment a new error pattern appears after a deploy.

But the best time is before the deploy ever reaches production.

That's what per-PR preview environments enable. When every pull request gets its own isolated live environment, QA can run against real code before merge. @Sentry can even be instrumented on preview environments — catching errors in the PR context before they reach real users.

Two layers of regression protection:
1. Deploy Previews catch it before production
2. @Sentry catches it if anything slips through

The teams with the lowest MTTR tend to use both.

→ netlify.com

Post 2 of 3

Deploy context in error tracking is a superpower — but only if your deploys are atomic.

One of @Sentry's most powerful features is correlating error spikes with deploy events. "This error started appearing 4 minutes after deploy #312" — that's the kind of context that turns a 3-hour debugging session into a 10-minute rollback decision.

But that workflow only works cleanly when your deploys are atomic.

Atomic deploys mean each deployment is a complete, immutable snapshot — not a partial file sync. When you roll back, you're rolling back to exactly what was there before, not approximating it.

Netlify's deploy model is atomic by design. Every deployment is a snapshot. Rollback is instant. The @Sentry + Netlify pairing is one of the cleanest error/deploy feedback loops in modern frontend engineering.

→ netlify.com

Post 3 of 3

MTTR is a deployment problem as much as a monitoring problem.

Most teams focus on detection time when they talk about MTTR. @Sentry has made detection faster than ever.

But mean time to *recovery* also includes:
— Time to confirm the bad deploy is the cause
— Time to prepare and test the fix (or rollback)
— Time to actually deploy the resolution

That second part is where slow deployment infrastructure costs you. If deploying a fix takes 20 minutes of CI, manual approvals, and cache invalidation steps, your MTTR can't drop below that floor.

Instant rollbacks and atomic deploys on Netlify remove the floor. When you see the error in @Sentry, the rollback to the last good deploy is one click.

Detection + recovery, both fast.

→ netlify.com