NeverBlink AI

Database reliability and performance, for AI with AI. Currently supporting #ClickHouse #OpenSearch #Elasticsearch.

[NeverBlink AI @neverblinkai]

NeverBlink now supports #ClickHouse: Cloud, self-hosted, and the Kubernetes operator. The worst ClickHouse failures never page you: merge backlogs, stalled TTLs, materialized views dropping data. We catch them while they're still warnings, with the fix attached.

https://t.co/Iut7EirwrN

[NeverBlink AI @neverblinkai]

Honest question for full-stack engineers who own a Postgres database: do you know when to use CREATE INDEX CONCURRENTLY? Or how to tune autovacuum for a high-churn table?

No shame either way. But someone on call should.

[NeverBlink AI @neverblinkai]

Wix merged Frontend, Backend and Mobile into one Engineering Guild. Linear hires Product Engineers. Vercel has Design Engineers.

Everyone owns everything now. Which means nobody deeply knows the database. And the database is the one layer that doesn't forgive.

[NeverBlink AI @neverblinkai]

https://t.co/6FH4jSKTZd

[NeverBlink AI @neverblinkai]

We asked the Top Voices in Search which lesser-known books shaped their thinking. David Tippett's pick: Designing Data-Intensive Applications. "It's by far the best book I've ever read, whether you're a search engineer or just a regular engineer."

[NeverBlink AI @neverblinkai]

https://t.co/gBmqSP1sEH

[NeverBlink AI @neverblinkai]

'Is the database up' is the easy question. The valuable ones: which queries eat the CPU, how far the replica lags, whether disk runs out next Tuesday. Database monitoring exists to answer those before a user files a ticket.

[NeverBlink AI @neverblinkai]

https://t.co/K3GnsZXKiZ

[NeverBlink AI @neverblinkai]

Critical cluster alerts go where your team actually is. NeverBlink supports Telegram and Discord as alerting destinations, alongside Slack and email. On-call at 2am rarely happens next to a desktop inbox.

[NeverBlink AI @neverblinkai]

https://t.co/4dRypR6RbZ

[NeverBlink AI @neverblinkai]

DORA is five metrics now (deployment rework rate joined the four), and the report renamed itself to State of AI-assisted Software Development. The research keeps landing on one point: the constraint isn't writing code faster. It's everything downstream of the merge.

[NeverBlink AI @neverblinkai]

pgvector keeps embeddings in the database you already run: no separate vector service, no ETL shuttling embeddings around, no second backup story. For plenty of teams that beats a dedicated vector DB on operational grounds alone. #PostgreSQL

[NeverBlink AI @neverblinkai]

A DROP TABLE stuck in an infinite retry loop, erroring every 5 seconds, invisible to every dashboard. Nobody graphs "operations that never finish." but an AI aware of it could find it.

It was found by a NeverBlink's agent reading error logs the way a DBA would. Monitoring only shows you what you thought to chart. Generic AI wouldn't know what to look for. NeverBlink did it.

[NeverBlink AI @neverblinkai]

7/ None of this slows you down. Investigations still drop from an hour to minutes. You're just not betting the database to get there.

[NeverBlink AI @neverblinkai]

1/ How to let an AI near your production database without losing sleep. The principles we've settled on while building NeverBlink:

[NeverBlink AI @neverblinkai]

6/ Treat confident urgency as a smell. Agentic debugging doesn't fail by hesitating, it fails by building an articulate story on one wrong premise. Guardrails matter most at the exact moment the agent is most sure of itself.

[NeverBlink AI @neverblinkai]

4/ Frontiers runs 11 self-hosted Elasticsearch clusters on it: 70% faster incident detection, 80% less time managing them.
Where does SRE ownership stop in your stack?
https://t.co/N6KOBJvKmb

[NeverBlink AI @neverblinkai]

1/ Ask an SRE team about their Kubernetes setup and you get a detailed answer.
Ask why the OpenSearch cluster went yellow last Tuesday and it gets vague.
The database is where site reliability engineering quietly stops.

[NeverBlink AI @neverblinkai]

3/ We built NeverBlink to be the AI SRE for your databases. It does the root-cause analysis on metrics, logs, queries and config, and tells you what to fix and why. Your team decides what gets applied. Engineers on call 24/7.