Building agents that's fast enough to help a human on a sales call respond in real time is no joke
But @thatguybg and @microhq figured out how to do it with @mastra + @recallai:
something new just happened here, at our live Postgres hacking session:
https://t.co/pfzgcz5fKk
@recallai was streaming our words right to @claudeai code, so we worked on new @PostgreSQL patches (support of logical decoding on physical standbys replaying WALs from archive) just talking
claude code became acting participant in this zoom call
going to explore this model further
look at it here: https://t.co/pfzgcz5fKk
we had an incident because we migrated traffic to a brand new s3 bucket.
our millions of servers instantly crushed the new bucket’s partitions and started getting slammed with 5xx errors
@SJP1804 we did a slow rollout and rolled back as soon as we saw the errors
after tracking down the root cause, we restarted with a slower rollout, which worked
but it was definitely surprising to us that S3 buckets "learn" your load pattern, and new buckets need to be trained!
@Mo69796244 auto partitioning works well for predictable workloads that scale up and down gradually.
too bad that real world workloads often don't fit that criteria!
The previous bucket had intelligently adapted its partitioning to our specific traffic pattern.
The new bucket had zero history and its default partitioning was a terrible fit for our workload.
Fun fact: when an S3 server is overloaded it will return a 503 Slow Down