Sure we can use an LLM but I can for now click around faster (if those breadcrumbs exist) than it can reason.
Also the LLM would only point to a direction and I’m still going to have to use the UI to confirm.
Here were some of my consideration points against a generator (I had a list for batteries too):
1. the previous owners electrified the house so having a tank just for a generator didn't make sense
2. per 1, a small tank wouldn't last very long and if we're out of power for multiple days gas delivery is unlikely to be happening anyway.
3. tank + generator didn't have a practical placement location for us
4. smaller portable generators didn't make sense from a maintenance perspective since they didn't auto test.
5. what happens if it fails a self test right before a storm?
6. we get intermittent power cuts / fluctuations / outages throughout the year and the generator + ATS wouldn't protect sensitive electronics well
Edit: ATS + batteries can play nice together then I might look at doing a small portable as aux backup
I highly doubt that. Maybe there will be an evolution to k8s but fundamentally it solves a whole host of challenges around defining the environment an application runs in.
The issue we've ran into is some team at work decides to re-write an entire table and things get backed up until they stop updating rows.
- Connection pooling / pipelining and circuit breaking is a must at scale. The clients are a lot better than they used to be but it's important developers understand the behavior of the client library they are using. Someone suggested using Envoy as sidecar proxy, I personally wouldn't after our experience with it with redis but it's an easy option. - Avoid changing the cluster topology if the CPU load is over 40%. This is primarily in case of unplanned failures during a change. - If something goes wrong shed load application side as quick as possible because Redis won't recover if it's being hammered. You'll need to either have feature flags of be able to scale down your application. - Having replicas won't protect you from data loss so don't treat it as a source of truth. Also, don't rely on consistency in clustered mode. - Remember Redis is single threaded so an 8xl isn't going to be super useful with all those unused cores.
Things we have alarms on by default: - Engine utilization - Anomalies in replication lag - Network throughput (relative to throughput of the underlying EC2 instance) - Bytes used for cache - Swap usage (this is the oh shit alarm)
Sure we can segment this into code generation models and code review models but are engineers really going to want to be questioned by a code review tool on "what are you trying to do?" or are they just going to merge and pull the slot lever again?