psoundy

When using Claude Code, it's possible to opt out of having one's sessions be used for training. But is that opt out for everything? Or only message content, such that there could remain sufficient metadata to derive useful insight from?

It's called 'setting an example'.

One might disagree with value of the example being set, but I'm not sure I would characterize his choices as in any way convenient for him.

I used kinesis advantage for 15 years, switched to a glove80 for the past 2, but recently went back to kinesis with an advantage2. The glove80 is very nice, but my hands are big enough that the kinesis thumb cluster works better for me and the cherry mx brown switches are much more satisfying than the low profile chocs.

So ymmv, but for those with larger hands, it may make sense to try a kinesis.

Even in the best case of what you are describing, how are these tools configured and their configuration maintained except via PRs to the repos in question? For every such change, N PRs having to be proposed, reviewed and merged. And all this without considering the common need (in a healthy project at least) to make cross-cutting changes with similar friction around landing a change across repos.

If you wanted to, sure, applying enough time and money could make it work. I like to think that those resources might be better spent, though.

Have you heard of OpenShift 4? Self-hosted Kubernetes by Red Hat. Every little piece of the control plane is its own 'operator' (basically a microservice) and every operator is developed in its own repo.

A github search for 'operator' in the openshift org has 178 results:

https://github.com/orgs/openshift/repositories?language=&q=o...

Not all are repos hosting one or more microservices, but most appear to be. Best of luck ensuring consistency and quality across so many repos.

If you like grainy mustard, I can’t recommend Kozliks’ Triple Crunch enough:

https://www.kozliks.com/

The number of deployed CRDs is not likely to be an issue. The number and size of custom resources (CRs - instances of CRDs) is potentially an issue.

Scalability is relative, and depends on many factors including but not limited to:

- the resources available on the hosts running apiservers and etcd members

- the number and size of resources (custom and native) that controllers will maintain

Relatively speaking, a cluster of a given size might be perfectly capable of handling on the order of many thousands of resources . Push that an order of magnitude and the overhead of serving LIST calls - marshaling json from etcd to golang structs for apimachinery and back again for sending over the wire - could exhaust an apiserver’s memory allocation. And since the impact of resources is cumulative, any one application relying on lots of CRDs might not destabilize a cluster on its own but might well contribute to an unhealthy cluster when running alongside similarly CRD-heavy applications.

The key takeaway is that the kube api is best thought of as a specialized operational store rather than a general-purpose database. Anyone wanting to rely on CRDs at non-trivial scale would be well-advised to test carefully.

Not sure why you’re being downvoted. OpenAPI schema generation is one of the most challenging elements of developing with CRDs. Anyone who is not an apimachinery SME is likely to struggle at some point to get their schema generated as expected.

tl;dr If you’re operating with CRDs at trivial scale, you probably having nothing to worry about. But operating with CRDs at scale is a different story and suggests careful testing with the specific applications involved.

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The usage patterns of native k8s types and the implications those patterns have on the scalability and reliability of etcd and the apiserver are relatively well-understood. CRDs can be a wild-card, though, and afaik testing efforts thus far have not investigated worst-case usage of CRD-based applications.

As commonly deployed, CRDs are served from the same apiservers and etcd cluster that serves the native types for a k8s cluster. That can result in contention between the CRDs supporting 3rd party additions to a cluster and the native types critical to the health of a cluster. This kind of contention has the potential to bring a cluster to its knees.

Efforts like priority and fairness seek to ensure that the apiserver can prioritize at the level of the API call. But that won’t prevent watch caches from OOM’ing the apiserver if excessive numbers of CRDs are present. The judicious use of quotas could head off the creation of an excessive number of objects, but it’s not just count that matters - the size of each resource is also a factor.

In theory, CRDs could be isolated from native types by serving them from an aggregated apiserver backed by a separate etcd cluster. afaik this not a supported configuration today, and even if it were the additional resources required to support it (especially the separate etcd cluster) may be prohibitive for many use cases.