I had the same experience with JobScan. I felt that it helped me on my resume overhaul with some good clear suggestions, but didn't work well with editing and replacement, especially with my resume hosted in Google docs. I cancelled my subscription during the free trial.
Thankfully, this is another tool in the current arms war of job searchers versus corporate recruiting.
I've been watching a YouTube channel named Climate Town that recently did a video talking about natural gas, and leakage was a focus point in the video. It's like a documentary-style comedy channel, and I quite enjoy both the content and the format. It reminds me a bit of Jon Stewart and John Oliver.
One aspect that I find discouraging is the significant opportunity cost associated with the time and effort required during the job search process. Instead of using this time for skills development, exploring new technologies, or excelling in a current role, it's consumed by:
- Tailoring resumes to suit ATS systems (now with AI/other automation)
- Leetcode and study for live interviews
- Identifying suitable positions on job boards for cold applications
- Probing your network
- Responding to unsolicited contacts
- Chasing ghost recruiters
It's disheartening and frustrating to remain unemployed for an extended period, especially when I possess relevant experience and expertise, yet continually face rejections due to the challenges of navigating the recruitment pipeline.
The ATS stuff in particular is a real pain, and I wonder how much of that is companies overtuning their application process instead of adapting to the realities of a remote workforce. It feels bizarre to be ghosted or summarily rejected by 97% of companies and yet very easily pass through multiple interview rounds with the remaining 3% who actually read my resume.
The consequence of this is cynicism and the use of AI on both ends. Applicants are using AI assisted tools to apply because of the sheer quantity of jobs they have to apply to to get anywhere and recruiters are using AI assisted tools to screen because of the sheer quantity of candidates they have. It's robots talking to robots and everyone is miserable.
They wrote an interesting blog some time back about how "random shuffle" isn't necessarily what people want, and how their algorithm works (https://engineering.atspotify.com/2014/02/how-to-shuffle-son... ). That was a decade ago, so maybe their approach has changed or that it does not perform well under certain conditions (like the one you mention). It works well for me on most playlists on the order of 10s.
I agree with you that it seems to work fine on playlists of less than 50 or 100 songs.
The problem seems to be that on larger playlists they will only use 50-100 of the tracks to shuffle through. Most times I'm listening to music I just want to put on a shuffle of all my favorites and listen. It's been that way since I got my first CD changer. Maybe that's a super unusual use case, but it's my primary one, and I get really tired of hearing the same songs repeatedly over a week. YMMV, my wife for example likes listening to the same songs every day.
As I mentioned above: I copied my Spotify playlists to YTMusic and am doing the same "shuffle my liked songs" and I'm literally hearing songs Spotify hasn't played for me in years. Usually the algorithm complaint in music players is that they are using random rather than shuffle, but even in that case I'd think that 2K songs over 2-3 years, I'd be hearing SOME of those songs that YTMusic is playing but Spotify is not. The cynic in me figured that they were prioritizing the songs by the ones that made them the most money, or from artists that paid for placement. But something about their shuffle is just totally off.
Yes, it looks like there is some artificial placement. This may be driven by malice (some sort of paid or more lucrative placement, like you said), but also by stupidity (algo prioritizing songs already in the client cache, to save some egress bandwidth perhaps?).
So I started clearing the Spotify client cache more often, and it looks to me there is more diversity, at least on the auto-generated "recommended songs" playlists. But still, no hard proof of this.
The "recommended songs" playlists seem to have more diversity, but also seem to be fairly short (they'll repeat in a couple hours it feels like; I rarely listen to them when I'm working because they'll start repeating, and I don't usually listen to music for a large fraction of the day, so I'm guessing 1-2 hours).
My best guess is that they are assuming no playlist is more than 50-100 songs, and are limiting the shuffle to that number, so that a shuffle doesn't consume too many resources (memory, database hits, CPU cycles). Maybe someone, possibly in the distant past had a large playlist that caused service problems. And because of that they clamped WAY down to prevent it.
The threads feature is pretty close to useful, but it still buries information and it isn't clear to other users in a channel when a thread becomes "live" again unless they explicitly follow the thread or make a comment. Both of those actions require excessive clicking on the desktop client (at least for Ubuntu).
The most frustrating part for me is that keyboard navigation for threads is nonexistent, and it forces you to weave in and out with your mouse.
I thought this was going to be similar to https://play.kotlinlang.org/koans/overview . I used that as a quick way when doing other things to slowly learn parts of the language before diving in, was hoping this would be similar.
YAML templating is something I've come to truly hate. It seems that tools and services want the configuration to be "simple" by using a format that is fairly easy for humans to parse while not trying to force a language specific tool onto the users. However, when they move into the inevitable phase of wanting to be more configurable, they can move into confusing, unintuitive, and surprising territory. It also doesn't help that everybody keeps inventing their own way to do it. A few examples of YAML bastardization that give me headaches trying to understand:
* saltstack Jinja templating [1]
* GitLab CI `include` directive for including and merging external CI files together [2]
A few tools that I have seen take a decently pragmatic approach:
* Kubernetes resources that use ConfigMap and `envFrom` that declaratively say where to resolve a value from [3]
* Circle CI commands which offer some reusability with its "commands" and "executors" type features [4]. To me, Circle CI has both good and bad aspects with some templating and some clever patterns
On the other side of things, there is essentially fully programmable type configurations like Jenkins Pipeline Groovy `Jenkinsfile`, which can be a nightmare, too.
I think it is tough to find a sweet spot between making it configurable and expressive for users while retaining a low barrier of entry and not turning the configuration into a complete program itself. Tools like Terraform are trying to find that sweet spot as they slowly introduce more programmatic ways of configuration while still being declarative, like the fairly recent introduction of if statements and soon (I think) to be released for loops. As soon as users of a tool and service have more complex use cases, there needs to be some way to solve that. The most common way (it seems) is taking the easy and familiar of introducing templating.
> there is essentially fully programmable type configurations like Jenkins Pipeline Groovy `Jenkinsfile`, which can be a nightmare, too. I think it is tough to find a sweet spot between making it configurable and expressive for users while retaining a low barrier of entry and not turning the configuration into a complete program itself.
Nowadays, Jenkins pipelines can be configured in either the Jenkins-provided "Declarative Syntax" [1] or the Apache Groovy-based "Scripted Syntax", with the Declarative Syntax used as the default for examples on the Jenkins website. I guess they've found the best way to not have users turn the configuration into a complete program is to provide declarative syntax only in the default option. It's good to see Kustomize is built with this in mind, too.
`kustomize` is an interesting and useful tool, and it is cool to see it available directly in `kubectl` in 1.14. A lower barrier to entry is nice to have for tools like this. It is useful on its own, but I'm also looking forward to additional features like being able to reuse a single patch on multiple targets (https://github.com/kubernetes-sigs/kustomize/issues/720). I'd also like to see a clear schema for what the YAML needs to look like and what the keys do. I wish it folowwed a similar pattern as `kubernetes` resources where each resource had an `apiVersion`, and the `kustomization` itself had an `apiVersion`.
I just did the big 1.x -> 2.x refactor myself, and whilst it's not exactly pretty you can still reuse patches to some degree. It's just that every patch that will be applied independently now needs to be part of its own "base". You can then pull in the "base" wherever you want the patch applied.
It's far from ideal, so I do hope the process improves, but it does at least work
EDIT: Although, after properly reading that linked issue, I see you mean applying the same patch to different resources. I interpreted "target" to mean build target (e.g. deployment/app), you can use the same patch across different deployments (apps) and overlays (environment). However, I see your use case is a bit different.
> I wish it folowwed a similar pattern as `kubernetes` resources where each resource had an `apiVersion`, and the `kustomization` itself had an `apiVersion`.
Thankfully, this is another tool in the current arms war of job searchers versus corporate recruiting.