What was the diagnosis process like? I'm in my early 30s and have been putting off trying to get an ADHD diagnosis (and medication) even though the symptoms clearly resonate, because I'm concerned that I'll be dismissed as just seeking pills. In college I tried to get diagnosed and was dismissed as lazy and just seeking a prescription. Did you have to deal with any of that skepticism?
I would highly highly recommend ETL as service, after adopting it recently. It substantially changes your relationship with your data sources in a really positive way. And frankly, ETL for common data sources is code that you just don't need to write.
I would say that you should pilot with a few ETL vendors. We currently use Fivetran, they're fine but we've had enough burps that I cannot cold recommend them over other vendors. I cannot for the life of me remember the details, but I think we went with them over Stitch for pricing reasons.
If people can't get off their asses to stop working with a company that has worked with organizations responsible for thousands of deaths, I don't think support of Gab is going to drive anyone's payment platform decisions.
The inefficiency you speak of is not inefficiency of code review, but rather the inefficiency of reaching a shared, deep agreement on what your code should look like and how it should work. Code review is just a place that it pops up.
Thinking of it as inspection alone is a disservice to the cultural value of code review. You want a process that can teach a team member to contribute with lesser inspection? Code review.
I've typically found that with a new team or team member, initially there are a lot of patches returned for modification. After 3-9 months, it tends towards 80% of patches being one-shot LGTMs, the remaining 20% having spec issues or substantive style issues (ie. this module structure will bite us in the butt because...)
This idea of communicating shared knowledge also points at more efficient ways to do that, if CR is a bottleneck:
- Technical onboarding - Google does a great job of this. Taking time to explain how to work with your technologies, and what the expected code style is.
- Linting + style guides - Arguing over style is dumb.
Completely disagree. I catch bugs, and have my bugs caught, at least a few times a month.
Beyond the "this will likely deadlock without a timeout", "this does not clean up after itself on exception", "if the cache is not hot, this operation you assume is 1 sec will take 1 min" type thing — one of the strongest points of code review is having a conversation about "this code is hard to understand" and turning code that's correct if you think about it really hard into obviously correct code.
It takes a reasonably strong reviewer to get there though, and that's not always possible.
Why was this article hidden from the front page? A similar satellite journalism article from the BBC about a killing in west Africa was allowed on the front page a few weeks ago.
The origin of those shared commands isn't from readline, but rather from emacs key bindings that made it into both readline and MacOS: https://jblevins.org/log/kbd
Any particular sites/webapps you like as examples of hierarchical design? I am a tech lead and struggle to come up with anything better than Material Design's nav drawers with sub-items, or equivalent.
I build internal tools in my organization. Most of our developers are on MacOS and use Chrome day to day. Most of our users are on Windows. Whenever a browser specific issue comes up, we do our best to address it, but our first response is typically "go install Chrome". Chrome adoption is 97% internally, 2.5% Safari, and the remaining 0.5% is Edge. We have a 0% internal Firefox usage rate.
Draw from that what you may, but I bet that dynamic is happening in other places.
I'm on Mac OS 10.11.6 (15G22010). The regression might be due to some interaction between Firefox and changes in the OS, if you're on a more recent version.
I spent a few minutes trying to replicate on a MacBook Pro (Retina, 13-inch, Late 2013) with Firefox 62.0.2 and could not replicate, browsing a number of resource intense sites.
Former employee and it's been a few years, but for what it's worth I was actually very reassured by what I saw there in terms of internal data security. For all products I worked with or knew something about in an engineering capacity, there was no unlogged access to personal data. Access to personal data without clear, approved reasons would result in termination. The controls were extremely strong.
I'd be more concerned in Google's case about "legitimate" but still problematic uses of data: disclosure to law enforcement, crazy specific ad targeting, machine learning for military applications, etc.
there are whole other approaches to the problem... which are extremely successful in practice, with real observational data sets.
I'm going to echo my sibling commenter, would love to hear it if you have any pointers on where to start reading.
I'm a software engineer but data work comes up all the damn time. Seeing coworkers approach causality in a hand-wavy way without knowing a more rigorous approach to suggest is frustrating! I had hoped Pearl's book was exactly that.
If your first thought is "Oh ----, I'm a manager now. How the hell do I do this?" I'd highly recommend The Effective Manager by Mark Horstman. It's prescriptive in a helpful way, and was brilliant in helping me find my footing when I started management.
Thanks to both of you for your time doing that discussion!
@slap_shot and anyone else — I'm curious if you have thoughts on, or even have heard of the Ballerina language? It's a programming language for doing data integration work, built by the ESB/integration consultancy WSO2. It seems to have a lot of eng resources sunk into it but surprisingly little fanfare.
And to pile on the anecdotes, I've been in the exact opposite situation where we needed somebody right now to handle a new requirement, and we hired someone "pretty good" after already taking longer than the rest of management was happy with to hire.
They ended up being pretty weak, having trouble shipping, eating large amounts of onboarding and management attention, as well as other engineers patching up their work to make it serviceable. They were just good enough to make it seem worth the slog too, so they sucked a bunch of extra labor out of everyone else for the better part of a year before they left.
In retrospect, I made the right decision with what I knew in the moment — but with 20/20 hindsight I should have waited.
I'd highly recommend the xonsh shell for scripting if you're familiar with python: http://xon.sh/tutorial.html
Once you do tutorial and learn the rules for going between shell and python, it's super easy to slice and dice.
I have always disliked that moment of "yes, a shell script is probably the best tool for this job" that then leads me to banging my head against bash syntax for an hour. Python libraries like `sh` are great, but still a little high overhead. Now the twice a month I have to shell script I find to be pretty fun, and I'd highly recommend xonsh.