I am working on a productivity/tasks/notes tracking application. I have tried everything out there but none of them are versatile enough. I am going to scratch my own itch and will see if I am able to scratch it for others too.
Not to be the guy who suggest the solution I’ve found to a be a panacea for all worlds ails, but have you checked out Org-Mode in Emacs? Its incredibly flexible, in both the good and bad ways.
Do you mean https://github.com/dspinellis/git-issue or https://github.com/stephencelis/ghi? CLI workflows tend to work well for me, but I never thought to look up cli issue managers.
Yes. I just push issues to a remote server (in my apt). Then I use jrnl for notes. I can find stuff by tag. I’ll even reference jrnl tags in my git-issues. I use it for everything, not just coding projects.
Edit: I use dspenelli’s. I also version my jrnls and push to a remote server.
Nice visuals!
A couple of remarks though:
O(3) = 3
. Big-O notation is about asymptomatic behavior so it kind of only makes sense with a symbolic n.Thanks Arthur for the feedback. I mostly kept it as simple as possible for the sake of fitting it in an image and be easier to understand at the same time. I agree with all the points that you have mentioned and will address them in the associated article. Thank you!
Not at the moment - I just made it and tweeted about it. However, I plan on putting it under “X in One Picture” series on https://roadmap.sh
I posted it elsewhere, for posterity, or something:
I think most of the backlash against Twitter is that it’s a poor format for reading long form writing, content is usually light or unsubstantiated (rumors), it’s a redirect to a better primary source.
For the most part, Twitter links do pretty well in Lobster.rs: https://lobste.rs/domain/twitter.com
The logarithmic complexity examples on the right are wrong. If T(10) = 1, T(20) = 2, T(30) = 3, T(40) = 4, etc. the running time grows linearly with the input size, since if the input size doubles, the running time doubles. In logarithmic time (e.g. log 10 for simplicity), you’d expect T(10) = 1, T(100) = 2, T(1000) = 3, etc.
More in general, but I think this is a difference between big-O in theory and how it is used in practice: big-O provides an upper bound on the growth rate of a function. E.g. an algorithm that runs in linear time is also O(n^2). Of course, proving an O(n) upper bound (if possible) is more useful than proving an O(n^2) upper bound.
While I used to love doing UML and Z-Notations early in my career and in the university and I still go with UML for my personal work or for when I have to provide formal/concrete design documents for anything. But I have never seen them formally used or enforced at any of the places at work - probably because startups where you have to iterate fast. Pen and papers or whiteboards with rough sketches are mostly the way to go.
Record a video on Transport Protocols (TCP vs UDP) for my youtube channel https://youtube.com/theroadmap and continue working on the redesign of https://roadmap.sh
I love the presentation for your DevOps roadmap. Do you have any plans to add a roadmap for people who want to get into systems programming?