[jacobian.org] maintaining a transition file; against performance "seasons"
Hello, Jacob here. Thanks for subscribing to my newsletter.
Can I interview you about your personal time management / productivity system?
I'm starting to work on a new series on personal time management systems -- the tools and techniques that we use to get our work done. Not the systems that our employers have set up to help the company get work done; the practices we do, individually, to get our tasks (both employment and personal) done.
Do you have a system that works really well for you? Would you be willing to be interviewed and have me write about your system (anonymously or not)? If so, please email me (jacob@jacobian.org, or just reply to this newsletter).
I'm particularly looking for people who are neuroatypical in some way and willing to talk about how that factors into the system they have going.
My Posts
Here's what I published on my blog lately:
📝 Performance "Seasons" Are Useless — Use Anniversary Reviews Instead
Stop doing performance reviews based on the calendar year. Instead, schedule performance reviews around each person's individual calendar — a year after they join the team, switch roles, get promoted, etc. Here's how.
📝 You should maintain a transition file
When you change jobs, ideally you’ll have the opportunity to brief your successor directly. But that isn’t always possible: you might get fired or laid off, you might leave for another job without a clear successor named before your last day, you might have to take sudden medical leave, etc. Situations like that will be disruptive, it’s unavoidable, but a transition file will help minimize that disruption.
Elsewhere
🔗 Adrian Holovaty: Thoughts on my first machine learning project — there are tons of "how to ML" articles out there, but very few that touch on the whole lifecycle from inception to deployment. Even fewer describe what working with ML tools "feels" like — it's super-different from what long-time programmers are used to:
I’ve been a professional programmer for 20 years now. If I had to distill the craft into a single, fundamental sentence, it would be: Give the computer precise instructions in order to achieve a result.
For me, the biggest mind-melting thing about machine learning has been realizing it’s basically the opposite. Rather than giving the computer precise instructions, you give the computer the result and it deduces the instructions.
🔗 Darius Kazemi: A highly opinionated guide to learning about ActivityPub — I'm starting to get pretty excited about the possibilities around ActivityPub. I'm finding this guide indispensible as I learn about the spec. It's a sort of meta-guide: it doesn't explain ActivityPub, but instead tells you what to read (and in what order) to get up to speed.