Some nice tips in this.

David A. Patterson:

I started my career at Hughes Aircraft in 1972 while working on my Ph.D. at the University of California, Los Angeles (UCLA). After designing airborne computers for four years, I graduated and then taught and did systems research at UC Berkeley for the next 40. Since 2016, I’ve helped Google with hardware that accelerates artificial intelligence (AI).

At the end of my technical talks, I often share my life story and what I’ve learned from my half-century in computing. I recently was encouraged to share my reflections with a wider audience, so I’ve captured them here as 16 people-focused and career-focused life lessons.

Jeremy Morrell:

Adopting Wide Event-style instrumentation has been one of the highest-leverage changes I’ve made in my engineering career. The feedback loop on all my changes tightened and debugging systems became so much easier. Systems that were scary to work on suddenly seemed a lot more manageable.

Gina’s visualisation puts things into perspective.

Lots of talk of backing up and stripping DRM from Kindle books lately.

In my associated adventures I found this app for doing similar with your Audible library.

This new camera is getting linked all over the place. I like the minimalist industrial design.

Loads of internal storage instead of SD cards makes sense.

The feature walkthrough video is worth a watch.

YouTube has so many interviews with great photographers these days.

Kent Hendricks:

Swearing improves grip strength by 9%, wall sit time by 22%, and plank time by 12%.

This matches my experience.

The first human object launched into space wasn’t Sputnik 1. It was actually a manhole cover accidentally blown off test shaft during a nuclear test in Nevada 38 days earlier. It reached speeds equal to six times Earth’s escape velocity and was never found.

Seems appropriate.

Mark Forster:

The most distinctive feature of FV is the way that its algorithm is primarily based on psychological readiness—this then opens the way to keeping urgency and importance in the best achievable balance.

I’m a sucker for productivity systems. I like the simplicity of this one.

Cal Paterson:

A general pattern seems to be that Artificial Intelligence is used when first doing some new thing. Then, once the value of doing that thing is established, society will find a way to provide the necessary data in a machine readable format, obviating (and improving on) the AI models.

Also check out Building LLMs is probably not going be a brilliant business.

Ryan Singer:

The counterintuitive thing is, we often feel like our task is to get to a “yes.” But what we actually need is a way to say “no.” It’s the ability to eliminate many, many things that aligns us on the one thing. It’s the “no, no, no, … YES!” that gives us the power to move forward and to stick with a project.

To help us to eliminate (not forever, but for the purpose of making a decision now) I’ve found one technique very helpful. The trick is to flip things around. Instead of describing the good that will happen by doing an idea, we look at what goes wrong when we don’t do it. To make that flip, we can ask two simple questions:

1. Knowing the customer can’t do what’s in the idea today, What are they doing instead?
2. What’s bad about that?

Donella Meadows (via James Clear):

There is too much bad news to justify complacency. There is too much good news to justify despair.

The art of naming what’s happening in the room to get things back on track.

I’m not a fan of the word “learnings” either.

Life wisdom from The Marginalian.