The greatest trick we ever pulled on ourselves as knowledge workers was convincing ourselves we could juggle multiple projects with no consequences.
Stop Begging Billionaires To Fix Software — Build Your Own
This is part one of a two-part series on using AI tools as one piece to fight back against tech company control of our lives. Part two shares the details of how I built my own task management tool …
Avoiding Digital Productivity Traps - Cal Newport
Last week in this newsletter, I summarized some interesting results from a study that analyzed the behavior of 164,000 knowledge workers. It found that introducing ... Read more
Don't Get Distracted
I’m going to tell you about how I took a job building software to kill people. But don’t get distracted by that; I didn’t know at the time.
How I Built A Task Management Tool For Almost Nothing
This is part two of my series on using AI tools to fight back against tech company control. Part one explained why we can get beyond just begging billionaires to fix our tools. This part shows exac…
I automated my job (and it made me a better leader)
Explore how my day as a senior leader looks now that I use 40 automations to help, and learn more about some of my favorites.
Why Is Everyone In Tech So Sad?
A lot of people seem to be realizing that knowledge work is mostly pointless. AI might give us the pleasure of finding out what happens if an entire class of workers loses faith in their careers.
Harnessing Frustration: Using LLMs to Overcome Activation Energy
One of my biggest weaknesses as a software engineer is procrastination when facing a new project. When the scope is unclear, I have a tendency to wait until I feel I’ve “felt out” the problem to start doing anything. I know I’ll feel better and work much faster when I get “stuck in” but I still struggle with that first step, overcoming the “activation energy” required to engage with the details. LLMs have been a game-changer for me in this respect: I can just throw a couple of sentences at them with the shape of the problem. This leads to one of two outcomes: The LLM comes up with a good solution, usually in a slightly different way than what I was thinking. I realize “oh wow the solution is much simpler than I thought”. Straight away I start thinking about the consequences of implementing and improving what the LLM suggested. The LLM comes up with a solution that I intuitively recognize as “wrong”. My immediate reaction is frustration (“How could it get it so wrong”) which leads me to go back and forth with the model, explaining to it why its solution could not possibly work. But in the process of arguing with the model, my brain is churning away and generating variations or different approaches that could work. After a while, even if the AI is still on the wrong track, the debate will trigger a moment of inspiration where suddenly the solution will come to me. I’ll excitedly start up a new conversation and start working through it with the model. The key is the emotional reaction I have immediately to the LLM’s response, either excitement or frustration. By harnessing this immediate feedback loop, I get my brain out of its passive, procrastination mode. It’s almost like a jolt: either I’m thrilled because it’s simpler than I thought, or I’m spurred to action by the urge to correct a perceived ‘wrong’ answer. This forces me to engage with the problem in a meaningful way.
‘AI gravity’ is pulling you toward dependency. Here’s how to push back | MIT Sloan
AI systems hold the promise of competitive advantage, but they can usher in cognitive decline among workers, says MIT Sloan School of Management’s Eric So. Learn how to protect cognitive capital.
‘AI gravity’ is pulling you toward dependency. Here’s how to push back | MIT Sloan
AI systems hold the promise of competitive advantage, but they can usher in cognitive decline among workers, says MIT Sloan School of Management’s Eric So. Learn how to protect cognitive capital.
I built a custom Slack inbox. It was easier than you think. | Yash Tekriwal (Clay)
Double Click: You Can Just Do Things—But Should You Always? | Figma Blog
The catchphrase is catching fire as early adopters ride the new wave of AI-powered tools. Here’s how we might channel that energy and avoid the risk of being overwhelmed.
Changing Minds: Computers, Learning, and Literacy
An impassioned guide to how computers can fundamentally change how we learn and think.Andrea diSessa's career as a scholar, technologist, and teacher has b
"I've got an idea!" On pluralism and resisting deskilling
A ramble around plural knowledges, data and AI, inspired by the wisdom from friends and colleagues in Nairobi and watching The Pitt. Plus! a random picture of an elephant
Artificial intelligence, cognitive offloading and implications for education
This report investigates the challenge driven by the rapidly expanding use of artificial intelligence (AI) in schooling: the risk that students will outsource too much of the cognitive work that is crucial to establishing knowledge, skill and ‘thinking infrastructure’. The report includes specific recommendations for policy and teaching and learning strategies.
The Agents Are Waking Up
The Intelligence Revolution that swept through the software industry this past winter is coming to knowledge-work next.
This entire grift relies on convincing people that they don't know how to do the things they have always known how to do, and ironically, if it works, we will, in a very short amount of time, forget how to do all the things we have always known how to do.
More Perfect Union
Jimmy Fallon: "And do you use ChatGPT when raising your baby?" Sam Altman: "I cannot imagine figuring out how to raise a newborn without ChatGPT."
