Research shows that knowing what you want to accomplish is more important than performance at the start. But once you know where you're going, you can accelerate your progress by considering and implementing these steps.
How to learn things at 1000x the speed
Throughout my schooling and career, I’ve impressed people over and over again with how quickly I pick up things, especially hard things. For instance, exactly 4y ago I did my first ever interview, got
Reflections on software performance
At this point in my career, I’ve worked on at least three projects where performance was a defining characteristic: Livegrep, Taktician, and Sorbet (I discussed sorbet in particular last time, and livegrep in an earlier post). I’ve also done a lot of other performance work on the tools I use, some of which ended up on my other blog, Accidentally Quadratic. In this post, I want to reflect on some of the lessons I’ve learned while writing performant software, and working with rather a lot more not-so-performant software.
My Approach to Building Large Technical Projects
Whether it's building a new project from scratch, implementing a big feature, or beginning a large refactor, it can be difficult to stay motivated and complete large technical projects. A method that works really well for me is to continuously see real results and to order my work based on that.
The Busy Person's Guide to Thinking
Thinking better isn't about learning more; it's about doing a few simple things that really help create unstoppable progress.
Introducing Routines: Research that keeps working - Elicit
Tell the Elicit Research Agent what to keep current. Routines find new evidence, update your work, and report back for your review.
On genAI: Was prototyping really a bottleneck?
I keep hearing folks claim that the fact we can ‘prototype’ so quickly now is a good thing (thanks to modern genAI). But what if the slow parts about prototyping are actually what make it worth doing?
Getting Ahead By Being Inefficient
Inefficient does not mean ineffective, and it is certainly not the same as lazy. You get things done – just not in the most effective way possible. You’re a bit sloppy, and use more energy. But don’t feel bad about it. There is real value in not being the best.
Deep research requires a slower pace than tech industry work
Anyone working in an industry for a while will become accustomed to that culture—its processes, its norms, its values, its tacit knowledge. Much of this is incredibly valuable, of course, but these ideas can also represent constraints. There are some important impedances here between tech industry culture and research culture. In particular, tech culture is calibrated to a much faster pace. This can lead to impatience or early abandonment when confronting problems which require a researcher’s pace.
Deep research requires a slower pace than tech industry work
Anyone working in an industry for a while will become accustomed to that culture—its processes, its norms, its values, its tacit knowledge. Much of this is incredibly valuable, of course, but these ideas can also represent constraints. There are some important impedances here between tech industry culture and research culture. In particular, tech culture is calibrated to a much faster pace. This can lead to impatience or early abandonment when confronting problems which require a researcher’s pace.
Attention Is My Most Valuable Asset for Productivity as a Software Developer | Zachary Betz
My high-level workflow looks something like this: identify the problem to solve; think on the problem and let ideas percolate; research, discuss, and experiment with these ideas; implement and test the solution; deliver and maintain the solution.
Harness Engineering for Self-Improvement
The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965), where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the phrase “recursive self-improvement” for a specific feedback loop: an AI uses its current intelligence to improve the cognitive machinery that produces its intelligence. This feedback loop in modern AI may indicate the model rewriting its own weights directly, or more broadly the model improves the training pipeline and the deployment system, which in turn enables a better successor model with improved performance across economically valuable tasks. The speed of research development in AI has been shown to drastically accelerated in frontier labs (Anthropic; OpenAI).
Mastering Success: Navigating Within Your Circle of Competence
Understanding where you have an edge and where you don't can help you prevent problems, spot opportunities others miss, and rapidly learn. We all have a circle of competence - an area in which we have a lot of earned knowledge.
Why Tacit Knowledge is More Important Than Deliberate Practice
What tacit knowledge is, and why it is the most interesting topic in the study of expertise today.
Recent discoveries on the acquisition of the highest levels of human performance
Scientists have long debated the origins of exceptional human achievements. This literature review summarizes recent evidence from multiple domains on the acquisition of world-class performance. We review published papers and synthesize developmental ...
Why efficiency is dangerous and slowing down makes life better | Psyche Ideas
The urge to do everything faster and better is risky. Far wiser to do what’s good enough for the range of possible futures
one of the reasons why I'm critical of "bigger models will solve this" is that cranking the output velocity of technician work sharpens the need for process work, which is the domain of expertise. process is incredibly hard to get right. most fields are bad at it. and it's *gaining* importance.
Ed
when it comes to LLMs, I have been trying to beat the technician/expert drum. LLMs are becoming master technicians, but they obviously lack integrative capabilities to deploy expertise. but I'm starting to realize that a lot of people don't understand, or don't value the difference.
