When you’re on fire, problems are transparent: they’re solved simply by the act of looking at them. Even complicated layers of multiple problems can simply be glanced through like stacked panes of glass. But nobody can work that way all the time.
The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes…
Cognitive Bias Lab | Learn to Make Better Decisions
Explore cognitive biases with interactive tests, simulations, and real-world examples. Free platform to sharpen decision-making and critical thinking — no sign-up needed.
People Who Can't Visualize Anything Are Challenging a 300-Year-Old Theory of Thought
Mental imagery might not be as central to complex human thought as we believed, philosophers say.
</> htmx ~ Working With AI: A Concrete Example
In this essay, Carson Gross walks through a concrete bug fix in hyperscript to show where AI helped, where it fell short, and why keeping a knowledgeable human in the loop is what kept complexity in check.
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.
Do not give up your brain
It's tempting to just let tools think for you, but you still need to be able to think for yourself and stay sharp.
I'm becoming AI-blind - Rafal Cymerys
Recently I've been catching myself having these little moments at work, when I'm trying to read a document someone has sent me and my brain somehow refuses to analyze it. It feels like I'm reading it, but I'm unable to focus on its content. I sat down to analyze these situations and realized they all have a common denominator: the documents all show a strong trace to AI. My brain learned to quickly spot signs of AI-generated content, at least the low effort one, and it now ignores it and moves on without thinking much about it.
Reticulum Network
For the better part of a generation, we have been taught to visualize the digital world through the lens of hierarchy. The mental maps we carry are dominated by a single, misleading image: The Cloud.
Alfred Korzybski
Alfred Habdank Skarbek Korzybski was a Polish-American philosopher and independent scholar who developed a field called general semantics, which he viewed as both distinct from, and more encompassing than, the field of semantics. He argued that human knowledge of the world is limited both by the human nervous system and the languages humans have developed, and thus no one can have direct access to reality, given that the most we can know is that which is filtered through the brain's responses to reality. His best known dictum is "The map is not the territory". Many of his ideas were presented in his book Science and Sanity (1933).
Hyperproblems: New Ways of Doing and Communicating Science - Hyperproblems
Hyperproblems: Hyperproblems are scientific challenges whose scale, complexity, novelty and interdependence overwhelm traditional research models, requiring…
The peril of laziness lost | The Observation Deck
In his classic Programming Perl — affectionately known to a generation of technologists as "the Camel Book" — Larry Wall famously wrote of the three virtues of a programmer as laziness, impatience, and hubris: If we’re going to talk about good software design, we have to talk about Laziness, Impatience, and Hubris, the basis of good software design. We’ve all fallen into the trap of using cut-and-paste when we should have defined a higher-level abstraction, if only just a loop or subroutine. To be sure, some folks have gone to the opposite extreme of defining ever-growing mounds of higher level abstractions when they should have used cut-and-paste. Generally, though, most of us need to think about using more abstraction rather than less.
What Functional Programmers Get Wrong About Systems - Ian Duncan
Type systems verify properties of programs. Production correctness is a property of systems. The gap between these is where the interesting failures live.
Combinatorial Creativity
Combinatorial creativity recombines existing ideas to spark novel, valuable innovations in cognition, AI, and design.
