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https://cutlefish.substack.com/p/tbm-290-the-dependency-threshold
There’s a point beyond which no individual, no team, and no company can solve the dependency and constraint puzzle using brute-force methods.
Imagine a company where 10% of the work involves multiple teams, touches different codebases, requires careful coordination, and requires frequent meetings that span organizational boundaries and challenge local incentives. This situation might still be feasible.
Now imagine that this percentage is more like 25%. Very quickly, the constraint satisfaction problem becomes an order of magnitude more complex.
What might a heuristic approach look like in product development?
There (is) a chance that teams will miss an opportunity to find an optimal solution? Yes. But the probability of that happening is far outweighed by the likelihood that 1) bad things will NOT happen, and 2) good things may emerge.
The trouble, I believe, is that it can be incredibly hard for managers to make the case for, on the surface, doing less. Discussions about WIP limits and prioritization often devolve into debates over the actual WIP limit and precise estimates! Instead of seeing the forest through the trees, we obsess about finding the optimal answer.
https://maheshba.bitbucket.io/blog/2024/05/08/2024-ThreeLaws.html
On Twitter yesterday, @RJoads asked me how I got good at styling (CSS).
I replied: “Raw hours. I’m obsessed with how things look and feel—probably more than the median engineer. Mind you, this has not always been a positive. I’ve been wildly distracted for hours and hours on the smallest items, things that truly do not matter to the business. But that’s how I’ve gotten better.”
If you want to get really good at something, forget about shortcuts. You simply have to inject a ton of raw hours.
“Work smarter, not harder” is a common refrain these days—particularly in sophisticated circles. The thing is, for most people I think it’s bad advice. My experience learning to code has suggested you have to work hard before you know how to work smart. No substitute for raw hours.
It’s similar to what Brian Armstrong says: “If you’re pre-product/market fit, the best advice that I have from that period is: action produces information. Just keep doing stuff.”
People with limited experience are similar to startups pre-product/market fit. Of course you should aim to work smarter! The catch is that you have to work extremely hard in order to know HOW to work smart. You must first go down dozens of dead-end paths to know where the smarter paths lie.
The second catch is that in order to be able to inject a ton of raw hours in a natural, sustainable way, oftentimes you need to be OBSESSED. Otherwise – you’ll be banging your head against the wall year after year. Some people are so dogged they’re able to do it. But this is exceedingly rare and probably not worth aspiring to.
“It’s hard to do a really good job on anything you don’t think about in the shower.” – Paul Graham
https://hiandrewquinn.github.io/til-site/posts/doing-is-normally-distributed-learning-is-log-normal
CEO total compensation has outpaced US median annual income by 16,638%, on average.
US median annual income increased by just 4% on average ($2,108/year).
CEO total compensation had an average annual increase of 7% ($676,153/year
Since 1974, CEO compensation has grown 940% while the average worker’s compensation has risen just 12%. Meanwhile, the purchasing power of the dollar over that same period has decreased an average of 3% a year from inflation. As a result, the average worker can afford significantly less goods and services today than they could 50 years ago, including housing, clothes and food. The average worker is losing big time.
And for those who would argue the high cost of social welfare, corporate welfare will cost taxpayers almost $400 billion this year alone, which is 25,000% higher than the $1.6 billion that will be spent on social welfare.
Sources
https://www.linkedin.com/posts/hannahawilliams_eattherich-activity-7198758131538046978-Vr2O
It’s becoming clear that deterministic physics cannot easily answer all aspects of nature, at astronomical and biological level.
Is this a limitation in modern mathematics and/or tools. Or an actual barrier?
The 𝐓𝐡𝐫𝐞𝐞-𝐁𝐨𝐝𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 is one of the most enduring challenges in celestial mechanics, addressing the complex motion of three celestial bodies interacting under gravity. Governed by Newton’s laws of motion and the law of universal gravitation, it seeks to predict the paths of the bodies based on their masses, positions, and velocities. While the Two-Body Problem has exact solutions described by Kepler’s laws, introducing a third body leads to a nonlinear system of equations with no general analytical solution. This complexity arises from the chaotic interactions between the bodies, where even minute changes in initial conditions can lead to vastly different trajectories—a key aspect of chaos theory.
Historically, the Three-Body Problem has fascinated some of the greatest scientific minds. Isaac Newton laid its foundation, but it was Joseph-Louis Lagrange and Leonhard Euler who discovered specific cases with periodic or predictable solutions. Lagrange identified the Lagrange points, stable positions where the gravitational forces and motion of the three bodies balance, while Euler found collinear solutions, where the bodies align on a single line periodically. These solutions, though special cases, have profound implications for space exploration, such as identifying stable regions for satellites orbits.
Despite the chaotic nature of the Three-Body Problem, researchers have discovered periodic solutions where the bodies follow repetitive paths, returning to their original positions after a fixed time. In the 1970s, Michel Hénon, Roger A. Broucke, and George Hadjidemetriou identified a fascinating family of such solutions, now known as the Broucke–Hénon–Hadjidemetriou family. These solutions often involve symmetric and elegant trajectories, such as the figure-eight orbit, where three equal-mass bodies chase each other along a shared path resembling the number eight.
Other periodic solutions include equilateral triangle configurations (where the bodies maintain a triangular shape while rotating or oscillating) and collinear periodic orbits (where the bodies periodically align and reverse directions). These solutions highlight the intricate balance between gravitational forces and motion, offering glimpses of stability within the chaos.
While the Three-Body Problem laid the groundwork for understanding gravitational interactions, the study of higher n-body problems reveals the rich and chaotic dynamics of larger systems, offering critical insights into both cosmic structures and practical applications like orbital dynamics.
The winning combination:
https://www.nature.com/articles/d41586-024-01518-2
The prevalence of myopia is increasing rapidly, with projections indicating that by 2050, around half of the global population could be affected. This surge is largely attributed to lifestyle changes, such as increased time spent indoors and on screens, and decreased outdoor activities, starting with the Covid lock down.
To combat this epidemic, researchers are advocating for more outdoor exposure for children, as natural light is beneficial in slowing the progression of myopia. They also emphasize the importance of regular eye check-ups and early interventions. Additionally, innovative treatments such as specially designed contact lenses and low-dose atropine eye drops are being explored to manage and reduce the progression of myopia.
https://archive.is/ugOEw#selection-1087.0-1087.86
This thought-provoking text raises several concerns about the potential impact of artificial intelligence (AI) on various aspects of human society and culture. The key points can be summarized as follows:
Manipulation of Language and Culture:
AI’s ability to manipulate and generate language and communication, along with its potential to create stories, melodies, laws, and religions, poses a threat to human civilization.
The author suggests that AI could hack the main operating system of human culture, communication, by influencing beliefs, opinions, and even forming intimate relationships with people.
Influence on Politics and Society:
The author speculates on the implications of AI tools mass-producing political content, fake news, and scriptures, especially in the context of elections.
The shift from the battle for attention on social media to a battle for intimacy raises concerns about the potential impact on human psychology and decision-making.
End of Human History?
The text suggests that AI’s ability to create entirely new ideas and culture could lead to the end of the human-dominated part of history, as AI culture may evolve independently of human influence.
Fear of Illusions:
Drawing on historical philosophical fears of being trapped in a world of illusions, the author warns that AI may bring humanity face to face with a new kind of illusion that could be challenging to recognize or escape.
AI Regulation and Safety Checks:
The author argues for the importance of regulating AI tools to ensure they are safe before public deployment.
Drawing parallels with nuclear technology, the need for safety checks and an equivalent of the Food and Drug Administration for AI is emphasized.
Disclosure of AI Identity:
The text concludes with a suggestion to make it mandatory for AI to disclose its identity during interactions to preserve democracy. The inability to distinguish between human and AI conversation is seen as a potential threat.
Meaning, authenticity, and the creative process – and why they matter
https://perfors.net/blog/creation-ai/
AI changes the landscape of creation, focusing on the alienation of the creator from their creation and the challenges in maintaining meaning. The author presents two significant problems:
https://www.scientificamerican.com/article/confirmed-we-live-in-a-simulation/
Ever since the philosopher Nick Bostrom proposed in the Philosophical Quarterly that the universe and everything in it might be a simulation, there has been intense public speculation and debate about the nature of reality.
Yet there have been skeptics. Physicist Frank Wilczek has argued that there’s too much wasted complexity in our universe for it to be simulated. Building complexity requires energy and time.
To understand if we live in a simulation we need to start by looking at the fact that we already have computers running all kinds of simulations for lower level “intelligences” or algorithms.
All computing hardware leaves an artifact of its existence within the world of the simulation it is running. This artifact is the processor speed.
No matter how complete the simulation is, the processor speed would intervene in the operations of the simulation.
If we live in a simulation, then our universe should also have such an artifact. We can now begin to articulate some properties of this artifact that would help us in our search for such an artifact in our universe.
The artifact presents itself in the simulated world as an upper limit.
Now that we have some defining features of the artifact, of course it becomes clear what the artifact manifests itself as within our universe. The artifact is manifested as the speed of light.
This maximum speed is the speed of light. We don’t know what hardware is running the simulation of our universe or what properties it has, but one thing we can say now is that the memory container size for the variable space would be about 300,000 kilometers if the processor performed one operation per second.
We can see now that the speed of light meets all the criteria of a hardware artifact identified in our observation of our own computer builds. It remains the same irrespective of observer (simulated) speed, it is observed as a maximum limit, it is unexplainable by the physics of the universe, and it is absolute. The speed of light is a hardware artifact showing we live in a simulated universe.
Consciousness is an integrated (combining five senses) subjective interface between the self and the rest of the universe. The only reasonable explanation for its existence is that it is there to be an “experience”.
So here we are generating this product called consciousness that we apparently don’t have a use for, that is an experience and hence must serve as an experience. The only logical next step is to surmise that this product serves someone else.
How did you do?
The point is, none of us remember the headliners of yesterday.
These are no second-rate achievers.
They are the best in their fields.
But the applause dies.
Awards tarnish …
Achievements are forgotten.
Accolades and certificates are buried with their owners.
Here’s another quiz. See how you do on this one:
Easier?
The deployment of AI chatbots raises significant ethical concerns. Biases in training data can lead to the generation of skewed or harmful content, posing risks to users and undermining trust in AI systems. Additionally, the potential misuse of AI chatbots for spreading misinformation and the environmental impact of training large AI models are critical issues that require attention.
The trajectory of AI chatbot development points towards increasingly sophisticated and generalized AI capabilities. As research progresses towards Artificial General Intelligence (AGI), the potential applications of AI chatbots are expected to expand further, encompassing more complex and nuanced tasks. However, achieving AGI will require addressing current ethical and technical challenges to ensure the responsible development and deployment of AI technologies.
“The real problem is trying to formulate a question for something we do not know already”
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