The hottest Complex Systems Substack posts right now

And their main takeaways
Category
Top Technology Topics
Complexity Thoughts β€’ 259 implied HN points β€’ 28 Oct 24
  1. Biodiversity is important for the stability of ecosystems, as different species contribute to their health and resilience. Losing biodiversity can harm not just the species we see, but also the tiny organisms that support them.
  2. Ecosystem synchrony is a concept that helps us understand how different ecosystems respond to changes in their environment. It looks at how similar patterns in ecosystem functions can reveal important information about their health.
  3. Belief dynamics show how people's beliefs change over time, influenced by their social networks. Understanding these dynamics can help tackle issues like misinformation and social conflict.
Complexity Thoughts β€’ 139 implied HN points β€’ 11 Oct 24
  1. New ideas in network science can help understand complex systems better. This approach looks at how systems behave over time, rather than just focusing on stable points.
  2. The evolution of multicellular organisms has led to many new species and ecosystems. Key innovations in multicellularity help organisms adapt and thrive in different environments.
  3. Research shows that convolutional neural networks (CNNs) face limits in recognizing patterns. This limitation is linked to the complexity of the data they're trained on, raising questions about their reliability.
The Intrinsic Perspective β€’ 9701 implied HN points β€’ 30 Jan 25
  1. Life has ups and downs, and problems often come in clusters. It's normal to feel overwhelmed when things go wrong.
  2. When you're at a low point, remember that life is like a rollercoaster with many twists and turns. Things often improve after tough times.
  3. Statistically, when you feel at your worst, it might actually be the moment before things start to get better. Hang in there!
Gonzo ML β€’ 441 implied HN points β€’ 16 Dec 25
  1. Self-replicating programs can spontaneously emerge from random code when programs interact and rewrite each other, without hand-built ancestors or an explicit fitness function.
  2. This emergence happens across many computational substrates and spatial setups (brainfuck variants, Forth, Z80, i8080, 0D/1D/2D, long tapes), though some languages resist, so language features and locality shape how and how fast replicators appear.
  3. The system shows a clear phase transition β€” complexity and copyable tokens spike as replicators take over β€” and the resulting dynamics (competition, coexistence, niche creation) mirror ecological and origin-of-life concepts.
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The Seneca Effect β€’ 176 implied HN points β€’ 11 Feb 24
  1. The attempt to improve science by 'free-access publishing' has led to unintended consequences, like the proliferation of mediocre papers.
  2. The concentration of scientific power in a few elite institutions is not enough to drive innovation and creativity, mirroring the limitations faced by the Roman Empire.
  3. The collapse of science, exemplified by issues in scientific publishing, aligns with systemic collapses and may indicate the need for renewal through unconventional sources and unconventional ideas.
Brad DeLong's Grasping Reality β€’ 176 implied HN points β€’ 29 Jun 25
  1. Understanding complexity and emergence is crucial for grasping advanced artificial intelligence concepts. It's not just about scaling up technology but comprehending how simple rules can create complex behaviors.
  2. Human intelligence is a result of both evolution and shared knowledge as a species. We are already a network of minds working together, which influences how we create and interact with machines.
  3. The future of AI should focus on enhancing human capabilities rather than mimicking intelligence. We need to consider if we're creating true understanding or just sophisticated imitation.
Subconscious β€’ 830 implied HN points β€’ 26 Feb 24
  1. Create good problems to have after the flywheel is already spinning, during rapid growth, which motivates the ecosystem to solve problems.
  2. Avoid building perfect technology as it leads to front-loading work, needing an ecosystem flywheel, and inability to anticipate scale problems.
  3. Creating good problems to have encourages co-evolution with the community and provides opportunities for others to contribute.
The Nature of Training β€’ 19 implied HN points β€’ 16 Jun 24
  1. Feeling frustrated and disappointed from traditional sports science education led to a journey of self-discovery and obsession with Complex Systems.
  2. Transitioning from competitive cycling to coaching unlocked new patterns and aspects influencing performance, beyond traditional theory.
  3. Discovering the Science of Complexity provided a new lens to address training problems and integrate diverse passions into a coherent field.
The Jolly Contrarian β€’ 39 implied HN points β€’ 06 Nov 23
  1. Lockdown during the pandemic was a chance to test new ways of working and learn important lessons for the future of work.
  2. The discussion around the future of office work is divided between those who believe everything has changed and those who advocate for returning to the traditional office setting.
  3. Human systems and behaviors are complex, and real change in the commercial world takes time to unfold as it depends on deep-seated cultural layers and incentives.
Sunday Letters β€’ 39 implied HN points β€’ 18 Jun 23
  1. It's normal to feel overwhelmed with all the rapid changes in technology and AI. Many people are struggling to keep up, and that's okay.
  2. Using first principles can help us find clarity in confusing situations. Focusing on what's truly important and how things work can guide our understanding.
  3. Looking at data and history can help us make sense of current trends. By finding patterns and using math, we can better understand the complexities of new technologies.
Midnight Manager β€’ 19 implied HN points β€’ 12 Jul 23
  1. Predictions in the heat of the moment may not always be accurate, it's better to avoid making them.
  2. Complex systems engineering involves problem-solving, invention, and durable product creation, not immediate catastrophic failures if work stops.
  3. Building and managing internet-scale systems like Twitter is extremely challenging due to the complexity of relationships between system components.
The Jolly Contrarian β€’ 59 implied HN points β€’ 24 Jun 22
  1. The Great Fire of London in 1666 presented a unique opportunity to reimagine and optimize the city but ultimately, London was rebuilt exactly as it was, showing how persistent and resilient systems can be.
  2. Complex adaptive systems, like cities, operate on different time scales with layers such as nature, culture, governance, infrastructure, commerce, and fashion, each dependent on the layers below.
  3. Lasting change in a complex system requires either a new shock it has not yet experienced or a transformative opportunity that existing layers cannot exploit, showing the need to understand the depth at which change must occur.
The Nature of Training β€’ 1 HN point β€’ 06 Jul 24
  1. Biomimetics uses natural organisms as inspiration to create innovative technology, like airplanes inspired by bird wings and Velcro by burdock seeds.
  2. The traditional view of organisms as machines with replaceable parts is being challenged, with organisms seen as complex systems that self-organize to maintain equilibrium and adapt to their environments.
  3. Understanding an organism's purpose and stimuli is more important than focusing on its individual parts, allowing for better responses to training and health needs.
The Digital Anthropologist β€’ 19 implied HN points β€’ 16 Jan 23
  1. Human societies are currently experiencing an unprecedented number of technological revolutions driven by digital technologies.
  2. The simultaneous arrival of multiple revolutionary technologies is putting immense pressure on sociocultural and socioeconomic systems.
  3. To navigate through the current period of immense change, collaboration between different fields like anthropology, sociology, and technology is crucial.
Who is Robert Malone β€’ 14 implied HN points β€’ 04 Nov 24
  1. It's important to understand the difference between complicated systems, like computers, and complex systems, like ecosystems or human societies. Complex systems are unpredictable and can't always be controlled with precise interventions.
  2. When dealing with complex systems, sometimes it's better to wait and observe rather than rush to act. Taking a careful, incremental approach can help prevent unintended consequences.
  3. Censorship and forced social engineering can hinder our ability to adapt and learn from experiences. Encouraging free communication and decentralized thinking is crucial for innovation and growth.
The Bigger Picture β€’ 59 implied HN points β€’ 14 Feb 20
  1. We are facing a meaning crisis, losing connections to our roots and historical trajectory, which impacts our politics and the environment.
  2. Different 'memetic tribes' are constructing maps of reality, leading to polarization and closed ways of perceiving the world.
  3. To navigate the complexity, we need to embrace lost ways of knowing, balance left and right brain functions, and engage in participatory learning to understand and connect with our environment.
The Strategy Toolkit β€’ 26 implied HN points β€’ 21 Mar 23
  1. The post discusses the importance of broad thinking in physics and its application to various fields.
  2. It highlights George Parisi's work on understanding complex systems through physics.
  3. Parisi's contributions have led to breakthroughs in areas like climate modeling and stochastic resonance.
Engineering Ideas β€’ 0 implied HN points β€’ 24 Apr 23
  1. Multiple theories of cognition and value should be used simultaneously for alignment.
  2. Focus on engineering the alignment process rather than trying to solve the alignment problem with a single theory.
  3. Having diversity in approaches across AGI labs can be more beneficial than sticking to a single alignment theory.
The Grey Matter β€’ 0 implied HN points β€’ 17 Jul 23
  1. The book emphasizes that machines will never rule the world, as AGI is fundamentally impossible due to computational limitations.
  2. The definitions of intelligence and machine intelligence play a crucial role in the argument against AGI.
  3. Language, context-dependence, and complex systems are central themes analyzed in the book to challenge the possibility of AGI.