Bzogramming

Bzogramming explores the intersections of programming language design, both applied and theoretical, with elements of neuroscience, complexity theory, and technological innovation. It delves into futuristic computing paradigms, efficiency in programming tools, the physical and conceptual limitations of computing, and the potential of integrating traditional engineering and computational approaches.

Programming Language Design Technological Innovation Theoretical and Applied Computing Neuroscience and Computing Complexity Theory Quantum Computing Graphical Programming AI and Machine Learning Trends Reversible Computing Physical Constraints in Computing

The hottest Substack posts of Bzogramming

And their main takeaways

Solving Difficult Problems as a Filter

45 implied HN points • 15 Feb 25
📖 Philosophy Problem Solving Complexity Constraints Creativity Human nature
  1. Asking good questions is key to solving problems. Starting with simple, unrelated questions helps narrow down possibilities and find better answers.
  2. Simplicity is usually better, but complexity can sneak in. Adding constraints to a problem can reduce options quickly, so it's important to manage them wisely.
  3. Being too picky when looking for solutions or people can backfire. Sometimes it’s better to adapt and make things work with what's available, instead of searching for the perfect fit.

Bicycles for the Mind and Software-Augmented Mentats

30 implied HN points • 06 Jan 25
🕹 Technology Artificial Intelligence Cognitive Science Software Development Digital Tools
  1. Our minds work like software made up of various pieces that interact with each other. The way we learn, remember, and think can change based on our experiences and the information we take in.
  2. Computers can help enhance our thinking, just like a bike helps us move better. But we still have a long way to go in fully using technology to improve how we think and learn.
  3. As we learn more about how the brain works and how to interact with computers, we may discover new ways to enhance our mental abilities. This could lead to different skills and talents that we haven't seen before.

Some Speculation on Future Computational Physics

61 implied HN points • 27 Nov 24
🕹 Technology Computing Physics AI Algorithms
  1. There are two main ways to tackle physics problems: symbolic methods that involve working with symbols directly, and numerical methods that use simpler calculations. Both have their pros and cons.
  2. Quantum mechanical problems can be very tough to solve and require immense computational power, often beyond what we currently have. Even with advancements, some problems could remain very hard for a long time.
  3. As computing develops, we should explore combining the best parts of symbolic and numerical physics. We might discover new tools and methods that make it easier to solve complex problems in the future.

Undecidability Isn't So Bad

22 implied HN points • 07 Dec 24
🕹 Technology Programming Computing Algorithms Mathematics Software
  1. Some problems in computing are called undecidable, which means we can't find a definite solution for them. However, that doesn’t mean we can’t approach them creatively and get some useful results.
  2. When working with programs, understanding their behavior can often reveal hidden bugs. If a program doesn't behave the way we expect, it might be a sign that something is wrong in the code.
  3. There are smarter ways to analyze code than just throwing our hands up and saying it’s impossible. Advanced tools are already in place in many programming environments, but they often work behind the scenes without us being aware of them.

Geometric Constraints on Computing

30 implied HN points • 29 Jan 24
🕹 Technology Computing Physics Architecture Optimization Future Trends
  1. The physical constraints of computing, such as distance and volume, significantly impact performance and efficiency.
  2. Parallelism at different scales within a program can affect latency and performance, offering opportunities for optimization.
  3. Considerations like curvature of computation, square-cube law, and heat generation play a crucial role in the design and limitations of computer chips.
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Non-Newtonian Computing?

30 implied HN points • 07 Jan 24
🕹 Technology Computing Physics Cryptography Algorithms AI
  1. Physics has alternative framings like Lagrangian and Hamiltonian mechanics, which could inspire new ways of viewing computation.
  2. Reversible computing, preserving information by having bijective gates, is crucial for energy efficiency and future computing technologies.
  3. Studying constraint solvers and NP-complete problems can lead to insights for accelerating search algorithms and developing new computing approaches.

Superconductors and Such

53 implied HN points • 01 Aug 23
🕹 Technology Materials science Software Imaging Algorithms Business Models
  1. There is potential for room-temperature superconductors with simple materials like lead, phosphate, and copper.
  2. A shift back to innovation in physical technologies, like hyperspectral imaging and geometric folding algorithms, might lead to significant advancements.
  3. A reemphasis on traditional engineering fields, such as cars and rocket engines, is essential for future innovations outside of software.

A Realistic Look at Quantum Computing

22 implied HN points • 06 Mar 23
🔬 Science Quantum Computing
  1. Quantum computers face significant engineering hurdles that limit their practical applications
  2. Quantum systems have a time-reversibility property, making them a type of reversible computer
  3. Reversible computing involves creating gates with the same number of inputs as outputs, like the CNOT gate

Algorithmic Dark Matter

15 implied HN points • 13 Feb 23
🔬 Science Computation Biology Quantum Computing
  1. In computer science, there are hidden structures and algorithms that go beyond our current understanding.
  2. New paradigms of computation may hold solutions to complex problems, such as optimization and error correction.
  3. Exploring fields like quantum computing and biochemical computation could lead to groundbreaking discoveries in algorithmic tools.

Some Brief Thoughts on Recent AI Trends

15 implied HN points • 06 Feb 23
  1. AI can still benefit from traditional algorithms like Locality-Sensitive Hashing and n-grams, despite the popularity of deep learning
  2. AI-generated images often exhibit flaws resembling those produced by algorithms like Wave Function Collapse, indicating potential for traditional techniques
  3. Hybrid approaches combining traditional search algorithms with 'outdated AI' techniques could offer competitive solutions to current neural networks

The Lure of Graphical Programming

7 implied HN points • 13 Mar 23
🕹 Technology Programming Visualization Coding Tools Innovation
  1. Visual programming languages with colored boxes and lines may not necessarily make code easier to understand.
  2. Human vision focuses on categorizing small pieces of images at a time, similar to how code should be structured.
  3. Text-based programming already utilizes spatial conveyance of meaning through features like indentation, highlighting the importance of enhancing visual tools in coding.