The hottest Computer Science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Inside FunSearch: Google DeepMind’s LLM that Discovered New Math and Computer Science Algorithms

TheSequence • 1106 implied HN points • 18 Jan 24
🕹 Technology AI Algorithms Computer Science Mathematics Machine Learning
  1. Discovering new science is a significant challenge for AI models.
  2. Google DeepMind's FunSearch model can generate new mathematics and computer science algorithms.
  3. FunSearch uses a Language Model to create computer programs and iteratively search for solutions in the function space.

Understanding Goodhart's Law [Math Mondays]

Technology Made Simple • 179 implied HN points • 11 Mar 24
🕹 Technology Computer Science Math System Design AI Tech industry
  1. Goodhart's Law warns that when a measure becomes a target, it can lose its effectiveness as a measure.
  2. The law often unfolds due to complications in real-world systems, human adaptability, and evolutionary pressures.
  3. To address Goodhart's Law, consider using multiple metrics, tying metrics to ultimate goals, and being prepared to adapt metrics as needed.

☞ The Spreadsheet is a Simulation Machine

Cabinet of Wonders • 87 HN points • 11 Mar 24
🕹 Technology Software Digital Tools Computer Science Innovation
  1. Spreadsheets are powerful simulation machines that allow users to build little worlds, play with scenarios, and predict the future.
  2. Spreadsheets are widely used in various fields such as small businesses, hedge funds, and biology laboratories due to their power, transparency, and ease of use.
  3. The act of creating and modifying a spreadsheet is akin to world-building, where users can manipulate data, test different parameters, and see how systems respond.

Fun with Serial and Parallel Sum Reductions

CPU fun • 121 implied HN points • 22 Feb 24
🔬 Science Mathematics Computer Science Algorithms
  1. Floating point arithmetic can be more complex than expected, especially due to limited mantissa bits, affecting the accuracy of calculations.
  2. Complaining about OpenMP reductions giving 'the wrong answer' is misguided; the issue likely existed in the serial code and is now being exposed.
  3. Changing the type of the accumulator to 'double' can help resolve issues with floating point arithmetic and accuracy during sum reductions.

Tools for thought: the first 300,000 years

Subconscious • 1658 implied HN points • 10 Jun 23
🕹 Technology History Artificial Intelligence Computer Science Communication
  1. 300,000 years ago, humanity started leaving messages in rocks and clay, allowing thoughts to outlive individuals.
  2. Throughout history, humans have continuously discovered new tools for thinking, such as language, art, and technology.
  3. The shared brain of humanity has evolved over time, with increasing collaboration and technological advancements, setting the stage for thinking together to address global challenges.
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A deeper than average look into Stacks [Math Mondays]

Technology Made Simple • 99 implied HN points • 21 Nov 23
🕹 Technology Programming Data Structures Algorithms Computer Science Software Engineering
  1. Stacks are powerful data structures in software engineering and can be modified extensively to suit different use cases.
  2. Implementing Stacks using a Singly Linked List can be beneficial for dynamic resizing, though Arrays are often preferred due to memory considerations.
  3. Exploring variations like Persistent Stacks, Limiting Stack Size, Ensuring Type Safety, Thread Safety, Tracking Min/Max, and Undo Operations can enhance the functionality and efficiency of Stacks in various scenarios.

Law of Large Numbers and the Gamblers Fallacy[Math Mondays]

Technology Made Simple • 179 implied HN points • 11 Sep 23
🚌 Education Math Probability Theory Computer Science Technology
  1. The Law of Large Numbers states that as the number of trials increase, the average of results will get closer to the expected value.
  2. This law is crucial in scientific fields, allowing predictions on chaotic events, leading to industries like gambling and insurance.
  3. Misunderstanding the Law of Large Numbers can lead to the Gambler's Fallacy, as it deals with the convergence of infinitely many experiments, not individual ones.

An Introduction to DAOS- The overlooked side of AI [Math Mondays]

Technology Made Simple • 79 implied HN points • 14 Nov 23
🕹 Technology AI Computer Science Storage Software Engineering
  1. DAOS is integral to High-Performance Computing and offers cutting-edge storage capabilities for next-generation computing.
  2. There is a serious lack of talent in developing DAOS products, making it a lucrative field for skill development and career advancement.
  3. The architecture of DAOS utilizes next-generation NVM technology and operates in user space with full OS bypass, offering lightweight and high-performance storage solutions.

Pascal: The language that almost took over the world part 2

Deus In Machina • 36 implied HN points • 16 Nov 23
🕹 Technology Programming Software Languages Development Computer Science
  1. Pascal programs have a structured format with specific sections for constants, types, and variables.
  2. Free Pascal supports multiple dialects which can be specified using mode directives like OBJFPC and DELPHI.
  3. In Pascal, functions and procedures are declared with keywords like constructor, function, and procedure, and variables are prefixed with T and F.

The Cognitive-Theoretic Model of the Universe: A Partial Summary and Review

Unstabler Ontology • 2 HN points • 27 Mar 24
🔬 Science Physics Computer Science Philosophy
  1. CTMU presents the universe as a self-processing language, enabling a unique perspective on reality.
  2. The theory explores concepts like telic recursion, generalized utility maximization, and syndiffeonesis to understand the universe's organization.
  3. Key principles such as the Telic Principle suggest a link between the universe's structure and the optimization of self-selection parameters.

The Pigeonhole Principle [Math Mondays]

Technology Made Simple • 99 implied HN points • 11 Apr 23
🚌 Education Math Computer Science Software Engineering
  1. The Pigeonhole Principle states that if you have more items than containers, at least one container must hold more than one item.
  2. In software engineering, the principle ensures the correctness and efficiency of algorithms, especially in large-scale system design.
  3. The Pigeonhole Principle can also be used to prove non-existence, such as showing the impossibility of a universal lossless compression algorithm.

A neat way to catch unrelaible data [Technique Tuesdays]

Technology Made Simple • 79 implied HN points • 07 Jun 23
🕹 Technology Machine Learning Software Engineering Computer Science
  1. Feature Drift occurs when the distribution of the features being tracked changes, and it is a subset of Data Drift.
  2. Detecting Feature Drift can be tricky when tracking numerous variables, potentially leading to detrimental outcomes over time.
  3. A technique to catch Feature Drift involves creating artificial target variables based on old and new data sets, then using a simple Supervised Learning algorithm to identify drifting features.

How the field of "AI" got like this

Apperceptive (moved to buttondown) • 20 implied HN points • 02 Nov 23
🕹 Technology AI Machine Learning Computer Science Deep Learning Models
  1. The field of AI can be hostile to individuals who are not white men, which hinders progress and innovation.
  2. The history of AI showcases past failures and the subsequent shift towards more practical, engineering-focused approaches like machine learning.
  3. Success in the AI field is heavily reliant on performance advancements on known benchmarks, emphasizing practical engineering solutions.

Computer Science Applications Of Aaron Lee Axiom, Extensions & Integrations Pt 3

Autodidact Obsessions • 4 implied HN points • 15 Feb 24
🕹 Technology Computer Science Logic Frameworks Empiricism Data
  1. The author worked on the discussed problems for 30 years, gaining a deep understanding before diving into specific terminology.
  2. Understanding the jargon allowed the author to quickly progress in relating logical paradigms to philosophical problems.
  3. Nesting the conceptual framework inside pragmatic empiricism produced similar results, while nesting pragmatic empiricism within the framework expanded capabilities.

Running GNU on DOS with DJGPP

Blog System/5 • 4 HN points • 14 Feb 24
🕹 Technology Programming Software Operating Systems Development Tools Computer Science
  1. DJGPP is a port of GNU development tools to DOS, challenging the limited memory and architecture of DOS systems.
  2. DJGPP's tooling was free and provided a complete development environment with Unix heritage, leading to differences in behavior from other DOS compilers.
  3. DJGPP faced challenges like running 32-bit programs on the 16-bit DOS operating system, dealing with large buffers, and handling Unix-style paths on DOS.

6 YouTube channels for developing your problem solving skills [Technique Tuesdays]

Technology Made Simple • 79 implied HN points • 08 Feb 23
🕹 Technology Problem Solving Computer Science Skill development
  1. Study underlying concepts and practice problems to improve problem-solving skills
  2. Watching experts solve problems can help you learn and progress in your own problem-solving journey
  3. Subscribing to specific YouTube channels like Byte by Bte, Back to Back SWE, Abdul Bari, Ryan Schachte, MindYourDecisions, and TED-Ed can provide valuable insights and techniques for sharpening your problem-solving skills

Proof By Contradiction [Math Mondays]

Technology Made Simple • 39 implied HN points • 28 Feb 23
🚌 Education Math Computer Science Problem Solving Learning Techniques
  1. Proof by contradiction is a powerful proof technique where you assume the opposite to be true and derive a contradiction, leading to the original statement being true.
  2. Skills needed for Proof by Contradiction, like defining the problem statement clearly and building logical inferences, align well with problem-solving in Leetcode and software engineering.
  3. To learn Proof by Contradiction effectively, focus on topics like Sets, Probability, Theoretical Computer Science, and Graph Theory for practical application.

Best YouTube Channels to learn about Graph Theory for Software Engineering for free[Math Mondays]

Technology Made Simple • 59 implied HN points • 10 Oct 22
🚌 Education Math Coding Computer Science Theory Algorithms
  1. Focus on using a mix of channels to become an expert in Graph Theory for Software Engineering. Channels vary in their emphasis on math, coding, and computer science.
  2. Utilize the recommended channels like Wrath of Math, David Amos, Trev Tutor, and FreeCodeCamp to sharpen your understanding of Graph Theory.
  3. Engage with the content from different channels to build strong theoretical foundations and improve your performance in coding interviews.

How your computer differentiates between positive and negative numbers [Math Mondays]

Technology Made Simple • 59 implied HN points • 06 Sep 22
🔬 Science Math Computer Science Quantum Computing
  1. Understanding how computers differentiate between positive and negative numbers is crucial in programming.
  2. Computers use memory bits to store the sign of numbers and their values, applying similar techniques for storing decimals.
  3. Breaking down complex problems into simpler, solved parts is a common approach in problem solving across software engineering and mathematics.

The Last Programming Language?

Maximum Tinkering • 19 implied HN points • 02 May 23
🕹 Technology Programming Machine Learning Artificial Intelligence Software Development Computer Science
  1. Learning to program may become more accessible with the use of large language models (LLMs) that allow anyone who can read and write to code.
  2. Programming languages are gradually being abstracted to be more English-like and user-friendly, potentially leading to the development of a 'last programming language' that simplifies coding for everyone.
  3. While traditional programming languages might still have a place, new tools like LLMs could revolutionize the way people approach learning to code and building software.

Lessons from the Approximations to the Travelling Salesman Problem[Math Mondays]

Technology Made Simple • 39 implied HN points • 20 Sep 22
🕹 Technology Math Computer Science Coding Heuristics
  1. A good solution that can be deployed quickly is often better than an extremely complex optimal solution.
  2. Introducing mutations and heuristics can be beneficial in finding solutions, especially when working on heuristics instead of provably optimal solutions.
  3. Combining ideas from multiple domains can lead to innovative and valuable solutions in computer science.

Using Visualization to create better solutions [Technique Tuesdays]

Technology Made Simple • 39 implied HN points • 16 Aug 22
🕹 Technology Visualization Math Computer Science Algorithms Learning
  1. Visualization helps in learning and problem-solving by making connections and identifying patterns.
  2. When visualizing complex ideas, start small by breaking down components and building up from there.
  3. Developing visualization skills requires a strong understanding of the concepts and practicing visualization techniques regularly.

Scale Was All We Needed, At First

AI Acumen • 1 HN point • 10 Feb 24
🕹 Technology Artificial Intelligence Data security Ethics Computer Science Innovation
  1. Speculative fiction vignette explores a possible path to AGI by January 2025, highlighting the role of scale in AI advancements.
  2. The story reveals how advancements in transformers and fine-tuning algorithms led to the rapid progress in AI, ultimately culminating in the creation of a powerful AGI model.
  3. Security concerns, alignment challenges, and the potential societal impacts of powerful AI systems are portrayed, emphasizing the need for caution and preparedness in the face of advanced technology.

The cheat code to getting good at Recursive Programming[Technique Tuesday]

Technology Made Simple • 39 implied HN points • 20 Apr 22
🕹 Technology Programming Computer Science Coding Algorithm
  1. Understanding recursion is crucial for coding at top tech companies, and it's a powerful concept in Computer Science.
  2. To improve at recursive programming, practice more recursion by solving specific types of questions such as sorting, list operations, and classic recursive functions.
  3. Getting exposure to Functional Programming can significantly enhance your recursive programming skills by encouraging a purely recursive way of thinking.

Idea behind Bit shifting [Math Monday]

Technology Made Simple • 39 implied HN points • 19 Apr 22
🕹 Technology Math Algorithms Computer Science
  1. Understanding Binary Math is crucial for coding interviews. Practice is key for mastering bit shifting.
  2. Familiarity with Modular Arithmetic, Number Systems, and Recursion is important. They are foundational math skills for solving interview questions.
  3. Being able to identify when to use Mod function, transitioning between number bases, and coding recursion are critical for successful problem-solving.

Recursion- A mathematical perspective [Math Monday]

Technology Made Simple • 39 implied HN points • 12 Apr 22
🚌 Education Math Recursion Proofs Computer Science Problem Solving
  1. Mathematical Induction is a technique for proving statements by starting with a base case and progressing through inductive steps. It forms the foundation for recursion.
  2. Both Mathematical Induction and recursion rely on base cases, operate on discrete domains, and reduce problems to already proven statements. They are like mirror images of each other in problem-solving.
  3. Understanding Mathematical Induction can greatly improve recursion skills as they share similar problem-solving approaches. Practicing PMI questions can enhance recursion proficiency.

An Intro to Finite State Machines[Math Monday]

Technology Made Simple • 19 implied HN points • 08 Aug 22
🕹 Technology Programming Software Engineering Computer Science Tutoring Algorithms
  1. Finite State Machines (FSMs) are like directed graphs that help in understanding the flow of a program. Nodes represent states and edges show reachable states.
  2. FSMs are useful for filtering input based on rules and when a system is defined by a set of conditions, like in Regex applications.
  3. Mastering FSMs involves patience, practice, and hands-on coding of theoretical concepts to understand and implement them effectively.