The hottest Computer Science Substack posts right now

And their main takeaways
Category
Top Technology Topics

The 10 Most Popular Posts of the Palindrome in 2025

The Palindrome • 1 implied HN point • 23 Dec 25
🚌 Education Computer Science
  1. The most-read posts emphasize math and foundational CS for machine learning, covering topics like a mathematics roadmap, algorithmic analysis, graph theory, and practical skills such as coding on paper and representing graphs.
  2. A holiday promotion offers a 30% lifetime discount on the annual paid subscription, which unlocks paid-only content and helps fund more math and machine learning material for the community.
  3. Subscriber-count milestones will unlock community perks (mini-courses, a dedicated Manim animator, and a full-time writer), and the publication invites feedback while planning to expand and reinvest in 2026.

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

Technology Made Simple • 39 implied HN points • 20 Apr 22
🕹 Technology Computer Science
  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 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 Computer Science
  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.

"Mechanistic interpretability" for LLMs, explained

The Counterfactual • 1 HN point • 08 Jul 24
🕹 Technology Computer Science
  1. Mechanistic interpretability helps us understand how large language models (LLMs) like ChatGPT work, breaking down their 'black box' nature. This understanding is important because we need to predict and control their behavior.
  2. Different research methods, like classifier probes and activation patching, are used to explore how components in LLMs contribute to their predictions. These techniques help researchers pinpoint which parts of the model are responsible for specific tasks.
  3. There's a growing interest in this field, as researchers believe that knowing more about LLMs can lead to safer and more effective AI systems. Understanding how they work can help prevent issues like bias and deception.
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A primer on sparse autoencoders

Nick’s Substack • 1 HN point • 03 Jul 24
🕹 Technology Computer Science
  1. Sparse autoencoders are tools that help us understand how language models work by breaking down their process into simpler parts. They help identify important features in the model that contribute to its outputs.
  2. The idea of sparsity means only a few features are needed to describe something, while superposition lets a lot of different features exist in a small space. This makes learning and processing more efficient for the model.
  3. Using sparse autoencoders opens up new ways to interact with language models. Instead of just inputting text and getting answers, we can manipulate features and explore the model's internal workings more creatively.

Newsletter #2: Certain Randomness

Decoding Coding • 19 implied HN points • 09 Feb 23
🕹 Technology Computer Science
  1. Random numbers are important in computer science for things like cryptography, simulations, and game mechanics. They help create unpredictability and realism in these applications.
  2. There are two main types of random number generators: True Random Number Generators (TRNGs) that use real-world entropy, and Pseudo Random Number Generators (PRNGs) that produce predictable outcomes based on a starting value.
  3. Algorithms like Linear Congruential Generators (LCGs) and Mersenne Twister are commonly used for generating pseudo-random numbers in various applications due to their efficiency and quality.

An Intro to Finite State Machines[Math Monday]

Technology Made Simple • 19 implied HN points • 08 Aug 22
🕹 Technology Computer Science
  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.

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

Deus In Machina • 36 implied HN points • 16 Nov 23
🕹 Technology 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.

An intro to Functions [Math Mondays]

Technology Made Simple • 19 implied HN points • 21 Jun 22
🚌 Education Computer Science
  1. Understanding functions in math helps in becoming a better programmer by teaching how to frame problems as inputs and equations.
  2. Mastering math functions translates well to coding, as both domains involve transformations on inputs to get desired outputs.
  3. To get better at functions, focus on topics like Linear Programming and Precalculus, which can sharpen problem-solving and understanding of transformations.

The interpretation translation

Infinitely More • 10 implied HN points • 07 Dec 24
🔬 Science Computer Science
  1. You can interpret one mathematical structure using another, which helps express features of the first in terms of the second. This means you find a way to connect different types of math using a common language.
  2. There are many examples of this interpretation, like placing integers inside natural numbers or examining complex numbers through real numbers. These examples show how different math concepts relate to each other.
  3. Understanding how to interpret structures can help us explore logic more deeply, opening up new ways of thinking in math, philosophy, and computer science.

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

Unstabler Ontology • 2 HN points • 27 Mar 24
🔬 Science Computer Science
  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.

Quantum computing: hype vs reality

More is Different • 7 implied HN points • 13 Jan 25
🕹 Technology Computer Science
  1. Quantum computers can do some tasks much faster than classical computers, but many claims about their abilities are exaggerated. For example, Google showcased a problem they created that doesn't have practical use.
  2. Currently, quantum computers mainly have three known useful algorithms, and none have been developed since 1996. This means their practical applications are very limited for now.
  3. Investing in quantum computing is risky because there is no clear winner among the different technologies. Many startups might fail, and it’s uncertain when quantum computers will become truly useful.

This Emptiness is Normal

Cybernetic Forests • 39 implied HN points • 14 Mar 21
🕹 Technology Computer Science
  1. Contemporary computing culture stems from a simple idea of punch cards, leading to a series of binary choices that automate decisions.
  2. Yuk Hui suggests viewing organisms as behaviors of their components and interactions, blurring the lines between organic and built systems.
  3. Hui encourages a shift from mechanistic thinking to understanding machines based on their behaviors, interactions, and integration into human lives.

How the field of "AI" got like this

Apperceptive (moved to buttondown) • 20 implied HN points • 02 Nov 23
🕹 Technology Computer Science
  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.

Multilingual Embeddings, Safer LLMs, and Log-Linear Attention

ppdispatch • 2 implied HN points • 13 Jun 25
🕹 Technology Computer Science
  1. There's a new multilingual text embedding benchmark called MMTEB that covers over 500 tasks in more than 250 languages. A smaller model surprisingly outperforms much larger ones.
  2. Saffron-1 is a new method designed to make large language models safer and more efficient, especially in resisting attacks.
  3. Harvard released a massive dataset of 242 billion tokens from public domain books, which can help in training language models more effectively.

How to explain algorithms

Notices to three friends • 3 implied HN points • 08 Mar 25
🕹 Technology Computer Science
  1. Sorting is about putting every item in a list in the right spot. There are two main ways to think about this: find the right spot for each item or find the right item for each spot.
  2. Quick-sort improves sorting by avoiding unnecessary comparisons. It achieves this by selecting a pivot and organizing elements based on their relationship to the pivot.
  3. Understanding algorithms isn't just about knowing the steps. It's important to understand the reasons behind them, as this knowledge helps you innovate or adapt the processes better.

🌻 E42: Calling the Right Agent

Musings on AI • 5 implied HN points • 19 Oct 24
🕹 Technology Computer Science
  1. Choosing the right agent is important and requires understanding the intent behind what the user asks. By clarifying these intents, we can better match them with the right tools.
  2. Frameworks like Re-Invoke and Agent Q help improve the way agents retrieve tools and make decisions. They use techniques to better understand user queries and enhance the agents' decision-making abilities.
  3. Advanced methods, such as Q-value models, enhance agent performance by guiding their actions based on expected rewards. This approach allows agents to learn from past experiences and make smarter choices in complex tasks.

Data Science Weekly - Issue 409

Data Science Weekly Newsletter • 19 implied HN points • 23 Sep 21
🕹 Technology Computer Science
  1. Trees can teach us a lot about intelligence and ecology. They inspire new ways to think about nature and our relationship with it.
  2. Before jumping into machine learning, focus on gathering quality data and building a solid framework. This can often mean starting without machine learning in your first steps.
  3. Business intelligence tools are changing and should help everyone make sense of data easily. They need to provide clear answers to data questions for all kinds of users.

CUAs Can Become APIs

The API Changelog • 3 implied HN points • 31 Jan 25
🕹 Technology Computer Science
  1. CUAs, or Computer-Using Agents, can perform tasks on computers like humans do. They are designed to help with tasks even when normal APIs are unavailable.
  2. As CUAs can act on your behalf after initial help, they can eventually work automatically. Their ability to do this raises questions about how much control we want to give them.
  3. Making CUAs available as APIs is technically simple. This opens up many questions about what tasks should be accessible and who gets to use them.

Why is it so hard to build a quantum computer?

More is Different • 3 implied HN points • 18 Jan 25
🕹 Technology Computer Science
  1. Building a quantum computer is very tricky because qubits are sensitive to their environment. If they interact with other things, they can lose their special state, making it hard to perform calculations.
  2. There are different types of quantum computers like trapped ion, superconducting, and photonic, each with its own challenges and advantages. For example, superconducting qubits need to be kept super cold, while photonic qubits work at room temperature but have their own difficulties in control.
  3. Current technology has big hurdles to overcome for scaling quantum computers up to the millions of qubits needed for practical use. Many experts think we might not easily reach such high numbers due to these challenges.

Scale Was All We Needed, At First

AI Acumen • 1 HN point • 10 Feb 24
🕹 Technology Computer Science
  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.

Data Science Weekly - Issue 363

Data Science Weekly Newsletter • 19 implied HN points • 05 Nov 20
🕹 Technology Computer Science
  1. Synthetic biology has gained a lot of attention over the past decade, and it's been evolving to deliver real technologies and breakthroughs.
  2. Data poisoning is a serious concern in machine learning, as bad data can manipulate model predictions, especially with NLP models.
  3. Managing data for machine learning projects is challenging, but using version control tools can help keep things organized and prevent unexpected issues.

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

Autodidact Obsessions • 4 implied HN points • 15 Feb 24
🕹 Technology Computer Science
  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.

Data Science Weekly - Issue 306

Data Science Weekly Newsletter • 19 implied HN points • 03 Oct 19
🕹 Technology Computer Science
  1. Data scientists are in high demand, and platforms like Vettery can help connect them with top employers. It’s a good time to create a profile and name your salary.
  2. New developments in AI are making it easier for algorithms to understand natural language and plan tasks effectively. This approach could lead to smarter AI capable of tackling unfamiliar challenges.
  3. The training process for Generative Adversarial Networks (GANs) is often tricky, but researchers are working on methods to stabilize it. This could improve how GANs are used in various applications.

Running GNU on DOS with DJGPP

Blog System/5 • 4 HN points • 14 Feb 24
🕹 Technology 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.

Data Science Weekly - Issue 264

Data Science Weekly Newsletter • 19 implied HN points • 13 Dec 18
🕹 Technology Computer Science
  1. Understanding how biological intelligence works can help us create better AI. It’s all about connecting different fields like neuroscience and psychology.
  2. Laughter in the workplace can boost team success. Measuring laughter might actually help improve innovation in projects.
  3. New methods in AI allow for training models while keeping data private. This could make using sensitive information like medical records safer.

AI as UX

Apperceptive (moved to buttondown) • 6 implied HN points • 02 Mar 23
🕹 Technology Computer Science
  1. Artificial intelligence is more of a metaphor for user interaction than just a system using machine learning.
  2. The term 'artificial intelligence' has evolved from a failed early definition to a marketing term, implying a human-like experience.
  3. User metaphors play a significant role in how people interact with computers, shaping their understanding and expectations.

Week #4 - Memory

Judson’s Substack • 5 implied HN points • 20 Jun 23
🚌 Education Computer Science
  1. In C, 'string' doesn't exist on its own, but is represented by 'char *' in the cs50.h library.
  2. Using pointers and pointer arithmetic in C helps in accessing and iterating through addresses in memory.
  3. When comparing strings in C, remember to use 'strcmp' instead of '==' to check if the contents are the same.

Memory - Week #4

Judson’s Substack • 5 implied HN points • 19 Jun 23
🕹 Technology Computer Science
  1. Computers use a system of hexadecimal values to understand numbers and characters beyond 10.
  2. Every piece of data on a computer has a specific address that can be accessed through pointers.
  3. Misusing memory or data addresses in programming can lead to bugs and code instability.

State Farming

FREST Substack • 2 HN points • 14 Jul 24
🕹 Technology Computer Science
  1. Coding can be seen as managing bits of information, or 'state', rather than just writing long programs. This means we need to handle and connect these pieces carefully to avoid complicated issues.
  2. Using coding languages that are too complex can introduce many problems like bugs and slow performance. It's better to use simpler methods when possible to make our code cleaner and easier to maintain.
  3. Relying more on databases and simpler query languages can help us streamline our coding. This way, we can focus on essential computations and reduce the amount of complex code we need to write.

RISC-V support for BFloat16

Fprox’s Substack • 3 HN points • 04 Sep 23
🕹 Technology Computer Science
  1. Brain Float 16 (BFloat16) format provides a compromise between accuracy and cost suited for machine learning applications.
  2. RISC-V is introducing support for BFloat16 format through scalar and vector extensions to improve efficiency in machine learning tasks.
  3. The new BFloat16 extensions in RISC-V have passed Architecture Review and are designed to be fully IEEE-754 compliant for numerical reproducibility.

Data Science Weekly - Issue 192

Data Science Weekly Newsletter • 19 implied HN points • 27 Jul 17
🕹 Technology Computer Science
  1. We need to consider the entire system when discussing data, not just the algorithms or models. This helps us understand the bigger picture and ask meaningful questions about how things work.
  2. There are many guidelines for figuring out if something causes another thing. It can be helpful to look at these through creative ways, like using comics to explain complex ideas.
  3. Robots are getting better at imitating humans, which can be a threat to democratic societies. It's important to stay aware of how these technologies can be misused.

Halting prophecy

Notices to three friends • 2 implied HN points • 01 Dec 23
🔬 Science Computer Science
  1. The halting problem in computer science cannot be solved because of the limitations of predicting a program's behavior.
  2. Prophecy and prediction face conceptual limitations due to the inability to fully control or predict the future.
  3. The connection between the halting problem and prophecy reveals insights about self-understanding, unpredictability, and the quest for knowledge.