The hottest Algorithms Substack posts right now

And their main takeaways
Category
Top Technology Topics

Meta: 5,000

lcamtuf’s thing • 8366 implied HN points • 27 Feb 25
🕹 Technology Programming Algorithms Mathematics Writing
  1. Reaching 5,000 subscribers is a big deal for a project that went against the usual trends. It's great to see growth, even if it seems small compared to others.
  2. Writing a newsletter is unique because you don't get much direct feedback from readers. It's interesting to see who signs up or leaves but hard to know what they really think.
  3. Three articles worth revisiting cover complex topics: discrete Fourier transforms, fractals, and core concepts in electronic circuits. They offer in-depth discussions that are easy to understand, even for beginners.

All paths point downhill

arg min • 218 implied HN points • 31 Oct 24
🕹 Technology Algorithms Optimization Data science Machine Learning Mathematics
  1. In optimization, there are three main approaches: local search, global optimization, and a method that combines both. They all aim to find the best solution to minimize a function.
  2. Gradient descent is a popular method in optimization that works like local search, by following the path of steepest descent to improve the solution. It can also be viewed as a way to solve equations or approximate values.
  3. Newton's method, another optimization technique, is efficient because it converges quickly but requires more computation. Like gradient descent, it can be interpreted in various ways, emphasizing the interconnectedness of optimization strategies.

Basic Linear Algebra Subprogramming

arg min • 178 implied HN points • 29 Oct 24
🕹 Technology Computing Mathematics Optimization Data science Algorithms
  1. Understanding how optimization solvers work can save time and improve efficiency. Knowing a bit about the tools helps you avoid mistakes and make smarter choices.
  2. Nonlinear equations are harder to solve than linear ones, and methods like Newton's help us get approximate solutions. Iteratively solving these systems is key to finding optimal results in optimization problems.
  3. The speed and efficiency of solving linear systems can greatly affect computational performance. Organizing your model in a smart way can lead to significant time savings during optimization.

When Algorithms Come to Your Neighborhood

The Honest Broker • 7513 implied HN points • 17 Jan 25
🕹 Technology Social media Algorithms Online Platforms User Experience
  1. Nextdoor can be useful for getting local alerts, especially in emergencies. However, it might not always provide timely information when you need it.
  2. Many users ignore alerts from apps like Nextdoor because they often send old or irrelevant notifications. This can create a false sense of security and put people at risk.
  3. It's important to question whether the information we receive from neighborhood platforms is reliable. If we learn to overlook their messages, we could miss crucial updates.

How Unix Spell Ran in 64kB RAM

Confessions of a Code Addict • 1683 implied HN points • 12 Jan 25
🕹 Technology Software Engineering Computer Science Data Structures Algorithms Information Theory
  1. Unix engineers faced a big challenge in fitting a large dictionary into just 64kB of RAM. They came up with clever ways to compress the data and use efficient structures to make everything fit.
  2. A key part of their solution was the Bloom filter, which helped quickly check if words were in the dictionary without needing to look up every single word, saving time.
  3. They also used innovative coding methods to further reduce the size of the data needed for the dictionary, allowing for fast lookups while staying within the strict memory limits of their hardware.
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Algorithmic ranking is unfairly maligned

DYNOMIGHT INTERNET NEWSLETTER • 1156 implied HN points • 23 Jan 25
🕹 Technology Algorithms Social media Digital Culture User Experience Data Ethics
  1. Not all algorithmic ranking is bad. Some algorithms can be useful if they align with what you want to see and achieve.
  2. A lot of current algorithms are designed to keep you engaged and make money for the companies, not necessarily to help you find what you like.
  3. We need better control over these algorithms to ensure they serve our interests, possibly through new technology or structures that prevent companies from taking that control away.

Convex Optimization at the Midpoint

arg min • 198 implied HN points • 17 Oct 24
🚌 Education Optimization Programming Statistics Algorithms
  1. Modeling is really important in optimization classes. It's better to teach students how to set up real problems instead of just focusing on abstract theories.
  2. Introducing programming assignments earlier can help students understand optimization better. Using tools like cvxpy can make solving problems easier without needing to know all the underlying algorithms.
  3. Convex optimization is heavily used in statistics, but there's not much focus on control systems. Adding a section on control applications could help connect optimization with current interests in machine learning.

Algowhoring; A Primer

Polymathic Being • 114 implied HN points • 16 Feb 25
🕹 Technology Social media Algorithms Content creation Digital Trends
  1. Algowhoring is when people create content just to get attention on social media, often copying what works instead of sharing their own original ideas. This can hurt the quality of communication online.
  2. These posts usually focus on getting quick likes and shares, which can feel shallow or scammy. Even though they might get lots of engagement, they don’t contribute anything meaningful.
  3. To improve social media, it's important to ignore algowhoring posts, reward genuine content, and avoid using those attention-seeking tactics yourself. This way, we can encourage a better online environment.

Reinforcement, evolution, and automata

Gradient Ascendant • 7 implied HN points • 26 Feb 25
🕹 Technology AI Machine Learning Computing Algorithms Automation
  1. Reinforcement learning is becoming important again, helping improve AI models by using trial and error. This allows models to make better decisions based on past experiences.
  2. AI improvements are not just for big systems but can also work on smaller models, even those that run on phones. This shows that smarter AI can be more accessible.
  3. Combining reinforcement learning with evolutionary strategies could create more advanced AI systems in the future, leading to exciting developments and solutions.

Inverse frontiers

arg min • 158 implied HN points • 07 Oct 24
🕹 Technology Optimization Machine Learning Algorithms Data science Artificial Intelligence
  1. Convex optimization has benefits, like collecting various modeling tools and always finding a reliable solution. However, not every problem fits neatly into a convex framework.
  2. Some complex problems, like dictionary learning and nonlinear models, often require nonconvex optimization, which can be tricky to handle but might be necessary for accurate results.
  3. Using machine learning methods can help solve inverse problems because they can learn the mapping from measurements to states, making it easier to compute solutions later, though training the model initially can take a lot of time.

How to Regulate Social Media

Erik Explores • 61 implied HN points • 09 Feb 25
🕹 Technology Social media Algorithms Moderation Regulation Ethics
  1. Social media algorithms often promote extreme or divisive content to keep users engaged, which is harmful. Creating ethics boards to oversee these algorithms could help focus on more positive and informative content instead.
  2. Moderation of social media content does not always balance free speech with the need to prevent harmful misinformation. It's important to have clear processes for content removal and to empower users in the moderation process.
  3. Users need better tools to evaluate and discuss opinions without just liking or disliking them. A system that rewards thoughtful, respectful discussions can shape healthier online interactions.

Algorithms are making political speech more extreme

Taylor Lorenz's Newsletter • 3582 implied HN points • 09 Nov 24
🇺🇸 U.S. Politics Political speech Social media Algorithms Communication Misinformation
  1. Algorithms are changing how politicians speak. They now exaggerate and hyperbolize to get more likes and shares, which can lead to more extreme views.
  2. Social media has replaced traditional broadcasting, making it harder for politicians to reach their audience directly. Now, they must adapt their messages for platforms that promote viral content.
  3. Facial recognition technology is increasingly used by governments to track and suppress protesters. This makes it riskier for people to express dissent, as they can be easily identified and punished.

The Sequence Research #548: Why I Can't Stop Thinking About AlphaEvolve

TheSequence • 56 implied HN points • 23 May 25
🕹 Technology AI Software Research Innovation Algorithms
  1. AlphaEvolve is a new tool that uses AI to create and improve algorithms, which could be a big step toward achieving artificial general intelligence (AGI).
  2. It combines evolutionary methods with large language models, allowing it to discover and refine algorithms more efficiently.
  3. AlphaEvolve not only makes significant math discoveries but also helps improve Google's technology operations.

The CAP Theorem of Clustering: Why Every Algorithm Must Sacrifice Something

Confessions of a Code Addict • 529 implied HN points • 29 Oct 24
🕹 Technology Algorithms Data science Software Engineering Mathematics Research
  1. Clustering algorithms can never be perfect and always require trade-offs. You can't have everything, so you have to choose what matters most for your project.
  2. There are three key properties that clustering should ideally have: scale-invariance, richness, and consistency, but no algorithm can achieve all three simultaneously.
  3. Understanding these sacrifices helps in making better decisions when using clustering methods. Knowing what to prioritize can lead to more effective data analysis.

Diffusion Models are Evolutionary Algorithms

Gonzo ML • 441 implied HN points • 09 Nov 24
🕹 Technology AI Algorithms Evolution Machine Learning Data science
  1. Diffusion models and evolutionary algorithms both involve changing data over time through processes like selection and mutation, which can lead to new and improved results.
  2. The new algorithm called Diffusion Evolution can find multiple good solutions at once, unlike traditional methods that often focus on one single best solution.
  3. There are exciting connections between learning and evolution, hinting that they may fundamentally operate in similar ways, which opens up many questions about future AI developments.

Faster NTT with RISC-V Vector

Fprox’s Substack • 62 implied HN points • 11 Jan 25
🕹 Technology Software Programming Cryptography Hardware Algorithms
  1. The Number Theoretic Transform (NTT) can speed up polynomial multiplications, which are important for modern cryptography. Optimizing how this process works leads to significant performance improvements.
  2. Using assembly language can help tailor code for specific hardware, allowing more direct control over how instructions are executed, which can greatly enhance speed.
  3. Combining multiple steps of the NTT process into fewer loops and minimizing unnecessary calculations can lead to much lower execution times, achieving targets that seemed difficult at first.

From Theory to Practice: Inductive Biases in Machine Learning

Mindful Modeler • 639 implied HN points • 23 Apr 24
🕹 Technology Machine Learning Algorithms Data Bias Modeling
  1. Different machine learning models exhibit varying behaviors when extrapolating features, influenced by their inductive biases.
  2. Inductive biases in machine learning influence the learning algorithm's direction, excluding certain functions or preferring specific forms.
  3. Understanding inductive biases can lead to more creative and data-friendly modeling practices in machine learning.

The mathematics of the game Waffle

A Piece of the Pi: mathematics explained • 48 implied HN points • 22 Jan 25
🔬 Science Mathematics Game Theory Algorithms
  1. Waffle is a fun word game where you need to form six five-letter words in a grid. You can swap letters to find the right words based on clues given.
  2. To solve Waffle, you must figure out the words first, then how to rearrange the letters, and finally do it using the least number of swaps.
  3. Group theory is useful for solving Waffle puzzles because it helps to find ways to rearrange the letters efficiently, especially when dealing with repeated letters.

Scalable Embedding based retrieval for target side value

Recommender systems • 23 implied HN points • 17 May 25
🕹 Technology Machine Learning Data science Algorithms Software Development Social Networks
  1. Scalability is key for embedding-based recommendation systems, especially when dealing with billions of users. Finding effective ways to limit the search can help manage this challenge.
  2. It’s important to deliver value not just to viewers but also to the recommended targets, as this can improve user retention. Balancing recommendations for both sides can create a better experience.
  3. Using advanced algorithms can help ensure viewers don’t get overwhelmed with too many recommendations while also making sure that every target gets the attention they need. This balance is crucial for effective recommendations.

Daylight swiping

Vague Blue • 778 implied HN points • 19 Mar 24
🕹 Technology Mobile Apps User Interaction Gaming Algorithms
  1. The evolution of the swipe gesture, popularized by Apple, has changed how we interact with technology, from unlocking phones to scrolling through social media.
  2. The swipe has become ingrained in modern culture, especially through dating apps like Tinder, where it serves as a rapid filter for potential matches.
  3. Continuous swiping on apps can create a sense of infinite possibilities but can also lead to mindless behavior, trapping users in a cycle of seeking without finding.

Inductive biases of the Random Forest and their consequences

Mindful Modeler • 379 implied HN points • 21 May 24
🕹 Technology Machine Learning Algorithms
  1. Machine learning models like Random Forest have inductive biases that impact interpretability, robustness, and extrapolation.
  2. Random Forest's inductive biases come from decision tree learning algorithms, random factors like bootstrapping and column sampling, and ensembling of trees.
  3. Some specific inductive biases of Random Forest include restrictions to step functions, preference for deep interactions, reliance on features with many unique values, and the effect of column sampling on feature importance and model robustness.

Folk search engines

escape the algorithm • 878 implied HN points • 20 Feb 24
🕹 Technology Search Engines Algorithms Social media
  1. Consider using small, alternative search engines for more unique and diverse results.
  2. Explore unconventional search methods, even on mainstream search engines like Google, to find less algorithm-optimized content.
  3. Utilize platforms like Reddit, Facebook Groups, and Discord for searching as they offer distinct content and avoid heavy SEO tactics.

Why TikTok ‘Knows You’: The Data Trick That Makes It Tick

The Daily Bud • 12 implied HN points • 25 Jan 25
🕹 Technology Social media Algorithms Data Privacy User Experience Machine Learning
  1. TikTok's algorithm is really good at guessing what you want to watch next. It keeps improving by watching how you interact with videos.
  2. Unlike other apps, TikTok avoids mixing user data, which helps keep recommendations super personal. This means you get content that's more tailored just for you.
  3. The way TikTok designs its data storage prevents recommendations from getting mixed up. This leads to a cleaner and more enjoyable experience while using the app.

We Already Solved Subscription Fatigue

Default Wisdom • 159 implied HN points • 19 Nov 24
🕹 Technology Social media Subscriptions Algorithms Content creation Digital economy
  1. Subscription models on social media can actually improve the user experience. They may create a better environment by encouraging more intentional use rather than endless scrolling.
  2. The problem isn’t subscriptions themselves, but the overwhelming number of individual subscriptions to small creators. Bundled options could make things simpler for users.
  3. Many people feel overwhelmed by how much they pay for subscriptions online. By making users think harder about what they subscribe to, it might lead to more careful choices.

Quick Note: swapping pairs with RVV

Fprox’s Substack • 62 implied HN points • 25 Dec 24
🕹 Technology Programming Algorithms Computer Science Software Development Cryptography
  1. There are two main techniques for swapping pairs of elements using RISC-V Vector: one uses slidedown and slideup operations, and the other uses narrowing and widening arithmetic. Each has its own method for rearranging elements.
  2. The slidedown and slideup technique tends to be faster because it uses fewer operations and avoids extra complexity, making it more efficient for swapping elements in practice.
  3. In testing, the slidedown method consistently showed lower latency in tasks compared to the widening approach, indicating it might be the better choice for optimizing performance in applications like NTT implementations.

HashMap is a Data Structure every SWE should know.

System Design Classroom • 239 implied HN points • 24 May 24
🕹 Technology Software Data Structures Algorithms Computer Science Development
  1. Hashmaps are useful for storing data by connecting unique keys to their values, making it easy to find and retrieve information quickly.
  2. When two different keys accidentally produce the same hash code, it's called a collision. There are ways to handle this, like chaining and open addressing.
  3. Hashmaps can do lookups, insertions, and deletions really fast, usually in constant time, but they can slow down if too many items cause collisions.

Number Theoretic Transform with RVV

Fprox’s Substack • 83 implied HN points • 07 Dec 24
🕹 Technology Algorithms Cryptography Computing Software Development Mathematics
  1. The Number Theoretic Transform (NTT) helps speed up polynomial multiplication, which is important in cryptography. It uses a smart method to do complicated calculations faster than traditional methods.
  2. Using RISC-V Vector (RVV) technology can further improve the speed of NTT operations. This means that by using special hardware instructions, operations can be completed much quicker.
  3. Benchmarks show that a well-optimized NTT using RVV can be substantially faster than basic polynomial multiplication, making it crucial for applications in secure communications.

Your Attention Has Been STOLEN | Peter Boghossian & Johann Hari

Peter Boghossian • 609 implied HN points • 30 Jan 24
🕹 Technology Social media Algorithms Interpersonal Relationships Democracy
  1. Johann Hari and Peter Boghossian discuss the impact of technology addiction on attention spans and society.
  2. They emphasize the role of social media in capturing attention and keeping users engaged through algorithms.
  3. The shortened attention spans affect relationships, political engagement, and democracy.

⬜ Beyond the search engine

One Thing • 573 implied HN points • 01 Feb 24
🕹 Technology Search Engines Algorithms Internet Social media Data Privacy
  1. Utilize small, alternative search engines that offer unique approaches not influenced by market trends
  2. Consider using unconventional methods when searching, such as leveraging platforms like Reddit for information
  3. Prioritize authentic search experiences, focusing on genuine connections and unique discoveries rather than catering solely to algorithms

Some Speculation on Future Computational Physics

Bzogramming • 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.

The Sliding Window Technique[Technique Tuesdays]

Technology Made Simple • 279 implied HN points • 28 Feb 24
🕹 Technology Algorithms Problem Solving Software Engineering Artificial Intelligence Data Structures
  1. The sliding window technique is a powerful algorithmic model used for problem-solving in coding interviews and software engineering, offering efficiency and practicality.
  2. Benefits of using the sliding window technique include reducing duplicate work, maintaining consistent linear time complexity, and its utility in AI feature extraction processes.
  3. Spotting the sliding window technique involves identifying keywords like maximum, minimum, longest, or shortest, dealing with continuous elements, and converting brute-force approaches into efficient solutions.

Navigating Attention-Driving Algorithms, Capturing the Premium of Proximity for Virtual Teams, & New AI Devices

Implications, by Scott Belsky • 530 implied HN points • 18 Nov 23
🕹 Technology AI Algorithms Remote work
  1. AI-powered algorithms are driving polarization by optimizing for attention-grabbing content, widening the surface area of topics that stoke anger.
  2. Our social media feeds are now sourced from algorithmic preferences rather than social networks, shaping the content we are exposed to.
  3. The benefits of physical proximity in fostering creativity and relationships for teams will lead to the emergence of new technologies and management strategies supporting hybrid and remote work environments.

Algorithms start out as magic. But then become captive to corporate interests.

Asimov’s Addendum • 19 implied HN points • 19 Aug 24
🕹 Technology AI Algorithms Big Tech Data Privacy Competition
  1. Google has been found to have abused its power to control search engine results, limiting competition. This means they had an unfair advantage to keep other companies from competing effectively.
  2. Algorithms that start off as amazing tools can end up being exploited for corporate gain. The way Google uses its algorithms looks like magic at first but turns out to serve its own business interests.
  3. To foster fair competition in the tech industry, we need more transparency and rules about how algorithms work. This could lead to better choices for users and support new companies to grow.

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.

wind and rain and sun and data

Dan Davies - "Back of Mind" • 334 implied HN points • 19 Jan 24
🕹 Technology Energy Data Trading Algorithms Regulation
  1. Supply and demand for electricity become more unpredictable with an increasing proportion of wind and solar energy
  2. The profit motive drives the application of information processing power and bandwidth to solve energy planning problems
  3. Market trading and the profit motive are ways to match the variety of the energy problem with the regulatory system

Can GPT-4 *Actually* Write Code?

Tyler Glaiel's Blog • 567 HN points • 17 Mar 23
🕹 Technology Artificial Intelligence Coding Algorithms Machine Learning Programming
  1. GPT-4 can write code when given existing algorithms or well-known problems, as it remixes existing solutions.
  2. However, when faced with novel or unique problems, GPT-4 struggles to provide accurate solutions and can make incorrect guesses.
  3. It's crucial to understand that while GPT-4 can generate code, it may not be reliable for solving complex, new problems in programming.

GPU peak calling for epigenetics

LatchBio • 11 implied HN points • 21 Jan 25
🕹 Technology Bioinformatics Data Analysis Algorithms Computing Genetics
  1. Peak calling is crucial for analyzing epigenetic data like ATAC-seq and ChIP-seq. It helps scientists identify important regions in the genome related to gene expression and diseases.
  2. The MACS3 algorithm is a common tool used for peak calling but struggles with handling large data volumes efficiently. Improving its implementation with GPUs can speed up analyses significantly.
  3. By using GPUs, researchers have achieved about 15 times faster processing speeds for peak calling, which is vital as more genetic data is generated in the field.