The hottest Algorithms Substack posts right now

And their main takeaways
Category
Top Technology Topics

The Authority of the Algorithms

The Digital Anthropologist • 19 implied HN points • 12 Feb 24
🕹 Technology Algorithms
  1. Algorithms are deeply integrated into our daily lives, impacting everything from music to job applications, showing both benefits and risks.
  2. Algorithms, designed by humans, are gaining authority in society, prompting questions about ethical guidelines and accountability for their creators.
  3. Concerns about algorithms creating a bland, uniform world are present, but societal values and human creativity may prevent dystopian outcomes.

We've Entered the Era of Hyper-Personalization

Embracing Enigmas • 39 implied HN points • 31 May 23
🕹 Technology Algorithms
  1. We are entering an era of hyper-personalization where content is tailored to specific individuals beyond just what they might like.
  2. The progression of personalization stages includes one-size-fits-all, segmentation, behavioral personalization, predictive personalization, and now hyper-personalization.
  3. The main components needed for hyper-personalization are data about the individual, algorithms for content selection, content creation, and a trust layer for quality control.

Twitter open-sourced their recommendation algorithm

MLOps Newsletter • 39 implied HN points • 09 Apr 23
🕹 Technology Algorithms
  1. Twitter has open-sourced their recommendation algorithm for both training and serving layers.
  2. The algorithm involves candidate generation for in-network and out-network tweets, ranking models, and filtering based on different metrics.
  3. Twitter's recommendation algorithm is user-centric, focusing on user-to-user relationships before recommending tweets.

Why 8(to)7 encryption is snake-oil

Cybersect • 39 implied HN points • 28 May 23
🕹 Technology Algorithms
  1. People often invent new encryption algorithms without solid evidence of effectiveness.
  2. Be cautious of encryption claims that use buzzwords and make unreasonable promises.
  3. It's important to seek technical explanations and benchmarks to evaluate the validity of encryption algorithms.
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XGBoost is the Secret of ML Energy

Sector 6 | The Newsletter of AIM • 39 implied HN points • 06 Sep 23
🕹 Technology Algorithms
  1. XGBoost, or Extreme Gradient Boosting, helps improve the performance and speed of machine learning models that deal with tabular data. It's known for being really good at finding patterns and making predictions.
  2. This algorithm works best for supervised learning when you have lots of training examples, especially when you have both categorical and numeric data. It can handle a mix of different data types well.
  3. If you're working with a dataset that has many features, XGBoost is a strong choice to enhance the capabilities of your machine learning model. It makes it easier to get accurate results.

Edge 461: The Many Challenges of Kowledge Distillation

TheSequence • 56 implied HN points • 31 Dec 24
🕹 Technology Algorithms
  1. Knowledge distillation can be tricky because there’s a big size difference between the teacher model and the student model. The teacher model usually has a lot more parameters, making it hard to share all the useful information with the smaller student model.
  2. Transferring the complex knowledge from a large model to a smaller one isn't straightforward. The smaller model might not be able to capture all the details that the larger model has learned.
  3. Despite the benefits, there are significant challenges that need to be tackled when using knowledge distillation in machine learning. These challenges stem from the complexity and scale of the models involved.

Some Speculation on Future Computational Physics

Bzogramming • 61 implied HN points • 27 Nov 24
🕹 Technology 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 mathematics of the game Waffle

A Piece of the Pi: mathematics explained • 48 implied HN points • 22 Jan 25
🔬 Science 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.

Algorithms you should know before taking System Design Interviews [System Design Sundays]

Technology Made Simple • 79 implied HN points • 11 Sep 22
🕹 Technology Algorithms
  1. Focus on understanding a few key algorithms that frequently show up in System Design interviews. Knowing these well can lead to great results.
  2. Prioritize learning the 'Five-star' algorithms, understand why they exist and the problems they solve. These are very important for interviews.
  3. Remember the 80/20 rule - most results come from basics that are commonly tested. Invest time in mastering those before moving on to advanced topics.

What We Talk About When We Talk About AI

Center for Veb Account Research Newsletter • 3 implied HN points • 12 Dec 25
🕹 Technology Algorithms
  1. AI is best understood as a set of decision‑making tools that 'satisfice' — they search for good‑enough solutions in complex models instead of finding perfect mathematical optima like operations research.
  2. AI tools expand a user or organization's administrative capacity by enabling new actions and complex modeling, but they can be brittle and depend heavily on training data and organizational process; the financial hype or stock valuations around AI are distinct from its practical usefulness.
  3. Intelligence and consciousness are not the same: systems can perform many cognitive tasks and even be 'general' in the sense of producing and using satisficing models without being conscious, so task performance alone doesn't imply subjective experience.

The Math Behind Your Commute

The Palindrome • 4 implied HN points • 05 Dec 25
🕹 Technology Algorithms
  1. BFS explores outward from a start node in layers, so it finds shortest paths by edge count and builds a BFS tree that groups nodes by distance.
  2. DFS follows one path as far as it can and backtracks when needed, producing a narrow, deep DFS tree. Any non-tree edge in DFS must connect a node to one of its ancestors or descendants.
  3. A connected component is the set of nodes reachable from a start node, and both BFS and DFS can find this component to decide if two nodes are connected. More generally, you can discover a component by repeatedly adding neighbors of the current set until no new nodes appear.

Grief for Twitter

Kyle Chayka Industries • 175 implied HN points • 11 Oct 23
🎭️ Culture Algorithms
  1. Twitter used to be a vibrant platform for diverse discussions and connections, but has now deteriorated due to algorithms and glitches.
  2. Finding spaces for meaningful human interaction on the internet is becoming increasingly challenging as platforms focus more on algorithms and less on authentic conversations.
  3. Despite the challenges, platforms like Substack are emerging as potential spaces for cultivating genuine communities and conversations.

Problem 77: Gas Station [Microsoft]

Technology Made Simple • 39 implied HN points • 02 Mar 23
🕹 Technology Algorithms
  1. Greedy algorithms are powerful and commonly used, especially in fields like AI.
  2. Understanding and applying greedy algorithms can help solve problems effectively, even in interviews.
  3. The Gas Station problem involves determining the starting index to travel around a circular route based on gas stations and costs.

Claiming LLMs are merely "next token predictors" is a fundamental misunderstanding

The Future of Life • 19 implied HN points • 18 Jan 24
🕹 Technology Algorithms
  1. LLMs are more than just next-token predictors. They use complex internal algorithms that let them understand and create language beyond simple predictions.
  2. The process that powers LLMs, like token prediction, is just a tool that leads to their true capabilities. These systems can evolve and learn in many sophisticated ways.
  3. Understanding LLMs isn't easy because their full potential is still a mystery. What limits them could be anything from their training methods to the data they learn from.

[Solution]Problem 65:Find the Fixed Point in a sorted array[Apple]

Technology Made Simple • 59 implied HN points • 18 Nov 22
🕹 Technology Algorithms
  1. A fixed point in a sorted array is an element whose value matches its index. Binary search can be used to efficiently find a fixed point if the array is sorted.
  2. When optimizing algorithms, focus on improving the major components like loop traversal to enhance the overall performance.
  3. In sorted arrays, utilizing comparators and the inherent comparison order can simplify the coding process and boost efficiency.

A Linear Algebra Trick for Computing Fibonacci Numbers Fast

Confessions of a Code Addict • 158 HN points • 05 Nov 23
🔬 Science Algorithms
  1. A linear algebra technique can be applied to compute Fibonacci numbers quickly with a logarithmic time complexity.
  2. Efficient algorithms like repeated squaring can compute powers of matrices in logarithmic time, improving performance for Fibonacci number calculations.
  3. A closed form expression using the golden ratio offers a direct method to compute Fibonacci numbers, showing different approaches with varied performance.

[Solution]Problem 52:Search 2D Matrix[Microsoft]

Technology Made Simple • 79 implied HN points • 18 Aug 22
🕹 Technology Algorithms
  1. Understanding the problem clearly is crucial in finding efficient solutions. This particular problem doesn't require special knowledge, just logic and basic algebra.
  2. Recognizing patterns and properties of the data can significantly enhance the algorithm. In this case, the unique rules about the sorted matrix rows were key to optimizing the search process.
  3. Optimizing code by leveraging insights about the data structures can simplify solutions and reduce unnecessary complexity. It's important to make the most of the given information to write efficient algorithms.

Not so fast, Mr. Fourier!

lcamtuf’s thing • 119 HN points • 12 Mar 24
🔬 Science Algorithms
  1. The discrete Fourier transform (DFT) is a crucial algorithm in modern computing, used for tasks like communication, image and audio processing, and data compression.
  2. DFT transforms time-domain waveforms into frequency domain readings, allowing for analysis and manipulation of signals like isolating instruments or applying effects like Auto-Tune in music.
  3. Fast Fourier Transform (FFT) optimizes DFT by reducing the number of necessary calculations, making it more efficient for large-scale applications in computing.

Fun with Serial and Parallel Sum Reductions

CPU fun • 121 implied HN points • 22 Feb 24
🔬 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.

Judging myself by likes

Kyle Chayka Industries • 195 implied HN points • 22 Jul 23
🎭️ Culture Algorithms
  1. Likes can impact how we judge our online success, but their significance varies across platforms.
  2. Algorithms on social media have changed how engagement is measured, making it harder to interpret likes as a true reflection of content quality.
  3. The age of likes is evolving, with platforms like TikTok shifting focus away from visible likes but still using them to influence content.

Scalable Embedding based retrieval for target side value

Recommender systems • 23 implied HN points • 17 May 25
🕹 Technology Algorithms
  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.

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 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.

[Solution]Problem 72: Permutation in String [Amazon]

Technology Made Simple • 39 implied HN points • 27 Jan 23
🕹 Technology Algorithms
  1. The problem discussed is about validating a binary search tree, ensuring the left subtree contains smaller values, the right subtree contains greater values, and both are valid binary search trees.
  2. Examples are provided to illustrate the concept, showing a valid and an invalid binary search tree.
  3. Constraints include the number of nodes and the value ranges in the tree.

The Vision Pro? Daddy, chill

The Rectangle • 113 implied HN points • 09 Feb 24
🕹 Technology Algorithms
  1. The release of the Vision Pro highlights a split in culture between bootlickers and contrarians.
  2. Bootlickers overly defend products and praise them, while contrarians overly criticize and find flaws in them.
  3. Algorithmisation exacerbates this cultural division by boosting controversial content and leading us into binary situations.

Problem 72: Permutation in String [Amazon]

Technology Made Simple • 39 implied HN points • 25 Jan 23
🕹 Technology Algorithms
  1. The problem discusses validating a binary search tree by checking if the left subtree contains keys less than the node's key and the right subtree contains keys greater than the node's key.
  2. It's important to ensure that both the left and right subtrees of a node are also binary search trees, following specific rules for structure and key values.
  3. Validating a binary search tree involves evaluating constraints like the number of nodes in the tree and the range of node values it can contain.

Problem 43: Jump Game (Amazon)

Technology Made Simple • 79 implied HN points • 23 Jun 22
🕹 Technology Algorithms
  1. The problem involves determining if you can reach the end of an array with elements representing maximum jump lengths at each position.
  2. It highlights the use of Greedy Algorithms for a specific optimization in this problem.
  3. The post encourages audience engagement by suggesting feedback, surveys, and subscribing for additional content.

Edge 447: Not All Model Distillations are Created Equal

TheSequence • 49 implied HN points • 12 Nov 24
🕹 Technology Algorithms
  1. There are different types of model distillation that help create smaller, more efficient AI models. Understanding these types can help in choosing the right method for specific tasks.
  2. The three main types of model distillation are response-based, feature-based, and relation-based. Each has its own strengths and can be used depending on what you need from the model.
  3. Response-based distillation is usually the easiest to implement. It focuses on how the student model responds to similar inputs as the teacher model.

For You

Conspirador Norteño • 44 implied HN points • 22 Nov 24
🕹 Technology Algorithms
  1. The 'For You' feed on X shows mostly posts from accounts you don't follow. In fact, more than half of the recommended posts come from these unfamiliar sources.
  2. Elon Musk's posts are the most frequently suggested, even to users who do not follow him. This indicates that trending figures often dominate the recommendation algorithm.
  3. Connections between suggested accounts are mostly based on repost interactions. Most recommended accounts have links to the ones you already follow, showing a network effect.

HackerNews Ranking Algorithm: How would you have done it?

Vigneshwarar’s Newsletter • 181 HN points • 09 Apr 23
🕹 Technology Algorithms
  1. The current HackerNews ranking algorithm is based on a simple formula involving points, age, and a constant factor.
  2. Proposing a new approach called HackerRank that incorporates PageRank-like scoring for user profiles based on upvotes and takes flagging into account.
  3. Additional ideas for improving the ranking algorithm include considering user submission upvotes, reading time, and website reputation.

[Solution]Problem 54:Multiply Strings[Google]

Technology Made Simple • 59 implied HN points • 02 Sep 22
🕹 Technology Algorithms
  1. The problem discussed involves multiplying two non-negative integers represented as strings without using built-in libraries or converting them directly to integers. This approach is used to prevent overflow.
  2. Understanding the process of multiplication and breaking it down into individual steps is crucial in tackling problems like this. It helps in figuring out the next steps and finding a path forward.
  3. Consider unique approaches, such as examining how computers encode characters and utilizing this knowledge to work around constraints. Building a strong foundation in software engineering fundamentals is beneficial for problem-solving.

📝 Guest Post: An introduction to Similarity Search*

TheSequence • 182 implied HN points • 03 Apr 23
🕹 Technology Algorithms
  1. Vector similarity search is essential for recommendation systems, image search, and natural language processing.
  2. Vector search involves finding similar vectors to a query vector using distance metrics like L1, L2, and cosine similarity.
  3. Common vector search strategies include linear search, space partitioning, quantization, and hierarchical navigable small worlds.

AGI will be made of heterogeneous components

Engineering Ideas • 19 implied HN points • 27 Dec 23
🕹 Technology Algorithms
  1. AGI will be made of heterogeneous components, combining different types of DNN blocks, classical algorithms, and key LLM tools.
  2. The AGI architecture may not be perfect but will be close to optimal in terms of compute efficiency.
  3. The Transformer block will likely remain crucial in AGI architectures due to its optimization, R&D investments, and cognitive capacity.

Embracing the Bitter Lesson

Future History • 170 implied HN points • 06 Apr 23
🕹 Technology Algorithms
  1. Leverage computation for effective AI – supercomputers are vital.
  2. General methods outperform specialized knowledge over time in AI development.
  3. Human ingenuity and values are still crucial in machine learning, alongside generalized algorithms.

On the background screening and the Nazi glow in the eyes of a child

Tessa Fights Robots • 15 implied HN points • 04 Jun 25
🕹 Technology Algorithms
  1. Algorithms can have a big impact on our lives, especially in job hiring. This can lead to a world where human choices are replaced by cold, unfeeling machines.
  2. It's important to resist dehumanizing systems and treat ourselves and others with love and respect. We can rebel against these systems simply by being kind and staying true to ourselves.
  3. Creating art and sharing personal experiences can be powerful ways to challenge the influence of these systems. Focusing on our own freedom can help us disconnect from the machine.

How to Study Data Structures and Algorithms [Technique Tuesday]

Technology Made Simple • 79 implied HN points • 06 Apr 22
🕹 Technology Algorithms
  1. Experts often give bad advice for studying Data Structures and Algorithms, like relying solely on Leetcode.
  2. To effectively learn DSA, take time to understand the history and purpose of each data structure beyond just learning the mechanics.
  3. Don't rush through learning Data Structures and Algorithms; taking it slow and grasping the fundamentals thoroughly will lead to better mastery and understanding.

Bloom Filter [Systems Design Sundays]

Technology Made Simple • 59 implied HN points • 10 Jul 22
🕹 Technology Algorithms
  1. Bloom Filters are probabilistic data structures used to efficiently test for membership.
  2. Bloom Filters work by having a bit array of size m with k hash functions mapping values to indices, setting the indices to 1 for a given input.
  3. Bloom Filters are great for reducing unnecessary disk access, but they can result in false positives and need regeneration as more values are added.