The hottest Algorithms Substack posts right now

And their main takeaways
Category
Top Technology Topics

455: Rolex Pricing, Jensen & Nvidia, China Chip Ban 1.5, Costco, Google Maps to Optimize Cities, Bicycle for the Mind, and Beckham

Liberty’s Highlights • 452 implied HN points • 18 Oct 23
🕹 Technology Chips Algorithms Data Analysis Artificial Intelligence Tech Companies
  1. It's liberating to realize that most fields are understandable to an interested outsider, focusing on big ideas.
  2. Exploring new fields and combining knowledge from different areas can lead to rich and interesting discoveries.
  3. Taking calculated risks and thorough preparation can lead to successful outcomes in business decisions, like pushing all the chips in.

For You

Conspirador Norteño • 44 implied HN points • 22 Nov 24
🕹 Technology Social media Algorithms Data Analysis User Experience Content curation
  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.

Standards Body Considers Uncloaking Secret Encryption Algorithms

Zero Day • 672 implied HN points • 11 Oct 23
🕹 Technology Security Encryption Standards Algorithms
  1. European standards body may make new encryption algorithms public due to backlash over secrecy.
  2. Previously kept secret algorithms had major flaws, prompting consideration for greater transparency.
  3. Independent researchers found vulnerabilities, including intentional backdoors, in old encryption algorithms in use for over 25 years.

Undecidability Isn't So Bad

Bzogramming • 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.
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Quant Letter: November 2024, Week-2

The Parlour • 25 implied HN points • 13 Nov 24
💰 Finance Investment Algorithms Risk management Quantitative Analysis Machine Learning
  1. A new computational method can measure the shadow rate, which helps in comparing different investment types. This can give investors better insights.
  2. Using multi-agent systems for investment research allows adaptation to changing market conditions, leading to improved performance over traditional models.
  3. Machine learning continues to show promise in finance, with various models effectively predicting market behavior and improving investment strategies.

Controlling Rate-Limited Events - Implementation

Low Latency Trading Insights • 117 implied HN points • 11 Feb 24
🕹 Technology Algorithms Data Structures Coding Performance
  1. The requirements for a rate-limiting algorithm include precise event counting, fast performance especially during market turbulence, and minimal impact on cache memory.
  2. Creating a rate-limiting algorithm using a multimap for counting events has inefficiencies; a better solution involves enhancements for optimal performance.
  3. A bounded approximation approach for rate limiting achieves memory efficiency by assuming a minimum time precision and implementing a clever advance-and-clear mechanism.

The Statistician Who Loved Machine Learning

Mindful Modeler • 279 implied HN points • 23 May 23
🕹 Technology Machine Learning Statistics Modeling Algorithms
  1. Leo Breiman emphasized the importance of both data modeling culture and algorithmic modeling culture in statistical modeling.
  2. Breiman advocated for being problem-focused over solution-focused, encouraging modelers to choose the appropriate mindset based on the task at hand.
  3. Understanding various modeling mindsets, such as statistical inference and machine learning, is crucial for effective modeling.

Edge 461: The Many Challenges of Kowledge Distillation

TheSequence • 56 implied HN points • 31 Dec 24
🕹 Technology AI Machine Learning Data science Algorithms Software Development
  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.

How to Learn Data Structures and Algorithms [Storytime Saturdays]

Technology Made Simple • 299 implied HN points • 22 Jan 23
🚌 Education Data Structures Algorithms Learning
  1. Understanding Data Structures and Algorithms is crucial for success in technical fields like software development.
  2. Many resources focus on DSA for coding interviews, but it's important to go beyond that to deepen your knowledge.
  3. Learning DSA effectively doesn't have to involve answering countless questions or watching numerous tutorials; there are better approaches available.

Fast Computation of Fibonacci Numbers: Part II

Confessions of a Code Addict • 288 implied HN points • 12 Nov 23
🕹 Technology Mathematics Programming Algorithms Education
  1. A new method to compute Fibonacci numbers using a closed-form expression without having to resort to floating point arithmetic.
  2. Representation of irrational numbers using two parts can be done in code allowing for precise computation of Fibonacci numbers.
  3. Understanding rings and implementing arithmetic operations within it can help in computing Fibonacci numbers without any loss of precision.

GM Shaders Mini: Math

GM Shaders Mini Tuts • 157 implied HN points • 11 Sep 23
🕹 Technology Algorithms
  1. Alpha blending in shader programming requires blending colors and alpha channels separately.
  2. Weighted averages provide greater control for combining multiple elements together in shaders.
  3. Creating a simple 3D perspective effect in shaders involves scaling with a linear gradient.

GM Shaders Mini: Imagination

GM Shaders Mini Tuts • 157 implied HN points • 02 Sep 23
🕹 Technology Graphics Programming Mathematics Algorithms Tutorials
  1. When working with shaders, think in terms of vector fields to direct the flow and create gradients.
  2. Consider the acceptable input domains and the output ranges of your functions to prevent errors and unexpected results.
  3. Utilize periodic functions for repetition, sine and cosine for waves and rotations, dot product as a ruler, and exponentiation for adjusting brightness levels.

Genre Grapevine Presents Even More Examples of Deceptive Language Around Machine Learning

Genre Grapevine • 137 implied HN points • 01 Aug 23
🕹 Technology Machine Learning AI Algorithms
  1. Deceptive language is used in discussions around machine learning, like calling machine learning 'artificial intelligence' when it's really algorithms crafted from data samples.
  2. Some authors exaggerate the use of AI, like claiming to have written and sold a large number of books when the reality is quite different upon closer inspection.
  3. Manipulative language is often used to promote machine learning systems, such as claiming a machine learning system is a 'poet' when in reality humans select the best output from thousands of generated pieces.

Math for Software Engineering[Math Mondays]

Technology Made Simple • 139 implied HN points • 21 Mar 23
🕹 Technology Mathematics Software Engineering Data Analysis Algorithms Machine Learning
  1. Linear Algebra is crucial for software engineers, especially for operations involving vector and matrix operations. Understanding the basics is key for most developers.
  2. Probability and Statistics play a significant role in analyzing data, and even non-AI professionals can benefit from grasping concepts like causal inference. Focus on foundational principles before diving deeper.
  3. Calculus, though important, may not be essential for all software engineers. Studying up to Calc-2 is generally adequate, as it appears in various other topics.

Do we need RL for RLHF?

Democratizing Automation • 182 implied HN points • 06 Dec 23
🕹 Technology AI Research Algorithms Data Models
  1. The debate around integrating human preferences into large language models using RL methods like DPO is ongoing.
  2. There is a need for high-quality datasets and tools to definitively answer questions about the alignment of language models with RLHF.
  3. DPO can be a strong optimizer, but the key challenge lies in limitations with data, tooling, and evaluation rather than the choice of optimizer.

Edge 447: Not All Model Distillations are Created Equal

TheSequence • 49 implied HN points • 12 Nov 24
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development 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.

Does terminology affect how we perceive and evaluate intelligent systems?

UX Psychology • 158 implied HN points • 16 Jan 23
🕹 Technology AI Algorithms
  1. Terminology used to describe intelligent systems can impact how people perceive and evaluate them. Different terms like 'AI', 'algorithms', or 'robots' can influence perceptions of complexity, trustworthiness, and human-likeness.
  2. Research shows that the terminology chosen can affect perceptions of fairness and trust in intelligent systems. Terms like 'algorithm' and 'sophisticated statistical model' may lead to better evaluations compared to 'artificial intelligence'.
  3. The terminology selected for discussing intelligent systems can have strategic implications. Companies and product designers can intentionally use terminology to shape perceptions, engage users, and influence attitudes towards products using intelligent systems.

Not so fast, Mr. Fourier!

lcamtuf’s thing • 119 HN points • 12 Mar 24
🔬 Science Computing Signal Processing Mathematics Algorithms Engineering
  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.

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.

Composites and Correlations

Cybernetic Forests • 139 implied HN points • 26 Feb 23
🕹 Technology Biases Machine Learning Algorithms
  1. Composite images were historically used to reinforce racist and eugenic ideologies, linking appearance with criminality and intelligence.
  2. The use of language and categorization in AI-generated images can perpetuate biases and stereotypes, reflecting societal norms and prejudices.
  3. The dataset used in AI models can influence the outcomes, showing how biases and problematic representations are embedded in the generated images.

Can Minor Document Typos Comprehensively Disrupt RAG Retriever & Reader Components?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 20 May 24
🕹 Technology AI NLP Data Algorithms Machine Learning
  1. RAG systems can struggle with small mistakes in documents, making them vulnerable to errors. Even tiny typos can disrupt how well these systems work.
  2. The study introduces a method called GARAG that uses a genetic algorithm to create tricky documents that can expose weaknesses in RAG systems. It's about testing how robust these systems really are.
  3. Experiments show that noisy documents in real-life databases can seriously hurt RAG performance. This highlights that even reliable retrievers can falter if the input data isn’t clean.

The Vision Pro? Daddy, chill

The Rectangle • 113 implied HN points • 09 Feb 24
🕹 Technology Hardware Social media Artificial Intelligence Spatial Computing 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 85: Count Complete Tree Nodes [Amazon]

Technology Made Simple • 99 implied HN points • 04 May 23
🕹 Technology Coding Data Structures Algorithms Tech industry
  1. The post discusses Problem 85: Count Complete Tree Nodes [Amazon], focusing on recursion, trees, and data structures.
  2. It is about solving a problem related to counting the number of nodes in a complete binary tree efficiently.
  3. The post mentions the importance of community engagement in choosing problems to discuss and the growth of the author's newsletter.

Understanding Big Oh, Big Theta, and Big Omega Notations[Math Mondays]

Technology Made Simple • 99 implied HN points • 16 May 23
🕹 Technology Algorithms Computing Education
  1. Time complexity refers to the number of instructions a software executes, not the actual time taken to run the code.
  2. Three common asymptotic notations for computing time complexity are Big Oh, Big Theta, and Big Omega.
  3. Understanding time complexity bounds is essential in computer science and software engineering, as they are fundamental concepts that appear regularly.

Daily bit(e) of C++ | Learn Modern C++ 6/N

Daily bit(e) of C++ • 98 implied HN points • 03 Jun 23
🕹 Technology Programming Algorithms Sorting
  1. Iterators provide an abstraction layer for containers and different types allow for specific operations such as forward, backward, or random access iteration.
  2. Algorithms in the standard library provide efficient ways to perform common operations on containers like sorting, copying, and looking up elements.
  3. Views help avoid unnecessary data copies by allowing for lazy evaluation of operations on ranges, providing a more efficient way to chain operations.

Practical AI is a Big Tent: Five Ideas for CEOs, Leaders, and Investors

Mike Talks AI • 98 implied HN points • 27 Aug 23
🕹 Technology AI Algorithms Data Analysis Team Building
  1. Practical AI encompasses various machine learning algorithms and techniques, including optimization and Operations Research.
  2. The concept of Practical AI allows for the inclusion of both established and emerging approaches in the field.
  3. To effectively solve real-world problems, AI leaders need a diverse set of skills and expertise, and must understand the strengths and weaknesses of different algorithms.

A Linear Algebra Trick for Computing Fibonacci Numbers Fast

Confessions of a Code Addict • 158 HN points • 05 Nov 23
🔬 Science Mathematics Computing Algorithms Linear Algebra Complexity
  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.

Adversarial Examples in AI

Rod’s Blog • 39 implied HN points • 29 Feb 24
🕹 Technology AI Security Research Algorithms
  1. Adversarial examples can deceive AI systems by manipulating inputs, leading to incorrect outcomes in various domains like medical imaging and autonomous vehicles.
  2. Understanding these risks is crucial for building effective defenses and creating awareness about the vulnerabilities in AI systems.
  3. Researchers are actively working to develop robust defenses against adversarial attacks to enhance the security and reliability of AI technology.