The hottest Optimization Substack posts right now

And their main takeaways
Top Technology Topics
Mindful Modeler • 279 implied HN points • 09 Apr 24
  1. Machine learning is about building prediction models. It covers a wide range of applications, but may not be perfect for unsupervised learning.
  2. Machine learning is about learning patterns from data. This view is useful for understanding ML projects beyond just prediction.
  3. Machine learning is automated decision-making at scale. It emphasizes the purpose of prediction, which is to facilitate decision-making.
Play Permissionless • 319 implied HN points • 18 Mar 24
  1. To win big, you only need to get a small number of things right and can afford to mess up everything else. This applies to both companies and individuals.
  2. Winning big often requires unlearning traditional schooling strategies and focusing on doing a great job at a few key aspects while neglecting the rest.
  3. Removing non-essential tasks and focusing solely on what helps deliver better and faster results can lead to significant improvements and ultimately winning big.
Confessions of a Code Addict • 577 implied HN points • 15 Jan 24
  1. Code efficiency at scale is crucial - data structures and algorithms matter, but execution cost is also important.
  2. Participating in challenges like the 1 Billion Row Challenge can enhance performance engineering skills.
  3. The workshop covers optimization techniques like flamegraphs, I/O strategies, system calls, SIMD instructions, and more.
Mindful Modeler • 818 implied HN points • 14 Nov 23
  1. Understanding the distribution of the target variable is key in choosing statistical analysis or machine learning loss functions.
  2. Certain loss functions in machine learning correspond to maximum likelihood estimation for specific distributions, creating a bridge between statistical modeling and machine learning.
  3. While connecting distributions to loss functions is insightful, the real power in machine learning lies in the flexibility to design custom loss functions rather than being constrained by specific distributions.
Gentle Nudge • 19 implied HN points • 28 May 24
  1. Funnel optimization involves analyzing stages, generating hypotheses, and considering user feedback to improve user experience.
  2. The 3B framework, focusing on Behavior, Barriers, and Benefits, helps adjust products from the users' perspective for better engagement.
  3. Identify potential barriers in the user journey, offer small incentives, like progress indicators, and align call-to-actions with expected results to enhance user motivation.
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Technology Made Simple • 179 implied HN points • 27 Feb 24
  1. Memory pools are a way to pre-allocate and reuse memory blocks in software, which can significantly enhance performance.
  2. Benefits of memory pools include reduced fragmentation, quick memory management, and improved performance in programs with frequent memory allocations.
  3. Drawbacks of memory pools include fixed-size blocks, overhead in management, and potential for memory exhaustion if not carefully managed.
Age of Invention, by Anton Howes • 1008 implied HN points • 10 Aug 23
  1. Robert Bakewell had an 'improving mentality' when it came to breeding animals, focusing on optimizing profit and efficiency.
  2. Bakewell selectively bred cows and sheep to maximize valuable meat and minimize feeding costs.
  3. The improving mentality led Bakewell to continuously optimize all aspects of his farm, from animal breeding to farm layout and operations.
Art’s Substack • 3 HN points • 12 Jun 24
  1. The One Billion Row Challenge in Rust involves writing a program to analyze temperature measurements from a huge file, requiring specific constraints for station names and temperature values.
  2. The initial naive implementation faced performance challenges due to reading the file line by line, prompting optimizations like skipping UTF-8 validation and using integer values for faster processing.
  3. Despite improvements in subsequent versions, performance was still slower than the reference implementation, calling for further enhancements in the next part of the challenge.
SwirlAI Newsletter • 432 implied HN points • 02 Jul 23
  1. Understanding Spark architecture is crucial for optimizing performance and identifying bottlenecks.
  2. Differentiate between narrow and wide transformations in Spark, and be cautious of expensive shuffle operations.
  3. Utilize strategies like partitioning, bucketing, and caching to maximize parallelism and performance in Spark applications.
The ZenMode • 42 implied HN points • 16 Mar 24
  1. Sharding is a technique to horizontally partition a data store into smaller fragments across multiple servers, aiding in scalability and reliability.
  2. Before sharding a database, consider options like vertical partitioning, database optimization, replication, and caching to improve performance without the added complexity of sharding.
  3. Different sharding strategies like Hash Sharding, Range Sharding, and Directory-Based Sharding have unique considerations and advantages based on factors like data distribution, queries, and maintenance.
SwirlAI Newsletter • 314 implied HN points • 06 Aug 23
  1. Choose the right file format for your data storage in Spark like Parquet or ORC for OLAP use cases.
  2. Understand and utilize encoding techniques like Run Length Encoding and Dictionary Encoding in Parquet for efficient data storage.
  3. Optimize Spark Executor Memory allocation and maximize the number of executors for improved application performance.
Ron Friedhaber • 3 HN points • 26 May 24
  1. Math notation focuses on simplification, not optimization, unlike in computer programming where efficiency is crucial.
  2. In math, statements are mostly immutable and remain so until proven true, contrasting with programs that are mutable to accommodate bugs and user requests.
  3. Python initially succeeded with dynamic typing for prototyping but has gradually shifted towards typed Python, reflecting a broader trend in the language's evolution.
MLOps Newsletter • 39 implied HN points • 04 Feb 24
  1. Graph transformers are powerful for machine learning on graph-structured data but face challenges with memory limitations and complexity.
  2. Exphormer overcomes memory bottlenecks using expander graphs, intermediate nodes, and hybrid attention mechanisms.
  3. Optimizing mixed-input matrix multiplication for large language models involves efficient hardware mapping and innovative techniques like FastNumericArrayConvertor and FragmentShuffler.
Arpit’s Newsletter • 157 implied HN points • 05 Apr 23
  1. Ensuring correctness in multi-threaded programs is crucial; use locking and atomic instructions to prevent race conditions.
  2. For optimality, ensure fairness among threads and efficient logic to avoid bottlenecks.
  3. Divide workload evenly among threads or use a global variable to track progress for efficient results.’s Newsletter • 157 implied HN points • 13 Mar 23
  1. Estimate the minimum and maximum learning rate values by observing when the loss decreases and increases during training.
  2. Choosing learning rates within the estimated range can optimize model training.
  3. Validating learning rate ranges and fine-tuning with different datasets can improve model flexibility and accuracy.
Bzogramming • 30 implied HN points • 29 Jan 24
  1. The physical constraints of computing, such as distance and volume, significantly impact performance and efficiency.
  2. Parallelism at different scales within a program can affect latency and performance, offering opportunities for optimization.
  3. Considerations like curvature of computation, square-cube law, and heat generation play a crucial role in the design and limitations of computer chips.
Technology Made Simple • 119 implied HN points • 26 Jul 23
  1. Branchless programming is a technique that minimizes the use of branches in code to avoid performance penalties.
  2. Branchless programming can offer optimization benefits, but its complexity can outweigh the performance gains and make code maintenance challenging.
  3. Simpler code is often better than overly complex code, and branchless programming may not be suitable for most developers despite its potential performance improvements.
Technology Made Simple • 119 implied HN points • 26 Apr 23
  1. Compile time evaluation can help execute functions at compile time instead of run time, saving memory and CPU time.
  2. Dead code elimination removes unused code, enhancing code readability and reducing executable size.
  3. Strength reduction is a compiler optimization technique that replaces expensive operations with simpler ones, making localized code changes easier.
Democratizing Automation • 90 implied HN points • 02 Aug 23
  1. Reinforcement learning from human feedback involves using proxy objectives, but over-optimizing these proxies can negatively impact the final model performance.
  2. Optimizing reward functions for chatbots with RLHF can be challenging due to the disconnect between objective functions and actual user preferences.
  3. A new paper highlights fundamental problems and limitations in RLHF, emphasizing the need for a multi-stakeholder approach and careful consideration of current technical setups.’s Newsletter • 98 implied HN points • 21 Jun 23
  1. D-Adaptation method automates setting learning rate, aiming for optimal convergence in machine learning.
  2. Implementing D-Adaptation can consume more VRAM and result in slower training speed compared to other optimizers.
  3. Initial results show D-Adaptation performing comparably to hand-picked parameters in generating high-quality models.