The hottest Programming Substack posts right now

And their main takeaways
Category
Top Technology Topics

The Messy Page

Register Spill • 216 implied HN points • 07 May 23
🕹 Technology Software Engineering Programming Debugging AI
  1. The author prefers messy projects over greenfield projects because they provide more certainty and direction.
  2. Having clear product-market fit and defined requirements make a project enjoyable to work on.
  3. The author finds debugging appealing due to its clear requirements and the assurance that efforts won't be wasted.

5 SIEM Capabilities for Detection Engineering

Detection at Scale • 59 implied HN points • 15 Apr 24
🕹 Technology Security Data Management Automation Cloud Computing Programming
  1. Detection Engineering involves moving from simply responding to alerts to enhancing the capabilities behind those alerts, leading to reduced fatigue for security teams.
  2. Key capabilities for supporting detection engineering include a robust data pipeline, scalable analytics with a security data lake, and embracing Detection as Code framework for sustainable security insights.
  3. Modern SIEM platforms should offer an API for automated workflows, BYOC deployment options for cost-effectiveness, and Infrastructure as Code capabilities for stable long-term management.

Using Matplotlib To Create The 50th Article Cover Mosaic Animation (#50 Part 2)

The Python Coding Stack • by Stephen Gruppetta • 119 implied HN points • 10 Feb 24
🕹 Technology Programming Animation Visualization
  1. You can use Matplotlib to create animations, like a mosaic of previous article cover images, by following a step-by-step tutorial.
  2. Before starting the animation, ensure you have images ready and the necessary libraries installed like Matplotlib, NumPy, and Pillow.
  3. You can control how images are plotted, resize images in the animation frames, and save the animation as a movie file like an mp4 or an animated GIF using libraries like Matplotlib or PillowWriter.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Under the microscope: Skeleton Warriors (Saturn, PlayStation)

Rings of Saturn • 14 implied HN points • 08 Dec 24
🕹 Technology Video Games Game Development Cryptography Programming Mathematics
  1. The article talks about finding cheat codes for the game Skeleton Warriors on the Saturn and PlayStation consoles. It highlights how some of these codes were previously unknown and required research and coding to uncover.
  2. The author used a combination of brute force methods and mathematical approaches to determine the missing cheat codes. They collaborated with a cryptographer to solve complex hashing problems involved in the game.
  3. Mick West, a cofounder of Neversoft, contributed to the findings by sharing source code and insights about how the cheats work in the game. This shows a connection between game development and player discovery.

How I've been using ChatGPT

The Counterfactual • 219 implied HN points • 25 Jul 23
🕹 Technology AI Tools Research Methods Programming Information Retrieval
  1. ChatGPT can help you learn about new topics by suggesting useful resources and references. This can speed up your research by providing relevant information without the hassle of searching through many documents.
  2. Using ChatGPT for recommendations can be helpful, but it shouldn't replace getting suggestions from friends or experts. It can fill in gaps when you don't have access to personal recommendations.
  3. ChatGPT acts as a good reading companion by answering specific questions while you read. This helps you understand the material better and encourages you to ask questions about what you’re learning.

LLM Disruption in Chatbot Development Frameworks

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 05 Jul 24
🕹 Technology AI Chatbots Natural Language Programming Development
  1. Large Language Models (LLMs) make chatbots act more like humans, making it easier for developers to create smart bots.
  2. Using LLMs reduces the need for complex programming rules, allowing for quicker chatbot setup for different uses.
  3. Despite the benefits, there are still challenges, like keeping chatbots stable and predictable as they become more advanced.

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.

Console #189 - Interview with Elia of Opal - a Ruby to JavaScript source-to-source compiler

Console • 413 implied HN points • 24 Dec 23
🕹 Technology Programming Open Source Web Development Software Tools Debugging
  1. Opal is a source-to-source compiler that converts Ruby to JavaScript.
  2. Opal leverages the underlying JavaScript engine for speed, size, and debugging benefits.
  3. The project Opal aims to continue improving by exploring features like dead-code-elimination and better module support.

OpenAI Agent Query Planning Using LlamaIndex

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 99 implied HN points • 05 Feb 24
🕹 Technology AI Software Machine Learning Data science Programming
  1. An OpenAI agent can analyze information from multiple documents at once. This helps create detailed answers to queries based on several sources.
  2. Using the LlamaIndex framework, you can easily set up a system to manage and query PDF documents. This makes finding specific data more efficient.
  3. The agent can summarize financial data, showing how companies like Uber grow revenue over time. This is helpful for understanding trends in business performance.

How Vector Databases enable Generative AI [Math Mondays]

Technology Made Simple • 199 implied HN points • 06 Jun 23
🕹 Technology Programming Databases AI Software Engineering Math
  1. Vector databases store data as high-dimensional vectors to enable advanced AI like Gen AI.
  2. Vectors are crucial for AI applications like language processing, computer vision, and recommendation systems.
  3. Vector databases offer flexibility in handling complex datasets, allowing AI models to interact more effectively.

Program-Aided Language Models

Deep (Learning) Focus • 196 implied HN points • 22 May 23
🕹 Technology AI Programming Reasoning Language Models Deep Learning
  1. LLMs can struggle with tasks like arithmetic and complex reasoning, but using an external code interpreter can help them compute solutions more accurately.
  2. Program-Aided Language Models (PaL) and Program of Thoughts (PoT) techniques leverage both natural language and code components to enhance reasoning capabilities of LLMs.
  3. Decoupling reasoning from computation within LLMs through techniques like PaL and PoT can significantly improve performance on complex numerical tasks.

dlt windsurfing

davidj.substack • 71 implied HN points • 04 Dec 24
🕹 Technology Software Data Engineering AI Programming APIs
  1. dlt is a Python tool that helps organize messy data into clear, structured datasets. It's easy to use and can quickly load data from many sources.
  2. Using AI tools like Windsurf can make coding feel more collaborative. They help you find solutions faster and reduce the burden of coding from scratch.
  3. Storing data in formats like parquet can make processing much quicker. Simplifying your data handling can save you a lot of time and resources.

Python Lists: A closer look, part 11

Mostly Python • 628 implied HN points • 30 Mar 23
🕹 Technology Programming Data Management Development Python Classes
  1. Copying a list in Python can lead to unexpected behavior if the items in the list are mutable objects.
  2. To create a true copy of a list with mutable objects, use the deepcopy() function from the copy module.
  3. When working with Python lists, consider the nature of the items in the list to decide between using list[:], list.copy(), or deepcopy().

When AI Promises Speed but Delivers Debugging Hell

Nick Savage • 40 implied HN points • 26 Jan 25
🕹 Technology AI Development Programming Software Innovation
  1. Codescribble is a new shared text editor that lets multiple people work on the same document at once. It's designed to be fast and easy to use, similar to Google Docs.
  2. Using AI to help build software can be frustrating and messy, especially if you don’t fully understand how it works. This can lead to a lot of debugging and wasted time.
  3. It's crucial to keep a broader perspective while coding. Getting too focused on small tasks can lead to mistakes and delays, so step back and see the bigger picture.

Intents: the end-game for computing interfaces?

Router by Dmitry Pimenov • 2 HN points • 11 Sep 24
🕹 Technology Interfaces Programming AI Computing Automation
  1. Computing interfaces are evolving from specific command-based systems to more user-friendly methods that focus on overall goals. This makes it easier for developers to work on what really matters instead of getting bogged down in details.
  2. Intent-driven interfaces allow us to express our thoughts directly to machines, removing the need for complicated steps. This means we can communicate what we want in a more natural way.
  3. The rise of AI and new technologies is shifting how we interact with computers. Soon, we may even communicate our intentions directly from our minds, making technology feel more personal and easier to use.

How to metacognate

Sunday Letters • 99 implied HN points • 29 Jan 24
🕹 Technology AI Programming Machine Learning Cognition Data Automation
  1. Working with complex models can be hard when they get confused by incorrect or incomplete information. This can lead to mistakes and conflicts in what they remember.
  2. Creating a stable pattern for how tasks are done can help models work better by giving them a solid structure to follow. This is like giving the model a framework to lean on for more complicated tasks.
  3. As models improve, the need for extra coding to guide their thinking may lessen. Better memory strategies will likely help them function more effectively over time.

THINK's Lightspeed C

Computer Ads from the Past • 512 implied HN points • 27 Sep 23
🕹 Technology Computers Programming Software Tech Companies Computer History
  1. Lightspeed C was a C programming language software developed in the mid-1980s for Atari systems and Macintosh computers.
  2. Clearstar Softechnologies created Lightspeed C for Atari systems in 1985, and the company was later purchased by Omega Soft in 1988.
  3. THINK Technologies released Lightspeed C for Mac and later renamed it to THINK C in the mid-1980s, receiving positive reviews for its speed and user-friendliness.

Can AI Speed up your Code?

Daniel’s Substack • 3 HN points • 03 Sep 24
🕹 Technology AI Software Programming Development Performance
  1. AI can write a lot of code quickly, but it often makes mistakes. So, it's not ready to fully replace human programmers yet.
  2. Converting Python code to C++ can make it run much faster, especially for tasks that involve heavy string processing.
  3. While AI can assist in code conversion, current tools have limitations and sometimes struggle with complex functions.

Amber, Babylon, sun.misc.Unsafe and the rest: Java's plans for 2024 - JVM Weekly vol. 69

JVM Weekly • 98 implied HN points • 25 Jan 24
🕹 Technology Programming Software updates Technology Adoption API Development
  1. Java has ambitious plans for 2024 with projects like Amber, Babylon, and Valhalla focused on improving performance, memory efficiency, and concurrency.
  2. New JEPs in 2024 include proposals to deprecate memory access methods in sun.misc.Unsafe and enhancements for pattern matching for primitive types.
  3. Jakarta EE 11 now offers support for JDK 17 alongside JDK 21 to ease migration and accommodate user preferences for Java versions.

What I Learned This Week #1

Eventually Consistent • 39 implied HN points • 06 May 24
🕹 Technology Databases Programming Podcasts Social media
  1. ScyllaDB introduces a shard per core design, maximizing parallelism by assigning a separate shard to each core.
  2. FoundationDB bridges SQL and NoSQL, offering ACID transactions with schema flexibility and performance.
  3. Compilers like Clang and language servers like Clangd have separate purposes; language servers follow the Language Server Protocol for portability.

What Every Developer Should Know About GPU Computing

Confessions of a Code Addict • 465 HN points • 18 Oct 23
🕹 Technology Programming Architecture
  1. GPUs are designed for high throughput and massive parallelism, while CPUs focus on executing sequential instructions quickly.
  2. GPU architecture includes streaming multiprocessors with cores, various memory layers, and dynamic resource partitioning for efficient execution.
  3. Executing code on GPUs involves launching grids of thread blocks, with each block consisting of threads that work in parallel to optimize performance.

Explore Your Modeling Mindset With A Quiz

Mindful Modeler • 179 implied HN points • 20 Jun 23
🕹 Technology Modeling Web Development Machine Learning Programming
  1. Modeling assumptions affect how the model can be used. For instance, causal considerations lead to causal claims.
  2. Revisiting and understanding our modeling assumptions can help us tackle problems more effectively, beyond our usual mindset.
  3. Creating simple static websites can be made easier with tools like GPT-4, especially if you have some understanding of HTML, CSS, and JavaScript.

Joe's Nerdy Rants #5

Joe Reis • 176 implied HN points • 17 Jun 23
🕹 Technology Data Modeling Artificial Intelligence Business Startups Programming
  1. Data professionals interpret the concept of 'model' in various ways, leading to confusion and inconsistency in the field.
  2. Establishing a shared understanding through high-level data modeling can promote consistent and reliable models in organizations.
  3. The use of AI tools in programming has become widespread, indicating a shift in the nature of programming but emphasizing the importance of understanding and verifying AI-generated code.

FlowMind Is An Automatic Workflow Generator

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 25 Jun 24
🕹 Technology AI Automation Workflow Data science Programming
  1. FlowMind is a new tool that helps create automatic workflows using advanced AI. It takes user requests and generates code to complete tasks quickly.
  2. The system uses APIs to gather information and provides real-time feedback, allowing users to adjust the workflows as needed. This makes the process more interactive.
  3. FlowMind aims to improve the reliability of AI by reducing errors and making sure there is no direct connection to sensitive data. It focuses on keeping user data safe while handling requests.

How Researchers found Gods Number[Technique Tuesdays]

Technology Made Simple • 139 implied HN points • 22 Nov 23
🔬 Science Math Programming Combinatorics Symmetry Simulation
  1. God's Algorithm aims for the fewest moves possible in combinatorial games like Rubik's Cube.
  2. Researchers found God's Number for Rubik's Cube using techniques like partitioning, symmetry, and dropping optimality.
  3. Key strategies used were dividing the problem into smaller parts, leveraging symmetry to reduce work, and focusing on finding solutions within 20 moves instead of the best possible solution.

HCF EP 007: Prototyping with imported data

Hasen Judi • 35 implied HN points • 17 Jan 25
🕹 Technology Software Development Programming Data Management UI Design Web Development
  1. The project aims to develop a conversation view that displays threaded replies in a linear format, improving user experience compared to platforms like Twitter or Reddit.
  2. A data model is proposed to track parent-child relationships between posts and replies, allowing for efficient retrieval of both ancestors and descendants of a post.
  3. The author emphasizes using the same 'Post' type across different system layers, arguing that this reduces code complexity and increases productivity compared to using separate representations for each layer.

Understanding Software Idempotency [Math Mondays]

Technology Made Simple • 119 implied HN points • 11 Dec 23
🕹 Technology Software Math Engineering Systems Programming
  1. Idempotency ensures the same output regardless of how many times an operation is executed, providing data consistency and preventing duplicate operations.
  2. Idempotency is crucial for resilience, scalability, performance, simplicity, and provability in software systems.
  3. Implementing idempotency involves using unique identifiers, versioning, transaction management, and leveraging HTTP methods, offering benefits like better error handling and faster response times.

All the bullshit I did as a kid (part 1?)

Basta’s Notes • 204 implied HN points • 17 Jan 24
🕹 Technology Programming Web Development Operating Systems Software Development Tech industry
  1. The author reflects on the interesting and ambitious projects they worked on as a kid, showcasing a strong interest in technology and programming.
  2. Despite lacking mentorship, the author taught themselves valuable programming skills, such as building their own web browser and writing complex code like a CSS parser.
  3. The journey from tinkering with personal computers to winning a programming contest and earning internship opportunities highlights the author's growth and passion for technology.

English is NOT a Programming Language 🤦

Sector 6 | The Newsletter of AIM • 79 implied HN points • 07 Feb 24
🕹 Technology Programming AI Software Natural Language Computing
  1. English has too many ambiguities to be a programming language. Programming needs precise rules, and English doesn't always follow them.
  2. Douglas Crockford, the creator of JSON, is worried about pushing English as a coding language. He believes that code must be perfect, which English is not.
  3. Using natural language through AI for programming might lead to confusion. Clarity and accuracy are crucial for writing successful code.

DoRA is The New LoRA!

Aziz et al. Paper Summaries • 59 implied HN points • 07 Apr 24
🕹 Technology Artificial Intelligence Machine Learning Data science Programming Software Development
  1. LoRA helps fine-tune large language models without changing all their parameters. It uses two small matrices, which keeps the performance quick during use.
  2. LoRA's updates to weights can miss valuable details you'd get from full fine-tuning, because it treats magnitude and direction together.
  3. DoRA improves on LoRA by separating magnitude and direction, leading to better performance on reasoning tasks and other applications. It works best with smaller settings, making it efficient.