The hottest Programming Substack posts right now

And their main takeaways
Category
Top Technology Topics

The Generalize Step In TDD

Software Design: Tidy First? • 265 implied HN points • 06 Jun 25
🕹 Technology Programming
  1. TDD is not just about writing tests, it also involves thoughtful design. Practitioners understand the balance between testing and creating a good product structure.
  2. Having good examples in TDD practices is really important for understanding and applying concepts effectively. Clear examples can help break down complex ideas.
  3. There is a common misconception that TDD lacks a design phase, but experienced users know that design is a key part of the process. It's essential to integrate design thinking into TDD for better outcomes.

Chip Letter Links No. 16 : Gordon Moore's Notebooks, Nvidia on Acquired, Assembly Language, Jeff Hinton, RISC-V on Jupyter

The Chip Letter • 2839 implied HN points • 16 Apr 23
🕹 Technology Programming
  1. Gordon Moore's notebooks from Fairchild provide a unique insight into his work and research in the early days of computing.
  2. Assembly language, especially 8-bit, was more popular and necessary in the past compared to modern 64-bit architectures.
  3. Nvidia's survival and success were closely tied to their alignment with Moore's Law in the GPU industry.

Language is not enough

Erika’s Newsletter • 412 implied HN points • 11 Apr 23
🕹 Technology Programming
  1. Writing code is a major barrier in lab automation, often leading to less sophisticated protocols created through GUI interfaces.
  2. Natural language is insufficient to accurately represent complex biological protocols, resulting in trial and error to get experiments working.
  3. Programming robots in English may improve user interfaces, but additional challenges remain in making lab automation more effective than human scientists.

Data Science Weekly - Issue 521

Data Science Weekly Newsletter • 339 implied HN points • 17 Nov 23
🕹 Technology Programming
  1. JAX is becoming popular for its speed and capabilities, and learning it may be essential for those familiar with PyTorch. It does have a steeper learning curve, but there are resources to help ease the transition.
  2. The demand for GPUs is skyrocketing, driven by various market factors. Understanding these dynamics can help anticipate the future of technology and resource availability in industries reliant on powerful computing.
  3. Freelancing in data science can lead to an overwhelming number of job offers. Tips on finding clients on platforms like Upwork and LinkedIn can help navigate this new freelance landscape.

Data Science Weekly - Issue 518

Data Science Weekly Newsletter • 379 implied HN points • 27 Oct 23
🕹 Technology Programming
  1. Web development is evolving with the use of local models and technologies for building applications, moving beyond just Python-based machine learning.
  2. It's becoming increasingly important for developers to understand GPUs since they're widely used in deep learning and can greatly enhance performance.
  3. Companies are exploring various use cases for generative AI that provide real value, focusing on practical implementations that drive return on investment.
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The Lightness of Unscheduled Calls

Register Spill • 196 implied HN points • 11 Feb 24
🕹 Technology Programming
  1. Collaboration without elaborate scheduling can feel light and spontaneous, leading to a more open and fluid work environment.
  2. Embracing unscheduled calls and spontaneous pairing sessions can foster better knowledge transfer and idea exchange among team members.
  3. Using tools that support easy and on-the-fly collaboration can significantly impact the culture and productivity of a remote team, making workdays feel full of possibilities rather than meetings.

LangGraph Studio From LangChain

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 03 Jul 24
🕹 Technology Programming
  1. LangGraph helps in creating a flow for conversational applications, allowing for both structured and flexible designs. This means you can manage how chatbots interact without forcing them into a rigid structure.
  2. With LangGraph Studio, users can visualize and control how their AI agents work. It provides tools to track performance, test different scenarios, and optimize interactions effectively.
  3. LangGraph Cloud allows developers to deploy their projects from GitHub and test them in a user-friendly environment. This makes it easier to understand and improve the behavior of AI agents in real-time.

Redis and Python: 20% of effort, 80% of effects

Bite code! • 1467 implied HN points • 03 Mar 24
🕹 Technology Programming
  1. Redis is a powerful companion for Python, used for caching, sharing states, and creating queues.
  2. Redis is easy to use and highly efficient, widely employed in the industry for tasks like caching and inter-process communication.
  3. With basic features like lists, hashes, and sets, Redis has vast utility in tasks such as caches, queues, and inter-process communication.

My Wronggest Content

Jacob’s Tech Tavern • 656 implied HN points • 26 Nov 24
🕹 Technology Programming
  1. Posting wrong code online can lead to getting helpful feedback from others. Sometimes people are quick to point out mistakes, but that can help you learn.
  2. Using social media regularly can grow your audience. Posting interesting and engaging content helps attract more subscribers.
  3. Accepting criticism is important. It can be tough to hear people say your work is bad, but it's a chance to improve and grow.

Disillusioning the Magic of the fork System Call

Confessions of a Code Addict • 649 implied HN points • 26 Nov 24
🕹 Technology Programming
  1. The fork system call creates a new process that is a copy of the parent process, but each can follow a different path after the call. This is why a program can behave differently depending on which process it is in.
  2. When the fork call is made, the operating system needs to return distinct values to both the parent and child processes. The kernel sets the return value for the child process to 0, while the parent gets the child’s process ID.
  3. System calls are handled in the kernel, which means understanding their low-level operations helps us see how programming languages like C manage processes, revealing the complexity hidden behind simple function calls.

Rust vs Python. Problem statement & Python impl

Art’s Substack • 79 implied HN points • 14 May 24
🕹 Technology Programming
  1. Porting a system from Python to Rust led to a significant cost reduction of 1400 times, increased pipeline success rate from 85% to 99.88%, and decreased data availability time from 10 hours to less than 15 minutes.
  2. Moving from reading everything into memory to streaming fashion and eliminating the intermediate JSON format were key improvements in the data processing system.
  3. Python's interpreted nature, dynamic typing, GIL limitations, and multiple packaging options can pose challenges in production systems, making it a less ideal choice for certain needs.

Functional Genie

Software Design: Tidy First? • 243 implied HN points • 08 Jun 25
🕹 Technology Programming
  1. When coding in Rust, it can be tricky to use the right idioms, especially when dealing with functions that return an Option. It's good to pay attention to how you handle these results.
  2. In Rust, the common way to check for values is with an 'if' statement, which can take some getting used to if you're new to the language.
  3. Building a high-quality map implementation in Rust can be performance competitive, but learning the language and its features is essential for success.

A Programmer’s Guide to x86-64 Assembly (Series Overview)

Confessions of a Code Addict • 192 implied HN points • 16 Jul 25
🕹 Technology Programming
  1. Understanding assembly language is important because it helps you see how high-level code runs at the machine level. This knowledge can make you a better programmer.
  2. The series will cover the basics like bits, memory, and CPU instructions, so even beginners can follow along. You'll build skills to read and write real assembly programs.
  3. Future topics will include practical applications like system calls and writing command-line utilities. You'll learn useful skills for tasks beyond just programming.

Finding the CAP theorem of agents

Sunday Letters • 59 implied HN points • 02 Jun 24
🕹 Technology Programming
  1. The CAP theorem shows that in any distributed system, you can only achieve two out of three things: consistency, availability, or partition tolerance. This means when things go wrong, you have to choose which one you're willing to sacrifice.
  2. In AI programming, there's a similar tension between using complex AI models and the need for reliable, deterministic code. Balancing these two aspects is a challenge, much like the early challenges with web applications.
  3. As technology evolves, the understanding and frameworks around these issues may improve. Just like how programmers now design around the CAP theorem, we might see better solutions and choices for AI challenges in the future.

Crafting something beautiful ♥️

André Casal's Substack • 19 implied HN points • 01 Aug 24
🕹 Technology Programming
  1. Improving customer access made it easier for users to start using LaunchFast. Instead of multiple steps, they can now just run one command.
  2. A conversation with Neeraj from BigBinary led to important changes in pricing and marketing strategy for LaunchFast. These adjustments should help clarify its value and appeal more to potential users.
  3. Learning about deploying an NPM package simplified the process of launching LaunchFast. This helped create an efficient script that sets everything up quickly.

Tidy First Example

Software Design: Tidy First? • 463 implied HN points • 30 Jan 25
🕹 Technology Programming
  1. It's important to make hard changes easier before tackling them. This means breaking down tough tasks into simpler steps.
  2. Many people want to make the easy changes they envision, but they often overlook the complexity involved.
  3. Always pay attention to examples in learning; they can provide valuable insights that you might miss if you skim over them.

Ongoing Chialisp Development

Bram’s Thoughts • 373 implied HN points • 02 Sep 23
🕹 Technology Programming
  1. On-chain gaming in Chialisp requires optimization at all levels, supporting few players and fast games.
  2. Games are played inside a 'referee' on chain, with hands over a payment channel for low latency and cost.
  3. Chialisp development differs from Solidity in reentrancy design, capability handling, and tooling maturity.

The Black Spatula Project

Am I Stronger Yet? • 564 implied HN points • 18 Dec 24
🕹 Technology Programming
  1. A mistake in a scientific paper about black plastic utensils showed that math errors can change health implications. This finding led to a community initiative to check past papers for errors.
  2. The project aims to use AI to find mistakes in scientific papers, helping researchers ensure their work is accurate. This could lead to better practices in publishing and scientific research.
  3. Many ideas have emerged for improving how we check scientific work, such as creating tools to validate papers and verify information. The community is in the early stages of exploring these possibilities.

Under the microscope: Michelin Rally Masters (PlayStation)

Rings of Saturn • 43 implied HN points • 02 Dec 25
🕹 Technology Programming
  1. Many published cheats are wrong because the game expects a 13-character code, not the 12-character string that appears on most sites; the correct "unlock everything" code includes a final question mark.
  2. The code system is fairly complex: it uses a 32-character alphabet, three separate checksums, and a PRNG that shifts the alphabet to decode the first ten characters.
  3. Entering a valid code unlocks six bonus cars and enables the WRC and Legends single-player modes, but it doesn’t add any extra drivers beyond those listed in the manual.

Last week at The Lunduke Journal (Nov 24 - Nov 30, 2024)

The Lunduke Journal of Technology • 574 implied HN points • 01 Dec 24
🕹 Technology Programming
  1. The C++ Standards Group made headlines by banning a contributor just for using the word 'Question' in their work. It shows how strict and odd some technical communities can be.
  2. The Linux Code of Conduct Board also banned a developer for not apologizing enough, highlighting tensions in developer communities around behavior expectations.
  3. Microsoft has faced accusations from Google about using 'dark patterns' in their Edge browser, pointing to ongoing issues with user experience and ethical design in tech.

Cadence of Code

Register Spill • 353 implied HN points • 25 Jun 23
🕹 Technology Programming
  1. Retyping other people's writing can help you learn more about their writing style and rhythm.
  2. Actively engaging with code by typing it out can help with better learning and absorption.
  3. Consider typing out pieces of code character by character to understand the rhythm and cadence of the programming language.

MCU land, part 9: ST7789, the final frontier

lcamtuf’s thing • 1836 implied HN points • 09 Sep 23
🕹 Technology Programming
  1. Driving a 2.8" 240x320 IPS panel with a Microchip SAM S70 Cortex-M7 MCU can be surprisingly simple.
  2. The steps to set up the ST7789 display controller involve minimal and straightforward commands like hardware reset and configuration for color mode.
  3. Generating image data for the display in 16 bpp RGB565 color mode is achievable through tools like ImageMagick.

The worst test suites read like word salad after a C++ compiler had a stroke. The best test suites adopt business language.

🔮 Crafting Tech Teams • 99 implied HN points • 10 Apr 24
🕹 Technology Programming
  1. Write tests in plain language aligned with business objectives for better understanding and communication.
  2. Ensure test names are clear and easily interpreted by humans to provide confidence and insight.
  3. Utilize BDD and Jasmine frameworks for more ergonomic testing and improved behavior analysis.

The Best Skillsets to Learn in 2024 for Generative AI

Rod’s Blog • 238 implied HN points • 15 Dec 23
🕹 Technology Programming
  1. Generative AI is a rapidly evolving field creating novel content like images, text, music, etc., with real-world applications from enhancing creativity to helping solve problems.
  2. To succeed in generative AI, you need skills like mathematics and statistics, programming, data science, knowledge of generative AI methods, and creativity in your specific domain.
  3. To learn generative AI in 2024, leverage online courses, books, blogs, tools, and engage in communities and events dedicated to this field.

Making a plan

Register Spill • 334 implied HN points • 04 Jun 23
🕹 Technology Programming
  1. Making a plan before starting a project is crucial for success.
  2. A detailed plan helps in thinking through the tasks and potential challenges.
  3. Writing down tasks and delegating them can improve productivity and efficiency.

Refactoring: Narrow ↔ Widen Parameter

Software Design: Tidy First? • 530 implied HN points • 25 Nov 24
🕹 Technology Programming
  1. Narrowing and widening parameters are important techniques in refactoring code. They help make the code more flexible and easier to understand.
  2. Refactoring can initially seem tricky, but it can also be enjoyable and rewarding. It's a good practice to keep improving your code.
  3. Getting feedback on your refactoring efforts is valuable. It helps you see what works and what can be improved.

RAG Glossary

The Tech Buffet • 179 implied HN points • 21 Jan 24
🕹 Technology Programming
  1. Retrieval Augmented Generation (RAG) helps AI answer questions and generate content. It combines searching through documents with generating relevant answers.
  2. Using RAG can be tricky, especially in production environments. Adjustments may be needed to improve reliability and performance.
  3. Different indexing methods can optimize how RAG retrieves information. This can make it more efficient and effective in finding the right data.

My Claude Code workflow

Aliveness Studies • 13 implied HN points • 12 Jan 26
🕹 Technology Programming
  1. Pay for the Max plan and run multiple model instances so you have enough usage and can parallelize feature work and background tasks.
  2. Use git worktrees (and a helper like worktrunk) plus plan-mode workflows to manage branches, run hooks, spin up per-branch dev servers, and have the model draft and implement features with tests and linting.
  3. Automate end-to-end: let the model ‘do it for me’ to run CLI tools, deploy, update DNS, run headless integration tests, and use browser or interview tools to gather info and fix problems without manual steps.

A Guide to Optimising your Spark Application Performance (Part 2)

SwirlAI Newsletter • 314 implied HN points • 06 Aug 23
🕹 Technology Programming
  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.

The Pythonic Emptiness

Confessions of a Code Addict • 529 implied HN points • 09 Nov 24
🕹 Technology Programming
  1. In Python, you can check if a list is empty by using 'if not mylist' instead of 'if len(mylist) == 0'. This way is faster and is more widely accepted as the Pythonic approach.
  2. Some people find the truthiness method confusing, but it often boils down to bad coding practices, like unclear variable names. Keeping your code clean and well-named can make this style clearer and more readable.
  3. Using 'len()' to check for emptiness isn't wrong, but you should choose based on your situation. The main point is that the Pythonic method isn't ambiguous; it just needs proper context and quality coding.

How to Use AI to Do Stuff: An Opinionated Guide

One Useful Thing • 1801 implied HN points • 15 Jul 23
🕹 Technology Programming
  1. Increasingly powerful AI systems are being released rapidly without proper user documentation.
  2. The major Large Language Models in use currently are GPT-3.5, GPT-4, Bard, Pi, and Claude 2.
  3. AI can assist with writing, generating images, coming up with ideas, making videos, and working with documents and data, but users must be cautious of biases and ethical concerns.

An Unreachable Hidden XKCD Easter Egg inside CPython

Confessions of a Code Addict • 505 implied HN points • 18 Nov 24
🕹 Technology Programming
  1. CPython, the Python programming language's code base, has hidden Easter eggs inspired by the xkcd comic series. One well-known example is the 'import antigravity' joke.
  2. There's a specific piece of unreachable code in CPython that uses humor from xkcd. When this code is hit during debugging, it displays a funny error message about being in an unreachable state.
  3. In the release builds of CPython, the unreachable code is optimized to let the compiler know that this part won't be executed, helping improve performance.

Accelerate by years part IV - The prototype

Opral (lix & inlang) • 19 implied HN points • 23 Jul 24
🕹 Technology Programming
  1. Using SQLite can really speed up the development of both inlang and lix. This saves a lot of time on needing to create complex systems.
  2. Lix 1.0 is coming soon, with simple plugins that can manage changes easily. This makes it easy for apps to work with changes directly.
  3. The next steps involve building a user interface for merging data and creating a plugin for inlang. This should help make the system more efficient.

Accelerate by years part III - Lix on SQLite

Opral (lix & inlang) • 19 implied HN points • 23 Jul 24
🕹 Technology Programming
  1. Building lix without relying on Git can simplify the process. This means avoiding the complications that come with Git's file-based storage model.
  2. Using SQLite for storing data will solve many problems like concurrency and data integrity. It makes it easier to manage application data compared to handling everything through Git.
  3. The main requirements for lix 1.0 will be a merging function and a plugin for inlang. This will open up opportunities for third-party developers to create new lix applications.

Accelerate by years part I - Inlang directories as lix repositories

Opral (lix & inlang) • 19 implied HN points • 23 Jul 24
🕹 Technology Programming
  1. Making inlang directories work as independent repositories can speed up the development process significantly. This means less reliance on GitHub and fewer complications.
  2. Smaller, self-contained inlang repositories require less hosting and have lower scalability needs. This makes it easier to manage and use them without needing a lot of resources.
  3. With control over push, pull, and commit actions, developers can streamline their workflows. This helps avoid many frustrating issues related to traditional version control systems.

GroupBy #35: The Netflix Data Engineering Stack, Atlassian - Evolve the data platform with a Deployment Capability

VuTrinh. • 59 implied HN points • 14 May 24
🕹 Technology Programming
  1. Netflix has a strong data engineering stack that supports both batch and streaming data pipelines. It focuses on building flexible and efficient data architectures.
  2. Atlassian has revamped its data platform to include a new deployment capability inspired by technologies like Kubernetes. This helps streamline their data management processes.
  3. Migrating from dbt Cloud can teach valuable lessons about data development. Companies should explore different options and learn from their migration journeys.

Python Plotly Mapping Magic: Better Results With GPT-4 Prompting

Data at Depth • 59 implied HN points • 13 May 24
🕹 Technology Programming
  1. GPT-4 can be useful for generating data cleaning and visualization code in Python when combined with libraries like pandas and plotly
  2. Using GPT-4, you can learn how to clean datasets, create choropleth maps, and even animated choropleth maps to visualize data over time
  3. Interactive geospatial data visualizations that tell stories over time can be quickly created with Plotly by using GPT-4 prompts

Data Science Weekly - Issue 512

Data Science Weekly Newsletter • 299 implied HN points • 14 Sep 23
🕹 Technology Programming
  1. Nvidia has been a leader in AI technology, but its dominance might not last. Changes in the market and technology could shift the competitive landscape soon.
  2. For those who know R and want to learn Python, there are resources available to help make the transition easier. These resources provide advice and tips catered to R users.
  3. Reinforcement Learning with Human Feedback (RLHF) is an important part of training large language models. It's essential for improving how these models understand and respond to human preferences.