The hottest Programming Substack posts right now

And their main takeaways
Category
Top Technology Topics

What the Hell Am I Doing Here?

Meanwhile, on the other side of my brain... • 99 implied HN points • 27 May 24
🕹 Technology Development Programming Gaming YouTube Projects
  1. The author faced a challenging recovery period after a car accident, including cognitive issues that persisted for months.
  2. Despite challenges, the author is enthusiastic about returning to writing, programming, and sharing knowledge on game development and tools.
  3. The author plans to expand the newsletter's topics, including sharing stories, project updates, and insights, and to create more video content.

Pytest is All You Need

LLMs for Engineers • 79 implied HN points • 12 Jun 24
🕹 Technology Software Programming AI Testing Development
  1. Pytest is a great tool for evaluating LLM applications, making it easier to set up tests and check their performance. It allows you to program your own evaluation metrics directly in Python without needing complicated configurations.
  2. You can easily collect and analyze data from multiple test runs using Pytest. This helps to understand how consistent the outputs are across different evaluations.
  3. The examples show how to compare different prompts and LLM models, enhancing the flexibility and variety in testing. This allows you to see which setups work best in various scenarios.

Can GPT-4 *Actually* Write Code?

Tyler Glaiel's Blog • 567 HN points • 17 Mar 23
🕹 Technology Artificial Intelligence Coding Algorithms Machine Learning Programming
  1. GPT-4 can write code when given existing algorithms or well-known problems, as it remixes existing solutions.
  2. However, when faced with novel or unique problems, GPT-4 struggles to provide accurate solutions and can make incorrect guesses.
  3. It's crucial to understand that while GPT-4 can generate code, it may not be reliable for solving complex, new problems in programming.

How Programming Intelligence Changes the Role of AI

The API Changelog • 3 implied HN points • 11 Jun 25
🕹 Technology AI Programming Software Engineering Data
  1. AI systems, especially large language models, are often unpredictable and work like black boxes, which can be a big problem in important fields like medicine or finance.
  2. To make AI more controllable, we can set limits on what it can do and ensure it asks for our approval before taking actions, giving us more confidence in its decisions.
  3. Making AI systems programmable means we understand what they do better, helping us regain control and turn AI into a tool that supports our goals instead of taking charge.
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Cellar Door

Logos • 19 implied HN points • 13 Aug 24
🕹 Technology AI Development Web Apps Coding Programming User Experience
  1. The project, Cellar Door, aims to find the most beautiful word in English by using a voting system based on people's preferences. It's a fun way to see which words people like the most.
  2. They initially struggled with a word list that included silly terms, but switched to a more reliable source to ensure the app only features valid words. The process of cleaning up the data is ongoing.
  3. The use of AI tools like OpenAI's API has made coding easier and more efficient for developing apps. However, there's still a need for better platforms to help non-technical users create their own apps with less confusion.

OOP in Python: Why is object-oriented programming so important?

Mostly Python • 1257 implied HN points • 06 Jul 23
🕹 Technology Programming
  1. Object-oriented programming (OOP) is important because it stores information and actions in one place.
  2. OOP is powerful for getting work done efficiently, as shown by the ease of creating and working with objects in Python.
  3. Even if you don't write classes often, understanding OOP in Python can make you a better programmer since everything in Python is an object.

Links: February 2024 edition

Blog System/5 • 496 implied HN points • 29 Feb 24
🕹 Technology Software Programming Operating Systems Web Browsers Tools
  1. The post summarizes interesting articles, videos, and projects from February 2024 with added commentary to urge readers to explore the content.
  2. There are discussions on topics like old hardware databases, software development reflections, and the challenges of modern software bloat.
  3. The author explores topics like breaking memory limitations in DOS, DJGPP running GNU programs on DOS, and the creation of a library in Rust for implementing memory vulnerabilities.

This will be easy

Bite code! • 1223 implied HN points • 08 Jul 23
🕹 Technology Programming Networking Error Handling Logging
  1. Making HTTP POST requests can have unexpected challenges, like dealing with network issues and corporate setups.
  2. Using ThreadExecutor and ThreadPoolExecutor in Python can help manage tasks efficiently, especially in scenarios like log aggregation.
  3. Error handling is crucial in programming, and sometimes unconventional solutions are needed to manage exceptions effectively.

Data Science Weekly - Issue 542

Data Science Weekly Newsletter • 139 implied HN points • 12 Apr 24
🕹 Technology Data science AI Machine Learning Programming Analytics
  1. This newsletter provides links and updates about data science, AI, and machine learning. It's a helpful resource for anyone wanting to stay informed in this field.
  2. One article teaches how to handle real questions using Python, which is great for people wanting practical coding skills. Another discusses techniques to make sure AI outputs stay on task.
  3. The newsletter also features resources and courses to help people learn and improve their skills in data science and related areas. It's a good place to find learning opportunities.

My Journey into Personal Computer Software Development in 1983

Farrs’s Substack • 125 HN points • 20 Apr 24
🕹 Technology Software Development Programming Management Bug Fixing
  1. Personal Computers were gaining popularity in 1983, despite being considered toys by some programmers, and had promising applications developed for them.
  2. Taking a risk to work in Personal Computer Software Development led to a successful job offer and opportunity to solve a challenging memory limitation issue.
  3. Facing skepticism and disrespect at the company, the individual showcased exceptional bug-solving abilities, but ultimately chose to leave due to being labeled unfairly.

LangChain Chatbot Framework With Retrievers

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 99 implied HN points • 07 May 24
🕹 Technology AI Chatbots Development Software Programming
  1. LangChain helps build chatbots that can have smart conversations by using retrievers for specific information. This makes chatbots more useful in different fields.
  2. Retrievers are tools that find documents based on user questions, providing relevant information without needing to store everything. They help the chatbot give accurate answers.
  3. A step-by-step example shows how to use LangChain with Python, making it easier to create a chatbot that answers user inquiries based on real-time data.

Harmony, melody, and coding with AI

Sunday Letters • 39 implied HN points • 07 Jul 24
🕹 Technology AI Programming Software Innovation Engineering
  1. We are experiencing a shift in programming that changes how we interact with code and AI. Just like moving from desktop to cloud, this new way will come with challenges and need new thinking.
  2. Combining traditional coding with AI models is important. It's like writing music where the code provides a solid structure, while AI adds creativity and flexibility.
  3. To succeed in this new environment, programmers should keep learning and adapting, using both past knowledge and new technologies carefully together.

Data Science Weekly - Issue 536

Data Science Weekly Newsletter • 179 implied HN points • 01 Mar 24
🕹 Technology Data science AI Machine Learning Programming Statistics
  1. The DSPy framework makes working with large language models easier by focusing on programming instead of complex prompting techniques. This helps reduce errors and improves usability.
  2. A new sequence model approach shows better performance than traditional Transformers, especially for long data sequences. It also works faster, making it a promising development in the field.
  3. Learning resources like online courses and free books on deep learning and causal ML can help deepen understanding of data science. They provide structured material that is great for both beginners and advanced learners.

A quick introduction to Memory Pools [Math Mondays]

Technology Made Simple • 179 implied HN points • 27 Feb 24
🕹 Technology Memory management Software Engineering Optimization Performance Programming
  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.

Language is not enough

Erika’s Newsletter • 412 implied HN points • 11 Apr 23
🕹 Technology Automation Programming AI Metadata
  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 Data science Machine Learning AI Programming Analytics
  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 Data science Machine Learning Artificial Intelligence Programming Data Engineering
  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.

The Lightness of Unscheduled Calls

Register Spill • 196 implied HN points • 11 Feb 24
🕹 Technology Collaboration Remote work Culture Communication 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 AI Software Development Cloud 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.

Easy Robotics with a 3D Printer and Raspberry Pi

Messy Progress • 59 implied HN points • 10 Feb 25
🕹 Technology Robotics 3D Printing Programming DIY Projects Electronics
  1. Making a robot with a 3D printer and a Raspberry Pi is fun and can be done on a budget. You can create many different designs without limits.
  2. A modular design is helpful because it allows for easy changes and quick fixes. This makes it easier for kids to participate and experiment.
  3. Using a Raspberry Pi for controlling your robot opens up many possibilities, like adding cameras or other fun components. You can even use simple coding to operate it.

Rust vs Python. Problem statement & Python impl

Art’s Substack • 79 implied HN points • 14 May 24
🕹 Technology Programming Comparison Performance Tools Benchmarking
  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.

Finding the CAP theorem of agents

Sunday Letters • 59 implied HN points • 02 Jun 24
🕹 Technology Software AI Distributed Systems Engineering 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.

Measuring Lines/Function

Software Design: Tidy First? • 132 implied HN points • 05 Dec 24
🕹 Technology Software Design Programming Data Analysis Development Practices
  1. Measuring lines of code in functions can be more complicated than expected. It's helpful to keep track of this while working on software projects.
  2. Looking for patterns in software, like Pareto distributions, can provide valuable insights. It's good practice to analyze your own code for these patterns.
  3. Documenting your findings is important. Sharing your experiences can help others who are trying to understand their software better.

Crafting something beautiful ♥️

André Casal's Substack • 19 implied HN points • 01 Aug 24
🕹 Technology Software Development Product Design User Experience Entrepreneurship 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.

Get the most out of Python dicts

Bite code! • 978 implied HN points • 13 Jun 23
🕹 Technology Programming
  1. Merge dictionaries with methods like dict.updates(), **, |, and collections.ChainMap
  2. Deal with missing values in dictionaries using methods like dict.get(), dict.setdefault(), and collections.defaultdict
  3. Extract multiple values at once using tools like operator.itemgetter and match/case

Cadence of Code

Register Spill • 353 implied HN points • 25 Jun 23
🕹 Technology Coding Learning 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.

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 Testing Programming Automation
  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 AI Programming Machine Learning Data science Resources
  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.