The hottest Programming Substack posts right now

And their main takeaways
Category
Top Technology Topics

Running GNU on DOS with DJGPP

Blog System/5 • 4 HN points • 14 Feb 24
🕹 Technology Programming
  1. DJGPP is a port of GNU development tools to DOS, challenging the limited memory and architecture of DOS systems.
  2. DJGPP's tooling was free and provided a complete development environment with Unix heritage, leading to differences in behavior from other DOS compilers.
  3. DJGPP faced challenges like running 32-bit programs on the 16-bit DOS operating system, dealing with large buffers, and handling Unix-style paths on DOS.

Malloy Data

Why Now • 5 implied HN points • 26 Oct 23
🕹 Technology Programming
  1. Malloy is a new query language for describing data relationships and transformations in SQL databases.
  2. Malloy compiles to SQL optimized for your database, has a semantic data model and query language, excels at reading and writing nested data sets, and handles complex queries seamlessly.
  3. Malloy also introduces a semantic layer similar to Looker, allowing for saving calculations like measures and defining dimensions to describe and transform data.

robotfindskitten, part 1

Dan’s MEGA65 Digest • 5 implied HN points • 13 Oct 23
🕹 Technology Programming
  1. MEGA65 is introducing a new hardware-accelerated keyboard scanner for faster and more accurate typing experiences.
  2. New games like Classy and MEGApple are being developed for MEGA65, adding variety and entertainment for users.
  3. robotfindskitten, a popular game originally created in 1997, is being revisited and recreated for modern platforms, offering a fun and nostalgic experience.

ai gold rush.

a quest for knowledge • 1 HN point • 22 Feb 23
🕹 Technology Programming
  1. The AI landscape is evolving, with the shift from AI to AGI causing excitement.
  2. AI tools like Copliot can be useful but may require careful review to ensure quality.
  3. The widespread use of AI in different aspects of life may have implications on creativity and authenticity.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 297

Data Science Weekly Newsletter • 19 implied HN points • 01 Aug 19
🕹 Technology Programming
  1. Integrating data science teams within companies can help improve collaboration and effectiveness. It's important to explore different models to find what works best.
  2. Automated thinking may lead to overdependence on AI, which can cause us to miss critical thinking skills. We should be cautious about relying too much on technology.
  3. Understanding how machine learning models work is crucial for building trust. New techniques are emerging that can help explain complex models better.

ChatGPT vs StackOverflow

Arthur's Newsletter • 1 HN point • 11 May 23
🕹 Technology Programming
  1. The author compares using ChatGPT and StackOverflow for getting programming-related help.
  2. ChatGPT provided tailored, step-by-step answers more efficiently than StackOverflow for specific programming tasks.
  3. ChatGPT helped the author quickly identify and solve a version mismatch issue, saving time compared to traditional research methods.

Stubs in Maineffectjs

Arvind’s Substack • 1 HN point • 17 May 23
🕹 Technology Programming
  1. In Maineffectjs, stub dependencies faster to test specific functionalities.
  2. The new 'stub' function in Maineffectjs automatically creates mocks for you, reducing complexity.
  3. Maineffectjs allows for flexible stub implementations, such as Jest or Sinon, to enhance testing capabilities.

Compliance with EIP-2535 Diamonds Standard

EIP-2535 Diamonds • 7 implied HN points • 21 Mar 23
🕹 Technology Programming
  1. The EIP-2535 Diamonds standard took 4 years to develop and optimize for flexibility and interoperability.
  2. To comply with EIP-2535, a smart contract must implement specific functions outlined in the standard.
  3. The `diamondCut` upgrade function in EIP-2535 is optional, but implementing it enables interoperability with existing and future tooling.

The Lure of Graphical Programming

Bzogramming • 7 implied HN points • 13 Mar 23
🕹 Technology Programming
  1. Visual programming languages with colored boxes and lines may not necessarily make code easier to understand.
  2. Human vision focuses on categorizing small pieces of images at a time, similar to how code should be structured.
  3. Text-based programming already utilizes spatial conveyance of meaning through features like indentation, highlighting the importance of enhancing visual tools in coding.

Data Science Weekly - Issue 289

Data Science Weekly Newsletter • 19 implied HN points • 06 Jun 19
🕹 Technology Programming
  1. Machine learning can create lifelike animations from just one photo, which is both impressive and a little creepy.
  2. The AI industry relies on a lot of hidden human labor, often in poor conditions, as it grows and changes how businesses operate.
  3. Training large AI models can be very harmful to the environment, producing as much carbon emissions as five cars over their lifetime.

Sensory Fragmentation

Bzogramming • 7 implied HN points • 27 Feb 23
🕹 Technology Programming
  1. Engage more of your brain by involving multiple senses in your work.
  2. Avoid sensory junk food like music or sludge content that provide little relevant structure to your tasks.
  3. Prioritize a multisensory approach to computing interfaces to make work more engaging and productive.

Bloom Filters and Beyond: An Illustrated Introduction and Implementation

Confessions of a Code Addict • 5 HN points • 05 Sep 23
🕹 Technology Programming
  1. Bloom filters are efficient data structures for quick searches in large datasets and minimize memory usage, with a probabilistic approach to determining membership
  2. Bloom filters use hash functions and bit vectors to store data item membership information while conserving memory by not storing actual items
  3. Counting Bloom Filters are an extension that allow item deletion but come with weaknesses such as handling hash collisions and counter overflow, providing an advanced data handling tool

Data Science Weekly - Issue 286

Data Science Weekly Newsletter • 19 implied HN points • 16 May 19
🕹 Technology Programming
  1. Los Angeles has significant noise pollution, mainly from airports and heavy traffic. A recent map highlights how loud different neighborhoods are.
  2. There's a growing debate on whether data can truly act as a competitive advantage for companies, especially with AI startups. It's worth questioning if real evidence supports this idea.
  3. A Swedish distillery is set to release the first whisky designed by artificial intelligence. It raises interesting questions about how AI can influence creative processes.

Data Science Weekly - Issue 284

Data Science Weekly Newsletter • 19 implied HN points • 02 May 19
🕹 Technology Programming
  1. Research on reinforcement learning is showing that agents can learn as quickly as humans by combining fast and slow learning techniques.
  2. Insurance and healthcare companies can use pictures of houses to better predict risk and improve their models.
  3. Artificial intelligence could help in designing buildings by providing new insights and alternative strategies for floor plans.

"Tf is a kernel?"

Aaron’s Blog • 6 implied HN points • 30 Apr 23
🕹 Technology Programming
  1. When working with Python, it's better to create separate project environments and add dependencies as needed to avoid conflicts and keep things organized.
  2. Understanding the role of a kernel in a computer - it helps software interact with physical components like keyboard and display.
  3. Users may find it helpful to engage in conversational learning, like Q&A sessions, to grasp complex technical concepts more easily.

Local Llama

ScaleDown • 5 implied HN points • 15 Aug 23
🕹 Technology Programming
  1. Running Local Llama models can be cost-effective compared to using commercial APIs, making AI more accessible to a broader range of users.
  2. By deploying LLMs locally, users have more control over the model, allowing them to bypass limitations and ensure efficient resource utilization.
  3. Local deployment of LLMs enhances privacy and security by keeping data on the user's machine, providing an additional layer of protection.

Data Science Weekly - Issue 278

Data Science Weekly Newsletter • 19 implied HN points • 21 Mar 19
🕹 Technology Programming
  1. AI development can lead to positive outcomes, so it's valuable to ask what could go right instead of just focusing on the risks.
  2. New AI techniques, like using GANs, can create exciting content, such as realistic dance videos of athletes.
  3. Reducing the need for labeled data is a key challenge in deep learning, and finding ways to tackle it can enhance model training.

Data Science Weekly - Issue 277

Data Science Weekly Newsletter • 19 implied HN points • 14 Mar 19
🕹 Technology Programming
  1. Data science teams perform better with generalists instead of specialists. This approach helps teams adapt and innovate rather than just focusing on increasing productivity.
  2. R is a powerful programming language for data analysis, with many surprising capabilities beyond statistics. It has features that can impress even those in the computer science field.
  3. China is expected to surpass the U.S. in AI research output soon. This shift highlights the increasing importance of global competition in technology and research.

CPython Object System Internals: Understanding the Role of PyObject

Confessions of a Code Addict • 4 HN points • 23 Nov 23
🕹 Technology Programming
  1. Programming languages use polymorphism and inheritance to create unified interfaces between modules.
  2. CPython mimics inheritance and polymorphism in C by embedding a parent struct in the child struct definitions, ensuring a unified memory layout.
  3. In CPython, the PyObject struct acts as the parent struct, containing fields for object reference count and type-related data, enabling type-specific method calls via function pointer tables in the object header.

Data Science Weekly - Issue 275

Data Science Weekly Newsletter • 19 implied HN points • 28 Feb 19
🕹 Technology Programming
  1. Artificial intelligence can help humans discover things we couldn't find on our own, making it a powerful tool in various fields.
  2. Creating a strong data science portfolio and tailored resume is crucial for job seekers in the data science field to stand out to potential employers.
  3. Machine learning can significantly improve the efficiency and value of renewable energy sources like wind power, showcasing its practical applications.

Demystifying GPUs for CPU-centric programmers

The AI Observer • 3 implied HN points • 14 Mar 24
🕹 Technology Programming
  1. GPUs are essential for modern compute, especially with the rise of AI workloads like large language models that heavily rely on tensor operations like matrix addition and multiplication.
  2. When working with GPUs, programmers use CUDA to define functions called kernels that can be launched on the GPU. Parallelism is explicitly defined and optimized, unlike in CPUs where loops iterate serially over data sets.
  3. Execution on GPUs differs from CPUs due to the minimal cost of GPU hardware threads, efficient thread scheduling at the hardware layer, and the use of warps to execute parallel instructions. GPUs optimize for high throughput with many hardware threads, while CPUs focus on low latency for individual instructions.

Data Science Weekly - Issue 269

Data Science Weekly Newsletter • 19 implied HN points • 17 Jan 19
🕹 Technology Programming
  1. Neural networks can be hard to understand, and researchers are exploring how to better interpret what they learn during training.
  2. In 2018, Google made significant advancements in AI research, and there's a lot for the community to reflect on and build upon going forward.
  3. Data science project flows can vary, and it's helpful for teams to structure their projects in ways that fit their unique challenges and goals.

Unnecessary Complicated Calculator

Judson’s Substack • 5 implied HN points • 10 Jul 23
🕹 Technology Programming
  1. Encountering issues with code? Sometimes a fresh start can solve the problem.
  2. Creating a calculator program sometimes involves defining variables and functions in a specific order.
  3. To handle different mathematical operations, use conditional statements like if-else based on user input.

Week #6 - Python #2

Judson’s Substack • 5 implied HN points • 03 Jul 23
🕹 Technology Programming
  1. Object-Oriented Programming (OOP) is like creating blueprints and instances that can be reused.
  2. In Python, you can create classes with attributes like shape and flavor, and then create instances like my_cookie or your_cookie.
  3. The concept is similar to the Aristotelian distinction between universal (class/blueprint) and particulars (instances).

Week 4 - Memory #3

Judson’s Substack • 5 implied HN points • 21 Jun 23
🕹 Technology Programming
  1. Copying strings in C requires explicit commands for each function or command.
  2. Allocating memory using malloc in C allows for flexible memory management.
  3. Freeing allocated memory using free() in C prevents memory leaks and segmentation faults.

Week #4 - Memory

Judson’s Substack • 5 implied HN points • 20 Jun 23
🚌 Education Programming
  1. In C, 'string' doesn't exist on its own, but is represented by 'char *' in the cs50.h library.
  2. Using pointers and pointer arithmetic in C helps in accessing and iterating through addresses in memory.
  3. When comparing strings in C, remember to use 'strcmp' instead of '==' to check if the contents are the same.

Once a Maintainer: Michael Grosser

Once a Maintainer • 5 implied HN points • 20 Jun 23
🕹 Technology Programming
  1. Michael Grosser started with programming by experimenting and modifying scripts, which led him to become a programmer.
  2. He transitioned from media informatics to software engineering due to a preference for programming over animation.
  3. Open source contributions like single_cov and parallel were born out of frustrations with existing tools and workflows.

Bazel: Aspirations and Realities

Subsack • 3 HN points • 22 Feb 24
🕹 Technology Programming
  1. Bazel can be amazing for bigger projects, but setting it up takes a lot of time, which startups often don't have. It's crucial to focus on building a product quickly before diving into complex systems.
  2. Using Bazel with languages like Python and JavaScript can be tricky because they aren't as well supported. It can lead to a lot of wasted effort if you're not careful about the tools you choose.
  3. While Bazel has great potential, it's often not the right choice for startups due to the time and resources needed. It's better to find a simpler solution until you have a stable system.

Data Science Weekly - Issue 261

Data Science Weekly Newsletter • 19 implied HN points • 22 Nov 18
🕹 Technology Programming
  1. AI tools are becoming more accessible, and new tools will help make AI more like general computing. This change could allow more people to work with AI easily.
  2. There's a strong need for better testing in data science, similar to what software developers do. Good testing can help avoid big problems from data errors.
  3. Deep learning is being explored in exciting new ways, such as detecting diseases in X-rays. These advancements could lead to better healthcare solutions.

Improve your SQL skills X2 in 5 minutes

Leading Developers • 3 HN points • 13 Feb 24
🕹 Technology Programming
  1. SQL skills are crucial for managers because they can help answer business questions, understand technical designs, and provide a huge return on effort invested.
  2. Don't stop with just learning joins in SQL. Advancing to using CTEs, window functions, and partitions can greatly enhance your ability to write complex queries.
  3. Window functions in SQL, such as ranking functions, aggregation functions, and positional functions, can help in advanced query writing by allowing calculations across sets of rows or returning a single value from a specific row within partitions.

Unlocking the Power of DALL·E 3 in C#: A Comprehensive Guide for .NET Enthusiasts

The AI Observer • 3 implied HN points • 14 Feb 24
🕹 Technology Programming
  1. DALL-E 3 in C# allows for high-quality image generation from textual descriptions with unique features like text incorporation, landscape/portrait compatibility, and intricate prompt interpretation
  2. Implementing DALL-E 3 in C# requires understanding API parameters and making adjustments like model selection, image dimensions, and quality for tailored image generation
  3. To avoid rate limit issues, consider upgrading plans for higher limits and be mindful of pricing details for different image quality options with DALL-E 3 in C#

Whatever happened to Elm, anyway?

derw • 6 HN points • 13 Feb 23
🕹 Technology Programming
  1. Elm's community size has grown over the years, with increasing audience and engagement.
  2. Elm's slower release cycle and emergence of alternative technologies like TypeScript have contributed to its stagnant growth.
  3. Elm's unique architecture and niche status may no longer provide a compelling reason for adoption compared to other frameworks like Svelte or Vue.