The hottest Programming Substack posts right now

And their main takeaways
Category
Top Technology Topics

Novitate

Vic's Verdict • 4 implied HN points • 01 Oct 23
⛪ Faith & Spirituality Programming
  1. Conformity in the contemporary paradigm is rooted in fear, but we can renew our minds by understanding and using Girard's mimetic theory to shift beliefs from fear to faith.
  2. Our beliefs are shaped by causal mechanisms such as mimetic desire and programming, as well as acausal mechanisms that introduce new ideas seemingly out of nowhere.
  3. To combat fear and find faith, we must actively question our beliefs, select positive mimetic models, filter mental models, and work on deprogramming negative conditioning through reflection and manipulation of our unconscious beliefs.

Data Science Weekly - Issue 258

Data Science Weekly Newsletter • 19 implied HN points • 01 Nov 18
🕹 Technology Programming
  1. Reinforcement learning agents can now explore better with curiosity-driven methods, helping them perform beyond human levels in certain games.
  2. Machines can simulate dreaming by recognizing patterns like the human brain, allowing them to create unique visual outputs without direct input.
  3. Choosing the right data science projects is crucial; a good strategy can lead to valuable results while a poor one may just waste resources.

Data Science Weekly - Issue 256

Data Science Weekly Newsletter • 19 implied HN points • 18 Oct 18
🕹 Technology Programming
  1. The Big Mac Index, which used to be calculated manually, is now done using the R programming language. This change promotes transparency in how data is gathered and shared in journalism.
  2. Compression might become a key application for machine learning on devices like phones. Many people are surprised to learn that it can significantly improve performance in this area.
  3. There is a growing trend of AI chatbots providing medical advice, which raises questions about their effectiveness compared to human doctors.

State Farming

FREST Substack • 2 HN points • 14 Jul 24
🕹 Technology Programming
  1. Coding can be seen as managing bits of information, or 'state', rather than just writing long programs. This means we need to handle and connect these pieces carefully to avoid complicated issues.
  2. Using coding languages that are too complex can introduce many problems like bugs and slow performance. It's better to use simpler methods when possible to make our code cleaner and easier to maintain.
  3. Relying more on databases and simpler query languages can help us streamline our coding. This way, we can focus on essential computations and reduce the amount of complex code we need to write.

Data Science Weekly - Issue 255

Data Science Weekly Newsletter • 19 implied HN points • 11 Oct 18
🕹 Technology Programming
  1. The ML Engineering Loop helps engineers improve their model development by following a cycle of analyzing, selecting approaches, implementing, and measuring. This cycle allows them to quickly find the best solutions.
  2. Understanding uncertainty in data visualizations is important, and integrating uncertainty estimates can improve how we interpret plots and models. This can lead to better decision-making based on data.
  3. Using tools like TensorFlow.js for practical applications, such as object recognition in games, shows how machine learning can be fun and engaging. These examples help in learning and applying complex concepts in a creative way.
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Data Science Weekly - Issue 253

Data Science Weekly Newsletter • 19 implied HN points • 27 Sep 18
🕹 Technology Programming
  1. Uber uses forecasting with machine learning and deep learning to enhance its products and services. This means they can predict customer needs better and improve their offerings based on accurate data.
  2. Deep learning is changing software development by requiring fewer lines of code. Instead of writing complicated rules, developers set a foundation and let the system learn from examples.
  3. AI is being influenced by how we sense smell, leading to advancements in both biology and technology. Understanding chemical information can help create more sophisticated AI systems.

Data Science Weekly - Issue 250

Data Science Weekly Newsletter • 19 implied HN points • 06 Sep 18
🕹 Technology Programming
  1. There's a growing need for data scientists in the U.S. now that there's a shortage, which is a big change from just a few years ago when there were too many people in the field.
  2. New approaches in machine learning, like unsupervised machine translation, are making it easier to provide fast and accurate translations in many languages, helping people connect better.
  3. Researchers are looking into how small changes in images can confuse computer vision models, and they wonder if the same happens to humans, pointing out potential vulnerabilities in both AI and human vision.

Data Science Weekly - Issue 247

Data Science Weekly Newsletter • 19 implied HN points • 16 Aug 18
🕹 Technology Programming
  1. Data science is an evolving field, and experts suggest there's still much to learn and improve upon.
  2. New tools and resources, like machine learning platforms, can help workers identify skills they need to develop.
  3. Collaboration between industry and academia can drive more innovation in artificial intelligence.

Once a Maintainer: Benji Nguyen

Once a Maintainer • 5 implied HN points • 28 Apr 23
🕹 Technology Programming
  1. Benji Nguyen started programming after leaving medical school and discovering a passion for it.
  2. Erdtree, a multi-threaded filesystem tool in Rust, was born out of boredom and the desire to create a modern alternative to an old program.
  3. Getting more people into open source involves educating them on engagement etiquette and encouraging empathy for fellow programmers.

Data Science Weekly - Issue 246

Data Science Weekly Newsletter • 19 implied HN points • 09 Aug 18
🕹 Technology Programming
  1. Balancing quick changes and long-term planning is tough in data science, and it's important to find ways to adapt without losing sight of the bigger picture.
  2. Coca-Cola successfully used advanced technology like TensorFlow for its marketing efforts, showcasing how big companies can leverage data science for effective campaigns.
  3. Automated machine learning tools, like AutoKeras, help people without deep technical skills to use powerful machine learning models easily.

Data Science Weekly - Issue 244

Data Science Weekly Newsletter • 19 implied HN points • 26 Jul 18
🕹 Technology Programming
  1. Companies should define data science roles using three tracks: Analytics, Inference, and Algorithms. This helps meet business needs more effectively.
  2. Google's AutoML is a tool that automates machine learning processes, tapping into transfer learning to enhance capabilities and ease of use.
  3. Multi-task learning allows machines to learn multiple tasks at once, making them smarter and better at handling complex problems, similar to how humans learn.

Data Science Weekly - Issue 243

Data Science Weekly Newsletter • 19 implied HN points • 19 Jul 18
🕹 Technology Programming
  1. AI might be able to replace some animal testing by predicting chemical toxicity. This could make testing faster and more ethical.
  2. Understanding what machine learning practitioners do is key to improving their training and tools. This could help more people get into the field of machine learning.
  3. The Netflix workshop highlighted that traditional recommendation methods might be outdated. New techniques are needed to keep up with changing user preferences.

*Bonus* Remaking Pokemon Yellow in Godot

Why Now • 5 implied HN points • 12 Apr 23
🕹 Technology Programming
  1. The article discusses remaking Pokemon Yellow in Godot game engine.
  2. The author talks about creating the main character, Pallet Town, and a menu in the game.
  3. The development process involves working with Godot's scripting language, creating custom art assets, and implementing game logic.

Data Science Weekly - Issue 240

Data Science Weekly Newsletter • 19 implied HN points • 28 Jun 18
🕹 Technology Programming
  1. AI has become very powerful, even beating expert humans in complex games like Dota 2. This shows how quickly technology is advancing.
  2. Data science can play a meaningful role in addressing social issues, like the problem of public human waste in cities. Mixing social science with data could lead to helpful solutions.
  3. Building a data dictionary is crucial for teams, as it helps clarify key terms and metrics. This can greatly improve communication and reduce confusion within a business.

TypeScript 5.5 release candidate

Andrew's Substack • 2 HN points • 09 Jun 24
🕹 Technology Programming
  1. TypeScript 5.5 introduces inferred type predicates, improving variable type tracking through code, even when dealing with undefined values.
  2. Control flow narrowing for constant indexed access in TypeScript 5.5 allows for safer type handling when accessing object properties.
  3. TypeScript 5.5 now supports type imports in JSDoc, making it easier to import types for type-checking in JavaScript files.

Data Science Weekly - Issue 234

Data Science Weekly Newsletter • 19 implied HN points • 17 May 18
🕹 Technology Programming
  1. Teaching AI about cause and effect can help make it smarter and more intelligent. Understanding the 'why' behind actions is crucial for progress.
  2. Self-driving technology is advancing, as seen with MIT's new car that can drive on roads it has never seen before using basic GPS and sensors.
  3. There are resources available to help people start a career in data science, including guides on building a portfolio and creating a standout resume.

Data Science Weekly - Issue 232

Data Science Weekly Newsletter • 19 implied HN points • 03 May 18
🕹 Technology Programming
  1. Using machine learning can be made easier and more accessible through tools like Google's Teachable Machine, which provides useful UX insights.
  2. Deep learning techniques are being adapted for different types of data, including enhancing performance in models working with tabular data.
  3. Focusing on good data practices and proper processes is key for startups looking to build a strong data science platform.

Data Science Weekly - Issue 230

Data Science Weekly Newsletter • 19 implied HN points • 19 Apr 18
🕹 Technology Programming
  1. You can learn how to become a data scientist with specific guides focused on gaps in knowledge, portfolio building, and resume writing.
  2. There are fun projects in AI, like training models to recognize dogs or create cartoons, showing how diverse applications of data science can be.
  3. Bias in machine learning models is a big issue, and it's important to understand how these biases can affect results in various tasks.

RISC-V Vector Programming in C with Intrinsics

Fprox’s Substack • 3 HN points • 23 Nov 23
🕹 Technology Programming
  1. RISC-V Vector Programming can be done in C using RVV Intrinsics, providing a more modern and accessible approach than assembly programming.
  2. RVV Intrinsics are low-level functions exposed by the compiler that have a one-to-one mapping with corresponding RVV instructions, embedding vector configuration information.
  3. The RVV Intrinsic API offers a variety of intrinsics for different types, operations, and configurations, enabling efficient programming with RISC-V Vector instructions.

Data Science Weekly - Issue 223

Data Science Weekly Newsletter • 19 implied HN points • 01 Mar 18
🕹 Technology Programming
  1. AI still struggles with creativity and emotional understanding in music, meaning it can't fully replace human DJs and playlist makers.
  2. Female characters are underrepresented in superhero comics, and their portrayal is important to analyze as well.
  3. Containerization is a complex topic for data scientists, and balancing their autonomy with the need for engineering support is essential for success.

Data Science Weekly - Issue 213

Data Science Weekly Newsletter • 19 implied HN points • 21 Dec 17
🕹 Technology Programming
  1. Machine learning can help decode animal communication, like chicken chatter, for better farming practices. This shows how AI can be useful in agriculture.
  2. Turning raw data into useful products is complex, as seen with Google Maps, which relies on a lot of behind-the-scenes work. It highlights the importance of data processing in creating useful tools.
  3. Finding exoplanets is challenging, but machine learning has made some progress in this area. It illustrates how technology is advancing our understanding of the universe.

“Useless syntax sugar”: Numeric block parameters

zverok on lucid code • 3 HN points • 10 Oct 23
🕹 Technology Programming
  1. Ruby introduced a feature with numbered block parameters to avoid repeating block arguments, making code more concise and readable.
  2. Using numbered block parameters can improve visual lightness, saving screen space and avoiding unnecessary repetition in chains of short blocks.
  3. The small syntax change of using numbered block parameters can encourage a declarative coding style, emphasizing transformations from inputs to outputs in a more readable manner.

When Nerdy and Productive Meet

Oren Cohen • 3 implied HN points • 06 Oct 23
🕹 Technology Programming
  1. The author made a new video on YouTube about a TV show and is expanding content on a new channel.
  2. They created free video courses for beginner software developers, like the 30-Day Python Challenge and the Git Masterclass.
  3. The author is consolidating their articles from different platforms and updating them to work with Substack.

Let’s Try This Weekly Newsletter Thing

Oren Cohen • 3 implied HN points • 29 Sep 23
🚌 Education Programming
  1. The newsletter is an experiment, so feedback is welcome.
  2. Main highlights this week: preparing for vacation, closing Nerdy Modern Blog, and reviving the Substack publication.
  3. Potential goals include creating a programming course, editing a book, and making Starfield-related YouTube videos.

Data Science Weekly - Issue 205

Data Science Weekly Newsletter • 19 implied HN points • 26 Oct 17
🕹 Technology Programming
  1. AlphaGo's victories sparked discussions about the significance and implications of AI developments. People are curious about how AI researchers view these breakthroughs.
  2. Machine learning software can be tricky to debug, so using unit tests is really important. They can save a lot of time and help ensure your algorithms work correctly.
  3. Adversarial attacks can trick machine learning models into making wrong predictions, raising safety concerns about AI systems that we rely on.

Data Science Weekly - Issue 201

Data Science Weekly Newsletter • 19 implied HN points • 28 Sep 17
🕹 Technology Programming
  1. Linear programming can help optimize diets for better health. It's about finding the best balance of food for weight loss and longevity.
  2. Understanding the risk of extreme weather events, like floods, can help cities prepare better. It's important to question outdated models when they don't match recent data.
  3. AI and machine learning are changing design fields, like web design, by enabling automated creation. This could make building websites easier and more efficient.

Emacs and Tiling window managers

Amaca • 4 HN points • 14 Apr 23
🕹 Technology Programming
  1. Computer enthusiasts often enjoy niche, specialized tools like Emacs and tiling window managers.
  2. The appeal of coding fast and optimizing code has roots in past technological limitations like low RAM.
  3. The future of programming may move towards more natural language interactions with machines, making traditional tools like Emacs less essential.

Data Science Weekly - Issue 200

Data Science Weekly Newsletter • 19 implied HN points • 21 Sep 17
🕹 Technology Programming
  1. Machine-vision drones can assist in monitoring wildlife by providing accurate population estimates in remote areas. This technology helps wildlife management efforts.
  2. Unity has introduced Machine Learning Agents that can help researchers and game developers experiment with applying machine learning in gaming scenarios. This will enhance both fields by bridging the gap between them.
  3. There are many resources available for those interested in data science, including tutorials and job listings. These can help you improve your skills and find opportunities in the data science field.

Data Science Weekly - Issue 196

Data Science Weekly Newsletter • 19 implied HN points • 24 Aug 17
🕹 Technology Programming
  1. Using machine learning models, like recurrent neural networks, can enhance text editing by making it smarter and more responsive. It allows for cool features like inline autocomplete that feels very natural.
  2. When choosing between deep learning frameworks like PyTorch and TensorFlow, think about how easy they are to use and their flexibility for your specific project needs.
  3. Building a strong data science resume and portfolio is crucial to getting hired; make sure they highlight your skills and tailor them to each job you apply for.

4 essential reads for devs this week

HackerPulse Dispatch • 2 implied HN points • 12 Mar 24
🕹 Technology Programming
  1. Visualize code complexity with 'dep-tree': Tool to map file dependencies and improve project structure
  2. C++ programming safety balance: Efficiency vs. security, the challenge of writing safe code in C++
  3. RFC significance: Structured approach for proposing features, enhancing software quality and developer collaboration

RISC-V support for BFloat16

Fprox’s Substack • 3 HN points • 04 Sep 23
🕹 Technology Programming
  1. Brain Float 16 (BFloat16) format provides a compromise between accuracy and cost suited for machine learning applications.
  2. RISC-V is introducing support for BFloat16 format through scalar and vector extensions to improve efficiency in machine learning tasks.
  3. The new BFloat16 extensions in RISC-V have passed Architecture Review and are designed to be fully IEEE-754 compliant for numerical reproducibility.

Data Science Weekly - Issue 195

Data Science Weekly Newsletter • 19 implied HN points • 17 Aug 17
🕹 Technology Programming
  1. The OpenAI DotA 2 bot is an impressive project, but it's important to understand that it's not the revolutionary breakthrough some claim it to be. It's a significant achievement in AI, yet its implications should be viewed more critically.
  2. There are innovative tools and experiments that use machine learning to enhance how we interact with platforms like Wikipedia, making it easier to explore content effectively. This shows how technology can change our access to information.
  3. Machine learning and AI are evolving rapidly, with new techniques such as autoregressive models and advanced algorithms present in various fields. It's exciting to see how these developments are shaping technology and everyday life.

High Performance Swift Apps

Jacob’s Tech Tavern • 2 HN points • 04 Mar 24
🕹 Technology Programming
  1. Testing on a real device to identify user-facing problems is crucial for improving app performance.
  2. Profiling the app using Instruments to identify bottlenecks and implementing targeted code improvements based on the findings can significantly enhance performance.
  3. Improving processing speed, utilizing parallelism, and optimizing code to run earlier during app launch are key strategies for enhancing the performance of Swift apps.