The hottest Programming Substack posts right now

And their main takeaways
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Top Technology Topics

Data Science Weekly - Issue 138

Data Science Weekly Newsletter • 19 implied HN points • 14 Jul 16
🕹 Technology Programming
  1. There's ongoing research in AI that allows it to write its own code. This could change how we view software development.
  2. Data from mobile phones can help understand literacy rates in developing countries. It's a surprising new way to gather important social data.
  3. Machine learning can identify signs of depression by analyzing speech patterns. This could help in early diagnosis and better mental health support.

Data Science Weekly - Issue 137

Data Science Weekly Newsletter • 19 implied HN points • 07 Jul 16
🕹 Technology Programming
  1. There are six basic emotional arcs that make up all stories, which can be found through data mining novels.
  2. The Toronto Raptors are using IBM’s Watson to help them make smart decisions in drafting players for their basketball team.
  3. Python is a slower programming language because it is dynamically typed and interpreted, which affects how data is stored and processed.

Open sourced AI is the worst thing imaginable

Theology • 3 implied HN points • 04 Apr 23
🕹 Technology Programming
  1. Open-sourced AI can be dangerous when unregulated and in the hands of individuals who may use it for harmful purposes.
  2. The proliferation of open-source AI projects without proper ethical boundaries makes it challenging for regulators to monitor and control its potential risks.
  3. There is a significant concern over the unintended consequences of developers creating and sharing homebrew versions of AI models, leading to a lack of understanding and control over the technology's impact.
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Lương 6 chữ số

Thái | Hacker | Kỹ sư tin tặc • 19 implied HN points • 04 Apr 16
🕹 Technology Programming
  1. Fresh graduates from Vietnam have the potential to earn salaries in the six-figure range, especially in the tech industry.
  2. With smart financial planning and opportunities abroad, computer engineers in Vietnam can aim for higher income levels than the local average.
  3. Consider seeking job opportunities at major global tech companies for better financial prospects, surpassing what local companies in Vietnam typically offer.

Data Science Weekly - Issue 135

Data Science Weekly Newsletter • 19 implied HN points • 23 Jun 16
🕹 Technology Programming
  1. Machine learning is becoming crucial for businesses, and understanding its implications can help you stay ahead. It's important to keep learning about new tech like machine learning instead of just focusing on the latest trends.
  2. Companies like Google are adapting to prioritize machine learning in their products. This means they are training their programmers to better integrate AI into their work.
  3. Real-world applications of data science show it isn't just theory. Companies use data science to improve their operations and products, making it more understandable for everyone.

Data Science Weekly - Issue 133

Data Science Weekly Newsletter • 19 implied HN points • 09 Jun 16
🕹 Technology Programming
  1. Data is really important for machine learning, and having good data can help achieve better results.
  2. Writing scripts to automate tasks can save a lot of time and effort in data science.
  3. Understanding different data structures, like Bloom filters, can help make efficient use of memory and speed up programs.

Data Science Weekly - Issue 130

Data Science Weekly Newsletter • 19 implied HN points • 19 May 16
🕹 Technology Programming
  1. Transcribing long-term rental ads can provide valuable insights into housing price trends. This type of data collection helps inform discussions on affordable housing.
  2. Data natives, or people who grew up using technology, expect smart systems that adapt to their preferences seamlessly. This shift is changing how we interact with data and technology.
  3. Power analysis is important for scientists planning experiments. It helps them understand if an experiment will be effective and what data they need to collect.

Data Science Weekly - Issue 129

Data Science Weekly Newsletter • 19 implied HN points • 12 May 16
🕹 Technology Programming
  1. Machine learning can help understand emoji usage and trends on social media. It's exciting to see how technology can analyze emotions expressed through simple icons.
  2. There's a growing idea that future AI may not be about creating more AI but about building platforms for people to design their own AI. This could make technology more personal and user-friendly.
  3. Automating data science processes can save time and make it easier for everyone to use machine learning effectively. Tools that simplify these tasks can be really useful for beginners.

Data Science Weekly - Issue 128

Data Science Weekly Newsletter • 19 implied HN points • 05 May 16
🕹 Technology Programming
  1. Kaggle competitions need more than just machine learning knowledge. It's important to have the right mindset and explore the data thoroughly.
  2. Neural networks are surprisingly good at compressing data. They can learn to behave effectively without being explicitly taught how.
  3. Machine learning can unintentionally reinforce social biases. It's crucial to recognize these biases and work to reduce their impact in models.

Data Science Weekly - Issue 127

Data Science Weekly Newsletter • 19 implied HN points • 28 Apr 16
🕹 Technology Programming
  1. Bayesian models can be useful for predicting future outcomes using smaller, time-stamped datasets, which may be overlooked compared to large data analysis.
  2. Visual information can highlight our mental errors and biases, suggesting that interactive graphics could help us understand our own behaviors better.
  3. Companies are quickly acquiring artificial intelligence startups to stay competitive, showing the race to lead in AI technology among major corporations.

Data Science Weekly - Issue 126

Data Science Weekly Newsletter • 19 implied HN points • 21 Apr 16
🕹 Technology Programming
  1. Drones are becoming easier to build and program, which can make them great hands-on projects for learning about tech.
  2. Applying data analysis techniques to literature can reveal interesting insights, like the emotional journey of characters in books.
  3. Collaborating between humans and machines often leads to better results than relying solely on one or the other.

Data Science Weekly - Issue 125

Data Science Weekly Newsletter • 19 implied HN points • 14 Apr 16
🕹 Technology Programming
  1. Platforms are important in data science because they help teams work better together and scale their projects. Good organization can make a big difference in data science tasks.
  2. Machine learning can be used to make accurate predictions, such as predicting the outcomes of sports tournaments. This can lead to impressive results in competitions.
  3. Understanding statistics is crucial in software development to assess performance and reliability. Without a solid grasp of statistics, it's hard to know how well software is performing.

Are you the chosen one? 💻[HTC #67]

Hold the code • 2 implied HN points • 17 Oct 23
🕹 Technology Programming
  1. The concept of the 10x developer remains intriguing but unproven in software engineering.
  2. Media disinformation enhanced by AI tools is a significant threat to the 2024 US elections.
  3. Utilizing AI in user research presents ethical considerations like transparency, privacy, and bias awareness.

Data Science Weekly - Issue 117

Data Science Weekly Newsletter • 19 implied HN points • 18 Feb 16
🕹 Technology Programming
  1. Understanding causality could be more important than just focusing on deep learning. It's a key concept that can help data scientists make sense of their work.
  2. Effective data science teams have clear markers of success. It's important to figure out what these teams do well and how they train their members.
  3. Data scientists often do basic arithmetic, which is actually very valuable. Simplifying complex data tasks can lead to meaningful insights.

Data Science Weekly - Issue 116

Data Science Weekly Newsletter • 19 implied HN points • 11 Feb 16
🕹 Technology Programming
  1. Kaggle is a great place for data scientists to learn and share ideas. They have a huge collection of machine learning models that can help you improve your skills.
  2. Genetic algorithms can solve tough problems by mimicking natural evolution. They work by selecting the best solutions and mixing them to create new ones.
  3. Understanding data ethics is important for data scientists. People often trust numbers too much, so it's crucial to think about how data is used responsibly.

Data Science Weekly - Issue 109

Data Science Weekly Newsletter • 19 implied HN points • 24 Dec 15
🕹 Technology Programming
  1. Children are amazing learners, and studying how they learn can help improve machine learning methods.
  2. There's a big surge in scientists promoting their work, which may lead to exaggerated claims in research.
  3. Using data to analyze behavior, like how people introduce others in emails, can reveal interesting social patterns.

Data Science Weekly - Issue 108

Data Science Weekly Newsletter • 19 implied HN points • 17 Dec 15
🕹 Technology Programming
  1. Data science is being applied in creative ways, like analyzing rap lyrics to see what makes a hit song. It's cool to see data being used to explore music trends!
  2. Recent advances in AI are allowing machines to perform vision tasks better than humans, showing how fast technology is evolving.
  3. Understanding the differences between jobs in data science, like data scientists and machine learning engineers, can help people find the best fit for their skills.

Data Science Weekly - Issue 107

Data Science Weekly Newsletter • 19 implied HN points • 10 Dec 15
🕹 Technology Programming
  1. An algorithm can identify influential universities based on Wikipedia data, revealing some unexpected rankings.
  2. Many commonly accepted rules in statistics might not hold true, and it's crucial to question them.
  3. Machine learning can lead to significant maintenance costs despite offering quick results, known as technical debt.

Data Science Weekly - Issue 104

Data Science Weekly Newsletter • 19 implied HN points • 19 Nov 15
🕹 Technology Programming
  1. Using Python with tools like Hadoop and Spark helps make data analysis easier in big projects.
  2. A new algorithm can make computational modeling much faster, helping researchers get results quicker.
  3. Machine learning is being used in cool ways, like studying galaxy formation and creating images from random noise.

Data Science Weekly - Issue 103

Data Science Weekly Newsletter • 19 implied HN points • 12 Nov 15
🕹 Technology Programming
  1. Facebook's AI has made big improvements, showcasing its capabilities in smart technology. This shows how AI is becoming more advanced and useful in everyday life.
  2. There are lots of resources available for learning about data science and machine learning. This includes articles, webinars, and books that can help both beginners and experienced users.
  3. Finding junior data scientist jobs can be tricky because many companies seek senior candidates. It's important for newcomers to explore different strategies and networks to find entry-level opportunities.

Data Science Weekly - Issue 98

Data Science Weekly Newsletter • 19 implied HN points • 08 Oct 15
🕹 Technology Programming
  1. Data science can help social good organizations, but they need more than just good intentions to make a real impact. Following certain principles can help these efforts succeed.
  2. The random walk hypothesis is a way to explore market behaviors and randomness. Understanding it can help in analyzing financial markets more effectively.
  3. Teaching statistics can be challenging, and it's important to make it easier for students. If students find it complicated, educators should look at their teaching methods.

Busy Beaver, the current BB(5) conjecture and bbchallenge.org

Fikisipi • 1 HN point • 24 Jun 24
🕹 Technology Programming
  1. The Busy Beaver function is a mathematical concept related to Turing Machines that aims to find the machine performing the most operations without entering an endless loop. It's a fun way to think about extremely large numbers.
  2. Professor Scott Aaronson made a conjecture that the value of BB(5) is 47,176,870, which is a big number in the context of the Busy Beaver problem. This means trying to determine how many steps the best machine with 5 states can make.
  3. A group called bbchallenge.org is working together to solve this conjecture and make progress on understanding BB(5). They've made some recent updates and are excited about their upcoming findings.

Data Science Weekly - Issue 90

Data Science Weekly Newsletter • 19 implied HN points • 13 Aug 15
🕹 Technology Programming
  1. Sorting algorithms can be visualized in a fun way through animations, making it easier to understand how they work.
  2. AI tools, like Baidu's medical robot, can help provide quick health advice based on symptoms, improving access to healthcare.
  3. Machine learning techniques are being used in diverse fields, from predicting wine prices to improving speech recognition systems.

Data Science Weekly - Issue 88

Data Science Weekly Newsletter • 19 implied HN points • 30 Jul 15
🕹 Technology Programming
  1. Hadley Wickham is a famous statistician known for his work with R, a programming language. He has made a big impact in the stats community, and people admire his contributions.
  2. Computers are moving beyond just calculations; they can now assess human character. This development raises questions about how we see technology's role in our lives.
  3. The concept of Dropout is key in modern neural networks, and there are simple ways to implement it in Python. Learning this can help improve machine learning projects.

Data Science Weekly - Issue 87

Data Science Weekly Newsletter • 19 implied HN points • 23 Jul 15
🕹 Technology Programming
  1. Machine learning is a powerful tool that helps companies boost revenue and engagement. Big names like Google and Amazon use it to improve their services.
  2. There are tools and methods to analyze stories using sentiment and data models. These can help summarize the emotions and shapes of narratives in books and movies.
  3. Online resources and workshops are available for those wanting to learn data science. They provide hands-on experience and mentorship to help you get started.

Your Code is not Yours

The Healthy Dev • 2 HN points • 27 Jul 23
🕹 Technology Programming
  1. Consider that programming should be seen as a form of creative work, not just technical.
  2. Digital tools can make it challenging to detach personal identity from your work when collaborating.
  3. Recognize and handle cognitive dissonance when receiving feedback on your work to avoid defensive reactions.

GPU 2D Drawing: Rectangls. Masking. Rotations.

Hasen Judi • 2 HN points • 04 Jul 23
🕹 Technology Programming
  1. Rectangles can be defined in different ways for SDF purposes, but simplifying it to (center, half_size) helps in calculations such as distance to edges or corner points.
  2. Simple transformations like growing, bordering, and inversion, along with masking and rotations, can create interesting visual effects and shapes.
  3. Combining these shape manipulation techniques can lead to the creation of more complex shapes and effects in GPU-rendered graphics.

Writing Libraries for AIs to Use

General Robots • 2 HN points • 12 Jul 23
🕹 Technology Programming
  1. Programs, libraries, and programming languages that are compatible with efficient AI assistance are likely to be favored over abstractions LLMs struggle with.
  2. Using AI assistance like ChatGPT and Github Copilot can significantly boost productivity in coding tasks, especially for less experienced programmers or when working in unfamiliar domains.
  3. LLMs excel at pattern matching, but struggle with newer or less common patterns; providing examples and good documentation can greatly improve LLM performance.

Signed Distance Function (Field)

Hasen Judi • 2 HN points • 01 Jul 23
🕹 Technology Programming
  1. Signed Distance Function calculates distance between a point and the nearest edge of a shape.
  2. SDF allows for creative 2D shape manipulations like creating borders, shadows, and color compositing.
  3. Using techniques like step and smooth step with SDF helps in creating smooth shapes and adding effects like drop shadows.

Announcing posetree.py: Wrangling Timestamps and Transforms for Robots

General Robots • 2 HN points • 10 Jul 23
🕹 Technology Programming
  1. Posetree.py is a library for dealing with poses and transforms in robotics, making code more readable and reducing common bugs.
  2. Understanding the distinction between transforms, poses, and frames is crucial for clarity in robotics code.
  3. The 'timestamps' capability of posetree.py allows for expressing powerful ideas with simple code by automatically handling frame motion.

Introduction to Hash Tables

Photon-Lines Substack • 2 HN points • 09 Jul 23
🕹 Technology Programming
  1. Hash tables allow for efficient storage and retrieval of key-value pairs using hash functions.
  2. Real-world applications of hash tables include dictionaries, caching systems, database indexing, and symbol tables in compilers.
  3. Good hash functions must be efficient, deterministic, and ensure a uniform distribution of generated keys to avoid collisions.

Data Science Weekly - Issue 63

Data Science Weekly Newsletter • 19 implied HN points • 05 Feb 15
🕹 Technology Programming
  1. Visual mapping helps understand the fast-growing communities on platforms like Twitch. It's a fun way to see how different groups connect.
  2. Data science can offer new ways to evaluate business risks, making it easier for startups to succeed. Using data helps to make better decisions.
  3. In data science portfolios, quality is often more important than quantity. Employers want to see impactful work rather than just a long list of projects.

Malloy's Near Term Roadmap

Making Things • 2 implied HN points • 15 Jun 23
🕹 Technology Programming
  1. Introducing MalloySQL, a new file type that combines Malloy and SQL for data transformation.
  2. Malloy simplifies Window Functions in SQL, making complex calculations easier to understand.
  3. Malloy offers a standard data function library that operates consistently across different SQL engines.