The hottest Software Development Substack posts right now

And their main takeaways
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Top Technology Topics

Data Science Weekly - Issue 379

Data Science Weekly Newsletter • 19 implied HN points • 25 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Algorithms
  1. Writing a book on data science can be a fun way to inspire others to use data in their lives. The process can feel challenging but is ultimately rewarding.
  2. Learning about Python concurrency can be tricky but understanding it is important for data scientists moving into software engineering roles. Engaging with live coding talks can clarify complex concepts.
  3. Feature stores are becoming essential for managing machine learning data and making it easier to deploy models. They help data scientists collaborate and quickly get their work into production.

Data Science Weekly - Issue 376

Data Science Weekly Newsletter • 19 implied HN points • 04 Feb 21
🕹 Technology Data science Machine Learning Statistical Analysis Software Development
  1. Data quality is super important for AI, especially in high-stakes situations like medical diagnoses. Poor data can lead to serious mistakes in predictions.
  2. DanNet revolutionized deep learning by being the first successful deep CNN in competitions. Its success marked a turning point in computer vision.
  3. Cohort analysis is a powerful way to examine customer data over time, helping businesses improve their user engagement and marketing strategies.

Episode 14: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 31 Jan 21
🕹 Technology Embedded Systems Software Development Programming Languages Open Source
  1. Choosing the right communication technology depends on balancing bandwidth and range. For example, LoRaWAN is great for long distances but has limited bandwidth.
  2. Bare-metal programming is becoming more common for developers using Qt embedded systems, especially with newer microcontrollers that can handle safety-critical applications.
  3. Bluetooth Long Range is a promising option for applications that require good distance and reliability, especially in environments with obstacles, compared to other wireless technologies.

Once a Maintainer: David Wobrock

Once a Maintainer • 2 HN points • 20 Feb 24
🕹 Technology Open Source Programming Software Development Python Community
  1. David Wobrock got into programming due to his parents being involved in meteorology and him tinkering with terminal commands from an early age.
  2. Wobrock's journey into open source started during his studies, with his first major contribution being a Python plugin for Visual Studio.
  3. In the Django community, the maintenance work involves a core team, the Django Software Foundation, technical boards, and security boards, showcasing a structured and collaborative approach.

The Case for Nitpicking

David Reis on Software • 2 HN points • 18 Feb 24
🕹 Technology Software Development Code Quality Engineering Culture Best Practices Team Collaboration
  1. Nitpicking in code reviews can lead to better code quality and a stronger engineering culture. It's important to discuss style and best practices instead of ignoring them.
  2. Good taste in code exists and is based on collective standards among practitioners. Competent programmers can generally agree on what makes code better, like readability and consistency.
  3. Having a style guide helps streamline code reviews and makes discussions less personal. It sets clear expectations and allows for respectful and constructive feedback.
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Data Science Weekly - Issue 374

Data Science Weekly Newsletter • 19 implied HN points • 21 Jan 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Research
  1. Controlled experiments are important for understanding the impact of new features in software. They help ensure that changes actually improve user experience and metrics.
  2. Deep learning is being used in various scientific fields, making tools like DeepChem important for democratizing access to advanced technologies. This helps researchers across disciplines like chemistry and bioinformatics.
  3. There are innovative methods for diagnosing diseases like prostate cancer using AI. These techniques can offer high accuracy and reduce the need for invasive procedures.

Data Science Weekly - Issue 373

Data Science Weekly Newsletter • 19 implied HN points • 14 Jan 21
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Software Development
  1. Machine learning is being used a lot in developmental biology. It helps scientists work with big data from things like images and gene studies, making analysis easier.
  2. There's a growing need for data engineers, with many companies looking for these roles. Focusing on engineering skills can open up more job opportunities than traditional data scientist roles.
  3. The U.S. government has started an initiative to promote and oversee artificial intelligence. This shows how important AI is to the economy and security of the nation.

Data Science Weekly - Issue 367

Data Science Weekly Newsletter • 19 implied HN points • 03 Dec 20
🕹 Technology AI Machine Learning Data science Software Development Robotics
  1. AlphaFold is a huge breakthrough in biology that helps solve the protein folding problem, which has puzzled scientists for 50 years. It shows how AI can speed up scientific discovery.
  2. Spotify needs good tools to make sense of its massive data from millions of users. Designing user-friendly data tools is key for them to understand and improve their services.
  3. Having high-quality data is essential for companies. New technologies can help businesses maintain data quality without spending huge amounts of money.

Aidan OS

The Startup Life • 4 HN points • 17 Jul 23
🕹 Technology Software Development
  1. Your personal OS is the set of tools and habits you use to manage your life.
  2. A personal OS manages resource allocation, provides common services, and universal basic functions.
  3. Building an organized and searchable personal OS can unlock exponential growth in your life.

Data Science Weekly - Issue 365

Data Science Weekly Newsletter • 19 implied HN points • 19 Nov 20
🕹 Technology Data science Machine Learning AI Analytics Software Development
  1. It's important to connect with AI researchers as people, not just through their work. Personal stories can give better insights into their lives and motivations.
  2. Dynamic data testing is crucial for effective data analysis. Unlike software testing, data needs flexible tests that can adjust as it changes.
  3. Creating open datasets for sound events helps improve research in machine learning. These datasets can provide valuable resources for training models.

Data Science Weekly - Issue 364

Data Science Weekly Newsletter • 19 implied HN points • 12 Nov 20
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analysis Software Development
  1. Organizing data in spreadsheets can help prevent errors and make analysis easier. It's important to keep a consistent format and to avoid leaving any empty cells.
  2. AI is being used to create music that sounds like famous artists, which could change the music industry. This technology raises questions about copyright and authenticity.
  3. Monitoring tools are becoming essential for data scientists to track their models for performance and integrity. These tools help ensure that models are accurate and reliable over time.

Data Science Weekly - Issue 363

Data Science Weekly Newsletter • 19 implied HN points • 05 Nov 20
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Science Software Development
  1. Synthetic biology has gained a lot of attention over the past decade, and it's been evolving to deliver real technologies and breakthroughs.
  2. Data poisoning is a serious concern in machine learning, as bad data can manipulate model predictions, especially with NLP models.
  3. Managing data for machine learning projects is challenging, but using version control tools can help keep things organized and prevent unexpected issues.

Once a Maintainer: Benji Nguyen

Once a Maintainer • 5 implied HN points • 28 Apr 23
🕹 Technology Open Source Programming Software Development Community Engagement
  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.

Revisiting: Why Scrum is the Wrong Way to Build Software Pt.1

Rethinking Software • 1 HN point • 09 Sep 24
🕹 Technology Software Development Agile methodologies Project management Team Dynamics Product Management
  1. Scrum gives all product decision power to the Product Owner, leaving engineers to persuade rather than decide. This can create frustration for engineers who want to contribute to product direction.
  2. Many companies confuse the Product Backlog with engineering tasks, making it hard for engineers to focus on their work without interference. Keeping these backlogs separate can help maintain clear roles.
  3. The way Scrum is often implemented leads to engineers being sidelined in decisions about what to build, showing a need for better practices to include their input in product decisions.

Data Science Weekly - Issue 359

Data Science Weekly Newsletter • 19 implied HN points • 08 Oct 20
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming
  1. Arduino is making machine learning easier for everyone by integrating TensorFlow Lite, which lets people run neural networks on Arduino boards to understand simple voice commands.
  2. Papers with Code is now working with arXiv to connect research papers to related code, making it easier for people to see how studies are applied in practice.
  3. Research shows that machine learning models can help automate tasks like counting craters on Mars, which saves human researchers time and effort, allowing them to focus on more complex questions.

Episode 10: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 30 Sep 20
🕹 Technology Embedded Systems IOT Artificial Intelligence Software Development Microcontrollers
  1. The Gartner study highlights various technologies that are influencing embedded systems in IoT, like Edge Networking and Embedded AI. These technologies will shape future Qt embedded systems significantly.
  2. Technologies like RISC-V offer chip vendors a cost-effective alternative to ARM by eliminating licensing fees. This could lead to more widespread use of RISC-V in embedded devices.
  3. Qt supports several protocols like CoAP and MQTT, making it suitable for developing applications in resource-constrained environments. The flexibility of Qt can facilitate efficient communication in IoT systems.

Data Science Weekly - Issue 356

Data Science Weekly Newsletter • 19 implied HN points • 17 Sep 20
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming
  1. ICML is an important conference for those in machine learning, catering to various professionals like researchers and engineers. It's a great place to learn and share knowledge about advancements in the field.
  2. NumPy is a key tool for scientific programming in Python, helping organize and analyze data efficiently. It's widely used and supports various other libraries for data science tasks.
  3. The emergence of generative AI technology is changing the entertainment industry rapidly. Soon, creating movies or shows could be done at a fraction of today's production costs.

Data Science Weekly - Issue 354

Data Science Weekly Newsletter • 19 implied HN points • 03 Sep 20
🕹 Technology Artificial Intelligence Machine Learning Data science Programming Software Development
  1. A machine learning algorithm recently helped discover 50 new planets from old NASA data, showing how AI can unlock new discoveries.
  2. There has been a noticeable drop in deep learning job postings in the past six months, revealing that many companies are reassessing the importance of this technology.
  3. Apple has introduced a residency program for AI and machine learning, offering training and hands-on experience for those with relevant backgrounds.

Episode 9: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 31 Aug 20
🕹 Technology Software Development Embedded Systems Programming Intellectual Property
  1. CppDepend did not meet expectations for finding dependency cycles in code, as it only detected a small number and struggled with parsing, suggesting the need for better tools in C++ dependency analysis.
  2. Understanding and leveraging usage rights in software development is crucial. Keeping some usage rights can allow developers to create reusable parts and explores pricing options for clients.
  3. There are valuable strategies to prevent bugs in software, focusing on clear requirements, effective architecture, and implementing unit tests through Test-Driven Development (TDD) for improving code quality.

Coding Cells - Computational tools for Protein Design

Kesav’s Lab • 1 HN point • 20 May 24
🕹 Technology AI Biotechnology Cloud Computing Software Development Machine Learning
  1. Artificial intelligence and synthetic biology are changing how we interact with biology. They can help us design new food, medicine, and materials more effectively.
  2. AlphaFold is a powerful tool that predicts protein structures, which is crucial for understanding how proteins work. This insight can lead to breakthroughs in drug discovery and other medical applications.
  3. The author is building a user-friendly tool for protein design using AlphaFold on Google Cloud to help protein engineers. The goal is to create a platform where they can easily make predictions and visualize protein structures.

Data Science Weekly - Issue 348

Data Science Weekly Newsletter • 19 implied HN points • 23 Jul 20
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Software Development
  1. Deep Learning papers can be confusing for beginners, but there's a roadmap to help you choose where to start. It's a good way to navigate through the vast amount of research out there.
  2. Machine Learning is creating a lot of value for businesses, and it's important to understand how this value can be captured. Different companies are finding unique ways to apply ML for their needs.
  3. New techniques in AI, like using neural networks for soundscapes, are not just tech innovations but can also help protect the environment. It shows how technology can contribute to nature conservation.

Episode 7: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 30 Jun 20
🕹 Technology Embedded Systems Software Development Open Source Programming Webinars
  1. Cross-building Qt applications can be efficiently done using Docker containers in QtCreator, allowing work on multiple projects with different setups easily.
  2. Building a Qt SDK with Yocto can present challenges, especially in getting QtCreator to work smoothly with CMake, but it's manageable with the right adjustments.
  3. CMake resources are important for developers, and collecting helpful materials can make future projects easier and more efficient.

Data Science Weekly - Issue 345

Data Science Weekly Newsletter • 19 implied HN points • 02 Jul 20
🕹 Technology AI Machine Learning Data science Software Development Computing
  1. Making machine learning useful in real life is a key focus for companies like startups, especially when they provide machine learning as a service.
  2. Documentation is important in machine learning to explain how models work and to clarify their intended use, which helps avoid misuse.
  3. There are ongoing discussions about improving the machine learning community, addressing issues like toxicity, fairness, and the peer-review process.

Data Science Weekly - Issue 343

Data Science Weekly Newsletter • 19 implied HN points • 18 Jun 20
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Algorithms
  1. AI models can now generate images just like they generate text, thanks to advanced training methods. This shows how powerful these technologies have become in creating complex visuals.
  2. MLOps is key for data scientists as it helps them work together better by automating tasks like testing and versioning. This makes their processes smoother and more efficient.
  3. Regulating algorithms is important because they influence many aspects of our lives without any oversight. A new system is needed to ensure they are used fairly and responsibly.

Episode 6: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 31 May 20
🕹 Technology Embedded Systems Software Development Consulting Marketing Intellectual Property
  1. Finding your niche is key to being a successful consultant. It helps you stand out and attract clients who need your expertise.
  2. Marketing yourself takes hard work and time, but it's essential. By creating valuable content and connecting with others, you'll start to receive project opportunities.
  3. Building a financial buffer is crucial for stability. Having savings allows you to navigate through tough times, like a pandemic, without stress.

Data Science Weekly - Issue 340

Data Science Weekly Newsletter • 19 implied HN points • 28 May 20
🕹 Technology Data science Artificial Intelligence Machine Learning Automation Software Development
  1. AI can be limited in business because of how it's researched, but understanding these limits can help identify new business opportunities. This means knowing the business process well can lead to better use of AI to save time and money.
  2. There's a growing belief that humans and machines should work together rather than striving for complete automation. Collaborating with machines can often be more effective and safer than going fully automated.
  3. Basic machine learning skills are still very important, even with all the focus on deep learning. Many companies want solid foundational knowledge rather than just the latest trends, so understanding the basics can be key to success.

Data Science Weekly - Issue 339

Data Science Weekly Newsletter • 19 implied HN points • 21 May 20
🕹 Technology Artificial Intelligence Data science Machine Learning Ethics Software Development
  1. AI Product Managers need special skills for managing AI products beyond traditional project management. This includes an understanding of machine learning and its real-world applications.
  2. Technical debt in machine learning is important to manage to avoid problems later. New tools can help address this issue, highlighting the need for staying updated over time.
  3. China is actively discussing AI ethics, contrary to popular belief. Their conversations align with global standards, and they are exploring how these principles fit into their own culture and systems.

Data Science Weekly - Issue 338

Data Science Weekly Newsletter • 19 implied HN points • 14 May 20
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming
  1. AR and machine learning can be combined to create cool tools, like cutting parts of our surroundings and pasting them into images.
  2. Mapping the connections in the human brain can help scientists understand how our brains work and what happens when they are not healthy.
  3. Data shows that during quarantine, people are not necessarily gaining weight or losing activity, which might surprise some people.

Data Science Weekly - Issue 336

Data Science Weekly Newsletter • 19 implied HN points • 30 Apr 20
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Visualization
  1. Tornado plots are a unique way to visualize time series data, showing how values change over time. They help us understand trends in a different way than regular graphs.
  2. Categorizing diverse products efficiently is crucial for platforms like Shopify. Proper categorization helps users find similar products faster, making shopping easier.
  3. Blender is an open-source chatbot by Facebook AI that feels more human and engages users better. It's a leap forward for conversational AI technology.

Data Science Weekly - Issue 335

Data Science Weekly Newsletter • 19 implied HN points • 23 Apr 20
🕹 Technology AI Machine Learning Data science Software Development Research
  1. Specification gaming is when AI follows rules exactly but misses the main goal. It's important to design AIs that understand the true purpose of their tasks.
  2. There's a growing need to improve how deep learning studies are reported in healthcare. This helps ensure that new AI tools are effective and trustworthy for patients.
  3. Bias in AI language models, like Google Translate, can reflect societal issues. Efforts are being made to address these biases for fairer translations.

Data Science Weekly - Issue 330

Data Science Weekly Newsletter • 19 implied HN points • 19 Mar 20
🕹 Technology Data science Machine Learning Epidemiology Software Development Analytics
  1. COVID-19 spreads very quickly, especially without measures to control it. Understanding how outbreaks work can help people take action sooner.
  2. Data and models are essential to understanding how COVID-19 will affect local areas. People should act decisively based on available information.
  3. New tools and research in data science are helping track and analyze the impact of COVID-19. These resources are making it easier to study and respond to the pandemic.

Data Science Weekly - Issue 329

Data Science Weekly Newsletter • 19 implied HN points • 12 Mar 20
🕹 Technology Machine Learning Data science AI Wearables Software Development Visualization
  1. Google has developed a new shoe insole that uses machine learning to analyze soccer players' movements, helping them improve their game in real-time.
  2. Human-in-the-Loop Machine Learning is beneficial in many ways, such as avoiding bias, maintaining accuracy, and making processes easier and safer by involving humans in decision-making.
  3. Reinforcement learning is being explored to optimize trading strategies and financial concepts, showcasing its ability to learn and adapt in complex environments.

Burkhard on Qt Embedded Systems: No. 3

burkhardstubert • 19 implied HN points • 29 Feb 20
🕹 Technology Embedded Systems Software Development Open Source Pricing Strategies Technical Debt
  1. The Qt Company should offer different pricing options for their software to accommodate various project needs. This would help developers find a pricing plan that fits their budget and offer varying levels of support.
  2. Making Qt relocatable can simplify the installation process for different development environments. This flexibility allows teams to build their own versions more easily and adapt to their specific needs.
  3. Integrating multiple electronic control units into a single super ECU, like Tesla does, can improve efficiency in agricultural and construction machinery. It can reduce costs and streamline software updates, which is crucial for modern technology.

Data Science Weekly - Issue 327

Data Science Weekly Newsletter • 19 implied HN points • 27 Feb 20
🕹 Technology Artificial Intelligence Data science Machine Learning Ethics Software Development
  1. AI startups might not be as promising as they seem and should be closely evaluated. A recent review suggests there's a big difference between AI investments and traditional software investments.
  2. Deep learning is being used to discover new antibiotics, which is crucial due to the rise in antibiotic-resistant bacteria. This shows the real-life applications of AI in solving global health issues.
  3. Ethics in AI is becoming more important, especially with autonomous systems. Companies need to think carefully about the implications of their AI technologies and how they are used.

Data Science Weekly - Issue 326

Data Science Weekly Newsletter • 19 implied HN points • 20 Feb 20
🕹 Technology AI Data science Machine Learning Software Development Computing
  1. AI businesses operate differently than traditional software companies and can seem more like service companies.
  2. Spotify Wrapped is a big marketing campaign that shares users' listening habits over the past year, showcasing engineering efforts to handle data.
  3. Addressing algorithmic bias in AI is becoming more important, and companies are working on ways to make AI fairer and more transparent.

Data Science Weekly - Issue 324

Data Science Weekly Newsletter • 19 implied HN points • 06 Feb 20
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Health tech
  1. Good experiments in product development involve learning from both successes and failures, refining techniques over time.
  2. AI can help detect health crises, as seen with a platform that warned about the Wuhan virus before major health organizations.
  3. Neural networks are being used to enhance older video game graphics, making classic games look modern and appealing again.

Burkhard on Qt Embedded Systems: No. 2

burkhardstubert • 19 implied HN points • 31 Jan 20
🕹 Technology Software Development Embedded Systems UI Design Programming Languages Standards
  1. Using address sanitizers can help find bugs in your code more easily. They show where problems are happening, making debugging faster.
  2. The SAE J1939 standard helps different devices communicate by defining the meaning of messages in vehicle systems. This is important for consistent data across various manufacturers.
  3. Creating portable code separates it from hardware specifics, making it easier to test and run on different systems. This is a key focus for using Qt effectively.