The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 446

Data Science Weekly Newsletter • 19 implied HN points • 09 Jun 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming
  1. The history of AI in literature shows how machines have been involved in writing since the 19th century. It's fascinating to see how far technology has come in helping with creative tasks.
  2. Jupyter Notebooks are versatile tools for data scientists, used for more than just coding. They can creatively combine text, visuals, and code to make data exploration easier.
  3. Using machine learning with small data sets can be tricky, but there are effective techniques to make it work. Smaller datasets can still yield valuable insights with the right approaches.

Once a Maintainer: Stephen Ierodiaconou

Once a Maintainer • 5 implied HN points • 02 Feb 24
🕹 Technology Open Source Programming Community Software Development Ruby
  1. Stephen Ierodiaconou's journey into programming began with an interest in electronics and evolved into software development through hands-on exploration and community involvement.
  2. Open source played a significant role in Stephen's growth as a software developer, providing a platform for learning, contributing, and connecting with like-minded individuals.
  3. Stephen's experience highlights the value of community engagement, continuous learning, and sharing knowledge in open source projects for personal and professional growth.

Step One: Help Others

Tribal Knowledge • 19 implied HN points • 29 Mar 22
🕹 Technology Software Development Problem Solving
  1. Helping others is fulfilling and valuable, even if it may come at a cost.
  2. Explaining problems to others or to an inanimate object like a rubber duck can help improve problem-solving skills by engaging different parts of the brain.
  3. Helping others can also benefit oneself by providing a fresh perspective and removing personal doubts and reservations.

Data Science Weekly - Issue 444

Data Science Weekly Newsletter • 19 implied HN points • 26 May 22
🕹 Technology Machine Learning Data science Artificial Intelligence Computer Vision Software Development
  1. Operationalizing machine learning models is important. There are key differences between how ML is used in research and in real-world applications, and understanding these can improve system design.
  2. DALL-E and similar AI models show that composition in AI can produce unexpected and enjoyable results. This is a fun way to think about how AI works with semantics, even if it doesn't always make sense.
  3. Data can sometimes lead to worse decisions. It's essential to think critically about how we use data rather than just relying on it blindly.
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Once a Maintainer: Robert Mosolgo

Once a Maintainer • 5 implied HN points • 26 Jan 24
🕹 Technology Open Source Programming Software Development Languages Performance
  1. Robert Mosolgo transitioned from a background in linguistics to becoming a prolific open source maintainer and creator of the graphql-ruby gem.
  2. He got involved in open source by taking over the React-Rails gem, contributing, and eventually becoming the maintainer, showcasing the accessibility and impact of open source contributions.
  3. His journey into writing parsers for the gem led him to explore his linguistics background, bridging the gap between human language and programming language parsing.

Data Science Weekly - Issue 443

Data Science Weekly Newsletter • 19 implied HN points • 19 May 22
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Data Visualization
  1. Data scientists should improve their software development skills by learning about project structure, testing, reproducibility, and version control.
  2. AI-generated artwork may not be considered true art because it lacks the communication and consciousness involved in traditional art creation.
  3. Using optimized tools like DuckDB can enhance the data processing experience by making it faster and easier to work with large datasets.

Lessons to a junior software developer

QED • 1 HN point • 26 Apr 24
🕹 Technology Software Development Programming Languages Knowledge acquisition Problem Solving Experience
  1. Writing code takes practice: The more you code, the faster you'll make decisions and write code.
  2. Continuous learning is essential: Understand problem domains, master tools, and know how to acquire new information as a junior developer.
  3. Learn deeply and take on challenging projects: Focus on mastering key concepts and push yourself with difficult projects to grow as a developer.

Vector Database: History and Basic Concept

The Beep • 2 HN points • 08 Feb 24
🕹 Technology Artificial Intelligence Database Management Data science Machine Learning Software Development
  1. Vector databases help store and manage embedding vectors effectively. This is important for improving how AI finds and retrieves information.
  2. The concept of vector databases has been around for a long time, dating back to the 1990s. They have evolved from early uses in semantic models to current advanced techniques.
  3. Various algorithms have been developed to convert digital items into vectors and to streamline searching within these vectors. This makes it easier for AI to understand and process data.

Data Science Weekly - Issue 440

Data Science Weekly Newsletter • 19 implied HN points • 28 Apr 22
🕹 Technology AI Machine Learning Data science Software Development Statistical Analysis
  1. AI is getting smarter, but we need a better way to understand how it makes decisions. A common language with AI could help us communicate our questions and concerns.
  2. Creating more synthetic data can help when there's not enough real data for training models. Techniques like data augmentation can help make our data better.
  3. Making data more accessible can solve big problems for society. If we can use available data properly, it can lead to more health and happiness for everyone.

[Solution]Problem 12: Group Shifted Strings [Facebook]

Technology Made Simple • 19 implied HN points • 05 Nov 21
🕹 Technology Coding Programming Problem Solving Algorithms Software Development
  1. Given an array of strings, group them based on being shifted versions of each other by finding the difference in characters.
  2. Implementing a hashset can efficiently group strings by their difference strings for quick retrieval.
  3. Creating helper functions and structuring your solution neatly can showcase your organization and problem-solving skills.

[In case you missed it] Data Science Weekly - Issue 439

Data Science Weekly Newsletter • 19 implied HN points • 24 Apr 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Software Development
  1. Building a recommendation system is challenging. It requires careful planning and execution to serve users quickly and efficiently.
  2. Understanding different probability distributions is essential in data science. They help us make better predictions and understand the variability in our data.
  3. Contrastive learning is an important method for training machine learning models. Recent advances in this area can improve how we represent data and solve complex problems.

Hexceptional Encoding: Mastering the Art of Hex Conversion in Rust

Aayushya’s Substack • 1 HN point • 20 Apr 24
🕹 Technology Coding Optimization Software Development
  1. Hex encoding is essential for storing or transmitting binary data in formats like json, xml. It is widely used for checksums, hash digests, and ensuring data integrity.
  2. Minimizing memory allocations can significantly improve performance in operations involving heavy processing of data, like databases or real-time data processing.
  3. Using dedicated crates like hex and faster-hex in Rust can provide substantial speed enhancements compared to traditional string concatenation methods for hex encoding.

[in case you missed it] Data Science Weekly - Issue 437

Data Science Weekly Newsletter • 19 implied HN points • 10 Apr 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Project management
  1. Distribution shift is a big challenge in machine learning. If we ignore how data changes in the real world, our models may fail.
  2. Tech apprenticeships are becoming more common and are a great way to learn while earning money. They help people start new careers in tech, even without a degree.
  3. There's ongoing research to give computers common sense. This could help AI understand the world better and make smarter decisions.

Data Science Weekly - Issue 437

Data Science Weekly Newsletter • 19 implied HN points • 07 Apr 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Tech Conferences
  1. Data in the real world can change, and we need to think about that when we use machine learning. If we don't, our models may not work well when they are put to the test.
  2. Attending conferences can be a great way to learn and connect with others in the field. They often showcase new startups and many interesting themes that can inspire ideas.
  3. Tech apprenticeships are a rising opportunity. They allow you to earn while you learn skills for a technology career, making it accessible for more people.

Special #1 - SCM

Infra Weekly Newsletter • 9 implied HN points • 08 Jul 23
🕹 Technology Software Development Version Control Community support Performance Features
  1. Source Code Management (SCM) has evolved over the years, from centralized to distributed systems like Git and Mercurial.
  2. Mercurial is known for its simplicity, ease of use, and better management of mono repositories compared to Git.
  3. Git offers benefits like widespread adoption, community support, flexibility in workflows, and better performance in certain areas.

Data Science Weekly - Issue 434

Data Science Weekly Newsletter • 19 implied HN points • 17 Mar 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Statistics
  1. Understanding NLP is important. It involves tokenization and encoding, which helps to improve how machines understand language.
  2. Performance in deep learning can often feel random, but reasoning from first principles can help simplify the process. Focus on compute, memory, and overhead to improve performance.
  3. There is a growing need for data product managers as data teams modernize. These managers bridge the gap between data science insights and product development.

A Dispatch On All Things Tools

Building Rome(s) • 7 implied HN points • 11 Sep 23
🕹 Technology Tools Program Management Software Architecture Software Development Product Management
  1. Building timelines can be frustrating due to lack of ideal tools like Gantt charts or whiteboarding tools.
  2. C4 Diagrams are a great visual language to discuss software architecture for TPMs and PMs.
  3. There is a desire for an all-in-one tool to manage product development, simplifying the use of multiple tools like Slack, Linear, Coda, Figma.

Episode 27: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 07 Mar 22
🕹 Technology Embedded Systems Software Development Team Dynamics Industry Trends
  1. Many companies are now stopping business with Russia due to the war in Ukraine, but it’s argued they should have done this much earlier when the conflict first started.
  2. The design of software often mirrors the organization structure, which means that how teams are set up can impact how effectively they create software.
  3. There are different types of teams in software development, such as stream-aligned teams that focus on delivering features quickly, and enabling teams that help improve the skills of those feature teams.

Episode 12: Burkhard on Qt Embedded Systems

burkhardstubert • 39 implied HN points • 30 Nov 20
🕹 Technology Embedded Systems Software Development Freelancing Project management
  1. Freelancers should focus on providing value to clients by saving them time. Clients will often pay more to have their time freed up for important tasks.
  2. It's important for freelancers to continually improve their skills and showcase successful projects to increase their perceived value. This can be done through side projects and sharing expertise online.
  3. Choosing the right pricing strategy is crucial for freelancers. Understanding different methods like value-based pricing can help in setting fair rates that reflect the value provided to clients.

The Scrum Survival Guide: How to Make Scrum Work (Maybe)

Rethinking Software • 2 HN points • 21 Sep 24
💼 Business Project management Agile Methodology Team Dynamics Organizational Behavior Software Development
  1. Using longer sprints can give teams more freedom and reduce stress over estimating work. It allows developers to manage tasks more effectively without getting stuck on details.
  2. It's important for developers to have control over their meetings and tools. Letting developers run their own stand-ups and choose simple tools can improve efficiency and morale.
  3. Teams should focus on collaboration and flexibility. Allowing for specialization in tasks and removing unnecessary management roles can lead to better job satisfaction and productivity.

Episode 11: Burkhard on Qt Embedded Systems

burkhardstubert • 39 implied HN points • 31 Oct 20
🕹 Technology Embedded Systems Software Development Programming IOT Tech Events
  1. Working from home has become the norm for many due to the pandemic. It's nice to have a routine and support from loved ones during tough times.
  2. Qt Marketplace offers a variety of components for developers at reasonable prices. It can save time and money compared to building from scratch.
  3. Testing is crucial for software quality, and using methods like approval testing can help improve the process, especially with legacy applications.

Episode 26: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 07 Feb 22
🕹 Technology Embedded Systems Software Development User Interface
  1. Investors are worried that the difference in value between Qt LGPLv3 and Qt Commercial is too small. They think that not enough extra value is offered to make customers want to pay for the Commercial version.
  2. The new simplified Qt Commercial licensing still may not attract more customers. Many companies are likely to stick with Qt LGPLv3 or even revert back because they see no compelling reason to upgrade.
  3. Companies prefer fixed pricing for licenses rather than fees based on the number of developers or devices. This straightforward approach could help Qt increase profits and appeal to more customers.

Data Science Weekly - Issue 426

Data Science Weekly Newsletter • 19 implied HN points • 20 Jan 22
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Programming
  1. Prospective learning is important because it focuses on preparing for future challenges instead of just learning from past experiences. This helps both humans and AI to adapt to new situations better.
  2. AI is set to change the field of medicine greatly, making things better for both doctors and patients by improving medical tools and approaches. But there are important ethical and technical issues to consider, like data fairness and bias.
  3. Using vectorization can speed up Python code significantly, but it's essential to understand what it means and when to apply it. This way, you can handle large sets of data more efficiently.

Feature flags are ruining your codebase

Leading Developers • 3 HN points • 05 Mar 24
🕹 Technology Software Development Testing Product Management
  1. Feature flags can make codebases more complex and harder to maintain, especially when used as an excuse to avoid making hard decisions like completely removing a feature.
  2. Having too many feature flags can lead to wasted time on dead code, increased testing burden, and making testing a substitute for fixing issues.
  3. Different types of feature flags, like release toggles, experiment toggles, and permission toggles, require specific management approaches to prevent the codebase from becoming unmanageable.

Data Science Weekly - Issue 424

Data Science Weekly Newsletter • 19 implied HN points • 06 Jan 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. New data science managers have a lot to learn in their first year. They should focus on gaining experience and reflecting on their journey to improve their skills.
  2. Chatbots still struggle with understanding complex human queries. They often provide confusing answers because they lack real-world comprehension.
  3. Real-time machine learning is a growing trend with unique challenges. Companies are talking about their pain points and seeking practical solutions for online predictions and continual learning.

Episode 25: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 03 Jan 22
🕹 Technology Embedded Systems Software Development Programming Community Engagement Tutorials
  1. The author received a significant award, becoming a Qt Champion in the Ambassador category for promoting Qt Embedded Systems. It's quite a recognition for their contributions!
  2. In 2022, the author plans to write more, give talks, and create video tutorials on Qt Embedded Systems, with over 50 ideas lined up. It sounds like they are excited to share more knowledge!
  3. The author encourages readers to engage and provide feedback, hoping to keep them as loyal readers and critics as they grow their content.

Data Science Weekly - Issue 423

Data Science Weekly Newsletter • 19 implied HN points • 30 Dec 21
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Programming
  1. 2021 was a great year for AI research, with many new papers and breakthroughs that need to be understood and followed up on.
  2. Graph machine learning gained a lot of attention, and there are many new trends and advancements worth knowing about.
  3. There are many resources and tools available for learning data science and machine learning, including free courses and beginner-friendly tutorials.

Data Science Weekly - Issue 422

Data Science Weekly Newsletter • 19 implied HN points • 23 Dec 21
🕹 Technology Data science Machine Learning Programming Artificial Intelligence Software Development
  1. Games can be made within spreadsheets like Excel or Google Sheets, making learning fun and interactive.
  2. Testing is an important part of a data scientist's job, and understanding how to do it can help improve analysis work.
  3. Understanding language can help in developing smarter machines, opening new paths for machine learning beyond just text processing.

Quyền được gãi ngứa

Thái | Hacker | Kỹ sư tin tặc • 19 implied HN points • 19 Sep 21
🕹 Technology Security Software Development Open Source Government
  1. User experience is crucial in technology design - products need to be safe and easy to use for all users, not just tech-savvy individuals.
  2. Open-source software fosters collaboration, innovation, and faster development, benefiting both creators and users.
  3. Maintaining an open-minded approach, embracing feedback, and encouraging diverse participation can lead to creative solutions and societal progress.

Episode 24: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 06 Dec 21
🕹 Technology Embedded Systems User Interfaces Machine Learning Software Development Human-Machine Interaction
  1. Most machines have difficult user interfaces that frustrate users. They don't help regular users figure out how to operate the machines easily.
  2. User interfaces need to better understand people's needs and improve communication between humans and machines. This can lead to smarter, more productive experiences.
  3. Manufacturers should invest in better hardware and software today to improve user interfaces. This will help users do more with machines and ultimately sell more machines at higher prices.

the end of code tinkering

The Spicy Take AI Sandwich • 3 HN points • 26 Mar 23
🕹 Technology Programming Software Development Machine Learning Artificial Intelligence
  1. Programming can be seen as an art form by some, focusing on clear communication and craftsmanship.
  2. Efforts are shifting towards writing clean code, thorough testing, and understanding mistakes for better software development.
  3. Programming is evolving towards more focus on developing communication tools with computers, especially in the realm of machine learning.

Software 3.0

Div’s Substack • 3 HN points • 01 Apr 23
🕹 Technology AI Programming Software Development AI Models
  1. Software 3.0 represents a shift in programming to using natural language as the new programming language.
  2. Software 3.0 involves querying a large AI model with natural language prompts to get desired output, making programming easier and more versatile.
  3. The transition to Software 3.0 brings benefits like human interpretability, generalization, and simplification of programming, but also comes with challenges like fault tolerance and latency.

Malloy Data

Why Now • 5 implied HN points • 26 Oct 23
🕹 Technology Programming Database Management Data Analysis Software Development Programming Languages
  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.