The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

Additional Testing After Refactoring

Software Design: Tidy First? • 950 implied HN points • 20 Jan 25
🕹 Technology Software Development Testing Refactoring Design Patterns Programming
  1. It's important to write more tests after refactoring. This helps improve accuracy and confidence in your code.
  2. When you break down a big piece of code into smaller parts, consider writing smaller tests for those parts, especially if you plan to reuse them.
  3. You might face a dilemma on whether to keep redundant tests after refactoring. It's good to regularly review tests to make sure you have the best approach for checking your code.

AI #97: 4

Don't Worry About the Vase • 2419 implied HN points • 02 Jan 25
🕹 Technology AI Machine Learning Data science Automation Software Development
  1. AI is becoming more common in everyday tasks, helping people manage their lives better. For example, using AI to analyze mood data can lead to better mental health tips.
  2. As AI technology advances, there are concerns about job displacement. Jobs in fields like science and engineering may change significantly as AI takes over routine tasks.
  3. The shift of AI companies from non-profit to for-profit models could change how AI is developed and used. It raises questions about safety, governance, and the mission of these organizations.

AI analysis: ChatGPT vs Claude

Handy AI • 19 implied HN points • 29 Oct 24
🕹 Technology AI Data Analysis Machine Learning Software Development Information Technology
  1. ChatGPT performed better in analyzing a Spotify dataset, providing accurate insights without errors, and displaying clear visualizations.
  2. Claude encountered issues with text extraction and made mistakes in data interpretation, like incorrectly assigning genre labels where they didn't exist in the dataset.
  3. Overall, ChatGPT offered a smoother user experience, allowing users to follow along with the analysis while Claude's process was less straightforward.

Bugs Optional?

Software Design: Tidy First? • 1723 implied HN points • 03 Jan 25
🕹 Technology Software Development Programming Engineering Quality Assurance Project management
  1. Bugs don't have to be a normal part of software development. Some teams manage to almost eliminate bugs by approaching their work differently.
  2. Instead of seeing bugs as inevitable, teams can work to understand and prevent them right from the start. This includes practices like continuous integration and team collaboration.
  3. Changing how we think about bugs from a normal part of life to something rare can help create a better work environment and improve software quality.

Kubernetes for Data Engineers

VuTrinh. • 859 implied HN points • 03 Sep 24
🕹 Technology Data Engineering Cloud Computing DevOps Software Development Infrastructure
  1. Kubernetes is a powerful tool for managing containers, which are bundles of apps and their dependencies. It helps you run and scale many containers across different servers smoothly.
  2. Understanding how Kubernetes works is key. It compares the actual state of your application with the desired state to make adjustments, ensuring everything runs as expected.
  3. To start with Kubernetes, begin small and simple. Use local tools for practice, and learn step-by-step to avoid feeling overwhelmed by its many components.
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What hath AWS wrought?

Cloud Irregular • 2661 implied HN points • 10 Dec 24
🕹 Technology Cloud Computing Software Development DevOps Infrastructure
  1. At this year's AWS re:Invent, there were no major new services launched, which is quite different from previous years. Instead, AWS focused on enhancing existing services and features.
  2. In the past, AWS released many new services, but many of them didn't succeed. This led to dissatisfaction within the developer community.
  3. Now, AWS seems to be concentrating on improving their core offerings. This change could help revive interest and excitement in the AWS developer community again.

#98 — Microsoft's casual Quantum Leap, NodeJS to settle on ESM-only, reserve best hours for dreams and more

The Nibble • 4 implied HN points • 22 Feb 25
🕹 Technology Quantum Computing Software Development Artificial Intelligence Web Development
  1. Microsoft has made a big step in quantum computing with their new Majorana chip. This chip could make quantum computing faster and more accurate, which is exciting for the future!
  2. Node.js is moving towards only using ESM (ECMAScript Modules), making it simpler for developers to build applications without worrying about different module systems. This should help streamline coding for many people.
  3. Companies like Apple are releasing new products like the iPhone 16e, while others are making big moves in AI and tech. It’s clear that innovation is happening at a rapid pace across the industry!

o1 Turns Pro

Don't Worry About the Vase • 3449 implied HN points • 10 Dec 24
🕹 Technology AI Models Software Development Computing Programming Innovation
  1. The o1 and o1 Pro models from OpenAI show major improvements in complex tasks like coding, math, and science. If you need help with those, the $200/month subscription could be worth it.
  2. If your work doesn't involve tricky coding or tough problems, the $20 monthly plan might be all you need. Many users are satisfied with that tier.
  3. Early reactions to o1 are mainly positive, noting it's faster and makes fewer mistakes compared to previous models. Users especially like how it handles difficult coding tasks.

Train and Serve an AI Chatbot Based on Llama 3.2

The Kaitchup – AI on a Budget • 179 implied HN points • 17 Oct 24
🕹 Technology AI Chatbots Machine Learning Data science Software Development
  1. You can create a custom AI chatbot easily and cheaply now. New methods make it possible to train smaller models like Llama 3.2 without spending much money.
  2. Fine-tuning a chatbot requires careful preparation of the dataset. It's important to learn how to format your questions and answers correctly.
  3. Avoiding common mistakes during training is crucial. Understanding these pitfalls will help ensure your chatbot works well after it's trained.

Fast Speculative Decoding with Llama 3.2 and vLLM

The Kaitchup – AI on a Budget • 219 implied HN points • 14 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Computing Software Development Data processing
  1. Speculative decoding is a method that speeds up language model processes by using a smaller model for suggestions and a larger model for validation.
  2. This approach can save time if the smaller model provides mostly correct suggestions, but it may slow down if corrections are needed often.
  3. The new Llama 3.2 models may work well as draft models to enhance the performance of the larger Llama 3.1 models in this decoding process.

How to build an AI side project using AI in 2025

High Growth Engineer • 1434 implied HN points • 05 Jan 25
🕹 Technology AI Tools Software Development Project management User Feedback
  1. Start a waitlist for your project before building it. This way, you can see if there's interest first and save time in the development process.
  2. When getting feedback, ask people about their experiences instead of yes-or-no questions. This helps you understand their actual problems and find better solutions.
  3. Using AI tools can make building your project more fun and efficient. You can create features quickly and not stress too much about cutting ideas.

Managing Expand Projects

Software Design: Tidy First? • 1193 implied HN points • 02 Jan 25
💼 Business Project management Software Development Risk management Growth Strategies
  1. In a phase of rapid growth, problems can emerge suddenly, and it's crucial to focus on quick fixes instead of getting bogged down in perfect plans. This might mean using basic solutions to keep things running.
  2. When facing high demand and limited resources, the goal is to delay or prevent resource shortages. This can involve spending more money or reducing the growth rate to manage resources better.
  3. It's important to stay calm and creative during crises. Experimenting with new ideas in small, parallel teams can help find solutions quickly, which is necessary to continue growing without causing irreversible problems.

I spent 5 hours learning how Google lets us build a Lakehouse.

VuTrinh. • 139 implied HN points • 24 Sep 24
🕹 Technology Cloud Computing Data Engineering Software Development Information Storage
  1. Google's BigLake allows users to access and manage data across different storage solutions like BigQuery and object storage. This makes it easier to work with big data without needing to move it around.
  2. The Storage API enhances BigQuery by letting external tools like Apache Spark and Trino directly access its stored data, speeding up the data processing and analysis.
  3. BigLake tables offer strong security features and better performance for querying open-source data formats, making it a more robust option for businesses that need efficient data management.

The Weekly Kaitchup #63

The Kaitchup – AI on a Budget • 119 implied HN points • 18 Oct 24
🕹 Technology AI Machine Learning Deep Learning Software Development Robotics
  1. There's a new fix for gradient accumulation in training language models. This issue had been causing problems in how models were trained, but it's now addressed by Unsloth and Hugging Face.
  2. Several new language models have been released recently, including Llama 3.1 Nemotron 70B and Zamba2 7B. These models are showing different levels of performance across various benchmarks.
  3. Consumer GPUs are being tracked for price drops, making them a more affordable option for fine-tuning models. This week highlights several models for those interested in AI training.

OpenAI o3 Model Is a Message From the Future: Update All You Think You Know About AI

The Algorithmic Bridge • 2080 implied HN points • 20 Dec 24
🕹 Technology Artificial Intelligence Software Development Machine Learning Computing Innovation
  1. OpenAI's new o3 model performs exceptionally well in math, coding, and reasoning tasks. Its scores are much higher than previous models, showing it can tackle complex problems better than ever.
  2. The speed at which OpenAI developed and tested the o3 model is impressive. They managed to release this advanced version just weeks after the previous model, indicating rapid progress in AI development.
  3. O3's high performance in challenging benchmarks suggests AI capabilities are advancing faster than many anticipated. This may lead to big changes in how we understand and interact with artificial intelligence.

Update re: Microsoft and training data

Marcus on AI • 2766 implied HN points • 26 Nov 24
🕹 Technology Artificial Intelligence Data Privacy Machine Learning Software Development Corporate Communication
  1. Microsoft claims they don't use customer data from their applications to train AI, but it's not very clear how that works.
  2. There is confusion around the Connected Services feature, which says it analyzes data but doesn't explain how that affects AI training.
  3. People want more clear answers from Microsoft about data usage, but there hasn't been a detailed response from the company yet.

Resilient Cyber Newsletter #15

Resilient Cyber • 119 implied HN points • 24 Sep 24
🕹 Technology Cybersecurity AI Security Software Development Cloud Computing Digital Transformation
  1. Some software vendors are creating security problems by delivering buggy products. Customers should demand better security from their suppliers during purchase.
  2. As companies rush to adopt AI, many are overlooking crucial security measures, which poses a big risk for future incidents.
  3. Supporting open source software maintainers is vital because many of them are unpaid. Companies should invest in the projects they rely on to ensure their continued health and security.

Are AI Coding Assistants Really Saving Developers Time? Study Suggests These Tools Don’t Increase Coding Speed.

Shenisha’s Substack • 19 implied HN points • 04 Oct 24
🕹 Technology AI Tools Software Development Coding Practices Productivity Metrics Quality Assurance
  1. AI coding tools, like GitHub Copilot, may actually slow down developers by increasing the number of bugs in their code. This raises questions about whether these tools truly help improve code quality.
  2. While some surveys show that many developers use AI tools and feel productive, a study found that these tools didn't significantly improve coding speed or help reduce burnout among developers.
  3. The rise of AI tools may require developers to spend more time reviewing the code these tools produce, which can cancel out any time they might save overall.

15 Times to use AI, and 5 Not to

One Useful Thing • 2226 implied HN points • 09 Dec 24
🕹 Technology AI Machine Learning Data science Automation Software Development
  1. AI is great for generating lots of ideas quickly. Instead of getting stuck after a few, you can use AI to come up with many different options.
  2. It's helpful to use AI when you have expertise and can easily spot mistakes. You can rely on it to assist with complex tasks without losing track of quality.
  3. However, be cautious using AI for learning or where accuracy is critical. It may shortcut your learning and sometimes make errors that are hard to notice.

DeepSeek-V3: Technical Details

Gonzo ML • 252 implied HN points • 06 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Computer Science Data Analysis Software Development
  1. DeepSeek-V3 uses a new technique called Multi-head Latent Attention, which helps to save memory and speed up processing by compressing data more efficiently. This means it can handle larger datasets faster.
  2. The model incorporates an innovative approach called Multi-Token Prediction, allowing it to predict multiple tokens at once. This can improve its understanding of context and boost overall performance.
  3. DeepSeek-V3 is trained using advanced hardware and new training techniques, including utilizing FP8 precision. This helps in reducing costs and increasing efficiency while still maintaining model quality.

Self-documenting Makefiles

Blog System/5 • 827 implied HN points • 10 Jan 25
🕹 Technology Software Development Programming Documentation Open Source
  1. Using Makefiles can help stitch together complex build processes easily. They allow you to create a command dispatcher with minimal code.
  2. By implementing a 'make help' command, you can provide users with a clear overview of available actions and necessary configuration, reducing confusion.
  3. Documenting both targets and user-settable variables in Makefiles can make them more user-friendly. This helps users know how to interact with the project without getting lost.

The massive DeepSeek affect

TP’s Substack • 37 implied HN points • 15 Feb 25
🕹 Technology AI Models Open Source Consumer Electronics Software Development Cloud Computing
  1. DeepSeek has gained huge popularity in China, surpassing major competitors and reaching 30 million daily active users. This shows that users really like its features.
  2. Chinese companies are rapidly integrating DeepSeek into their products, from smartphones to cars, suggesting that more devices will soon be using this powerful AI tool.
  3. The rise of DeepSeek is changing how people in China use AI and might even provide better search options compared to existing services like Baidu. It's a big deal for the tech industry there.

How Adaptive AI Microcontainers Outmaneuver Modern Cybersecurity Threats in AI Workloads

Phoenix Substack • 14 implied HN points • 20 Feb 25
🕹 Technology AI Security Cybersecurity Microservices Software Development Cloud Computing
  1. AI workloads are important for businesses but are also very attractive targets for cyber threats. This means we need better ways to protect them.
  2. Traditional security methods struggle because they can be predictable and static, making it easier for hackers to get in and steal data or disrupt systems.
  3. Adaptive AI Microcontainers offer a modern solution by constantly changing and healing themselves, making it much harder for cybercriminals to succeed.

The most neglected words of the Agile Manifesto

Ageling on Agile • 99 implied HN points • 17 Oct 24
🕹 Technology Software Development Agile Methodology Project management Innovation Best Practices
  1. The Agile Manifesto emphasizes that we are constantly discovering better ways to develop software, not just using established methods. This means we should keep looking for improvements in our processes.
  2. It's important to focus on finding unique solutions that work for your specific organization. No single method is perfect for everyone.
  3. The Agile principles encourage collaboration and adaptation rather than strictly following a set plan. Being flexible helps teams create more value.

SDF

davidj.substack • 59 implied HN points • 12 Feb 25
🕹 Technology Data Tools Software Development Data Engineering Machine Learning Analytics
  1. SDF and SQLMesh are alternatives to dbt for data transformation. They are both built with modern tech and aim to provide better ease of use and performance.
  2. SDF has a built-in local database, allowing developers to test queries without costs from a cloud data warehouse. This can speed up development and reduce costs.
  3. Both tools offer column-level lineage to track changes, but SQLMesh provides a better workflow for managing breaking changes. SQLMesh also has unique features like Virtual Data Environments that enhance developer experience.

Commodore Magazine Interviews Epyx (1989)

Computer Ads from the Past • 256 implied HN points • 27 Jan 25
🕹 Technology Video Games Software Development Computer History Game design Consumer Electronics
  1. Epyx started as a small game company and became successful by creating original titles and working closely as a team. They really focused on innovative ideas and stayed dedicated to their projects.
  2. The company faced challenges in licensing properties, like trying to secure the Olympic name, but they adapted by creating unique games that avoided conflicts with big players in the industry.
  3. Their games often combined fun gameplay with good graphics and sound, and they focused on making games that were enjoyable for everyone, not just hardcore players.

Last Week in AI #291 - Nobel Prizes, Adobe's Video Model, Tesla's Robots

Last Week in AI • 99 implied HN points • 16 Oct 24
🕹 Technology Artificial Intelligence Software Development Research Innovation Automation
  1. Two scientists won a Nobel Prize in Physics for their important work on artificial intelligence and neural networks, showing how AI is changing technology and society.
  2. Adobe has released a new AI video model that helps users create and edit videos easily, bringing exciting tools to programs like Premiere Pro.
  3. Tesla showcased new robots and vehicles at an event, but some people felt the demonstrations weren't as impressive as expected, leading to a decline in Tesla's stock.

Should You Pick A CIAM Solution That Implements Standards?

ciamweekly • 62 implied HN points • 10 Feb 25
🕹 Technology Cybersecurity Software Development Identity Management
  1. Choosing a CIAM solution that follows standards like OIDC and SAML can enhance security, thanks to the collective expertise of many developers. This leads to fewer vulnerabilities and better protection for users.
  2. Using a standards-based CIAM system makes it easier for your software to work well with existing tools and libraries. This can speed up development since your team is likely already familiar with these standards.
  3. A standards-compliant CIAM solution offers better portability if you need to switch systems later. It allows for shared practices between different solutions, reducing the need to start from scratch when migrating.

The Product Development Triathlon

Software Design: Tidy First? • 1436 implied HN points • 06 Dec 24
🕹 Technology Software Development Product Design Innovation Project management User Experience
  1. Product development happens in three main phases: Explore, Expand, and Extract. Each part has its own challenges and ways to tackle them.
  2. You need different skills and tools for each phase. Trying to use expansion tools in exploration will slow you down.
  3. It's important to notice when you're transitioning between phases. Adapting quickly helps keep the project on track.

Do Not Sign the Qt License Agreement Unchanged

burkhardstubert • 167 HN points • 16 Sep 24
🕹 Technology Software Development Licensing Embedded Systems Application development Legal Compliance
  1. Always read the Qt license agreement carefully before signing. It has many complex parts that could lead to unexpected costs or obligations.
  2. Consider using the Qt LGPL license as a more affordable and less complicated option compared to the commercial license. Many companies find it meets their needs just fine.
  3. Don't just accept the terms of the agreement as they are. You have the right to negotiate changes, and knowing your alternatives can strengthen your position.

o3: The grand finale of AI in 2024

Democratizing Automation • 815 implied HN points • 20 Dec 24
🕹 Technology AI Models Machine Learning Natural Language Software Development Research Trends
  1. OpenAI's new model, o3, is a significant improvement in AI reasoning. It will be available to the public in early 2025, and many experts believe it could change how we use AI.
  2. The o3 model has shown it can solve complex tasks better than previous models. This includes performing well on math and coding benchmarks, marking a big step for AI.
  3. As the costs of using AI decrease, we can expect to see these models used more widely, impacting jobs and industries in ways we might not yet fully understand.

I spent 4 hours learning Apache Iceberg. Here's what I found.

VuTrinh. • 799 implied HN points • 10 Aug 24
🕹 Technology Data Engineering Software Development Database Management Big Data Cloud Computing
  1. Apache Iceberg is a table format that helps manage data in a data lake. It makes it easier to organize files and allows users to interact with data without worrying about how it's stored.
  2. Iceberg has a three-layer architecture: data, metadata, and catalog, which work together to track and manage the actual data and its details. This structure allows for efficient querying and data operations.
  3. One cool feature of Iceberg is its ability to time travel, meaning you can access previous versions of your data. This lets you see changes and retrieve earlier data as needed.

I spent 7 hours diving deep into Apache Iceberg

VuTrinh. • 339 implied HN points • 31 Aug 24
🕹 Technology Data Engineering Software Development Cloud Computing Big Data Database Management
  1. Apache Iceberg organizes data into a data layer and a metadata layer, making it easier to manage large datasets. The data layer holds the actual records, while the metadata layer keeps track of those records and their changes.
  2. Iceberg's manifest files help improve read performance by storing statistics for multiple data files in one place. This means the reader can access all needed statistics without opening each individual data file.
  3. Hidden partitioning in Iceberg allows users to filter data without needing extra columns, saving space. It records transformations on columns instead, helping streamline queries and manage data efficiently.

Behind AI #7: Top Python Libraries Any AI Enthusiast Should Know

Artificial Corner • 138 implied HN points • 09 Oct 24
🕹 Technology AI Programming Data science Software Development Web Development
  1. Python is a key language for AI because it has many useful libraries for tasks like data collection, cleaning, and visualization. Learning these libraries can help you work effectively on AI projects.
  2. For data collection, libraries like Requests and Beautiful Soup are useful for web scraping. If you need to handle JavaScript-driven sites, Selenium and Scrapy are great options.
  3. To visualize data, Matplotlib and Seaborn can help you create standard plots, while Plotly and Bokeh allow for interactive visualizations, making your data easier to understand.

DeepSeek-V3: Training

Gonzo ML • 126 implied HN points • 08 Feb 25
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Computer Science
  1. DeepSeek-V3 uses a lot of training data, with 14.8 trillion tokens, which helps it learn better and understand more languages. It's been improved with more math and programming examples for better performance.
  2. The training process has two main parts: pre-training and post-training. After learning the basics, it gets fine-tuned to enhance its ability to follow instructions and improve its reasoning skills.
  3. DeepSeek-V3 has shown impressive results in benchmarks, often performing better than other models despite having fewer parameters, making it a strong competitor in the AI field.

✨ While tech CEOs talk up AGI imminence, real-world AI use cases emerge

Faster, Please! • 639 implied HN points • 06 Jan 25
🕹 Technology Artificial Intelligence Cybersecurity Software Development Data science Emerging technologies
  1. In a few years, we might see AI agents start working alongside humans, which could really change how companies function.
  2. Tech leaders believe that powerful AI could lead to huge advances in science and medicine, speeding up progress significantly.
  3. While there is excitement about AI's potential, it's also important to manage the risks to make sure it benefits everyone.

Last Week in AI #290 - OpenAI's massive VC round and DevDay, Flux 1.1, NotebookLM

Last Week in AI • 139 implied HN points • 08 Oct 24
🕹 Technology Artificial Intelligence Software Development Venture Capital Tech news Innovation
  1. OpenAI raised a massive $6.6 billion in funding, making it one of the most valuable tech companies. This will help them expand their research and computing power.
  2. At OpenAI's DevDay, they introduced a new Realtime API for developers, allowing nearly instant AI-generated voice responses for apps. Developers are excited about the new possibilities they can create.
  3. Black Forest Labs released a faster and improved version of their image generation model, Flux 1.1 Pro. This could change the game for how quickly and effectively images are created using AI.

Custom KiCad Silk Screen

atomic14 • 173 implied HN points • 27 Jan 25
🕹 Technology Electronics PCB Design DIY Projects Software Development
  1. You can easily add custom designs to the silk screen of your PCB in KiCad. It’s a simple process to import images, opening up creative possibilities for your designs.
  2. Remember that KiCad only supports black and white for silk screen designs, so any colors or shades will need to be converted. This can affect how your graphics look when you import them.
  3. To get the best results, it's helpful to turn off anti-aliasing in your graphics software. This way, what you see is what you get in KiCad, making it easier to control the final look.

Does data quality matter?

benn.substack • 1099 implied HN points • 22 Nov 24
🕹 Technology Data Quality AI Models Software Development Business strategy Analytics
  1. Data quality is important for making both strategic and operational decisions, as inaccurate data can lead to poor outcomes. Good data helps companies know what customers want and improve their services.
  2. AI models can tolerate some bad data better than traditional methods because they average out inaccuracies. This means these models might not break as easily if some of the input data isn’t perfect.
  3. Businesses now care more about AI than they used to about regular data reporting. This shift in focus might make data quality feel more important, even if it doesn’t technically impact AI model performance as much.

The Weekly Kaitchup #61

The Kaitchup – AI on a Budget • 139 implied HN points • 04 Oct 24
🕹 Technology AI Models Machine Learning Computational efficiency Software Development Tech industry
  1. NVIDIA's new NVLM-D-72B model is a large language model that works well with both text and images. It has special features that make it good at understanding and processing high-quality visuals.
  2. OpenAI's new Whisper Large V3 Turbo model is significantly faster than its previous versions. While it has fewer parameters, it maintains good accuracy for most languages.
  3. Liquid AI introduced new models called Liquid Foundation Models, which are very efficient and can handle complex tasks. They use a unique setup to save memory and improve performance.