The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

Episode 23: Burkhard On Qt Embedded Systems

burkhardstubert • 19 implied HN points • 08 Nov 21
🕹 Technology Embedded Systems Software Development Architecture
  1. Hexagonal architecture is suggested as the standard for Qt embedded systems. This architecture helps organize code and makes it easier to manage.
  2. Current navigation apps in cars often lack self-learning features. A better app would remember routes taken and suggest them based on past trips.
  3. Automatic software updates are crucial for embedded systems. This helps companies quickly fix problems or add features without needing to send technicians.

Bazel: Aspirations and Realities

Subsack • 3 HN points • 22 Feb 24
🕹 Technology Software Development Startups Programming Engineering
  1. Bazel can be amazing for bigger projects, but setting it up takes a lot of time, which startups often don't have. It's crucial to focus on building a product quickly before diving into complex systems.
  2. Using Bazel with languages like Python and JavaScript can be tricky because they aren't as well supported. It can lead to a lot of wasted effort if you're not careful about the tools you choose.
  3. While Bazel has great potential, it's often not the right choice for startups due to the time and resources needed. It's better to find a simpler solution until you have a stable system.

Shaping Obsidian's Tomorrow - How Obsidian Can Embrace AI and Stay True to Its Roots

Exploring Tools for Thought • 1 implied HN point • 23 Nov 24
🕹 Technology AI Tools Productivity Software Development Privacy Innovation
  1. Obsidian is known for its focus on privacy, making it a strong tool for personal knowledge management. This is an important feature for many users who want to keep their data secure.
  2. The rise of AI presents both opportunities and challenges for Obsidian. It raises questions about how to integrate AI capabilities without losing user control or compromising privacy.
  3. There are bold ideas out there for making AI work with Obsidian. Developers can bridge the gap between AI technology and the platform while maintaining its core values.

Data Science Weekly - Issue 414

Data Science Weekly Newsletter • 19 implied HN points • 28 Oct 21
🕹 Technology Data science Machine Learning AI Software Development Data Analysis
  1. Machine learning can work with messy data. The key is to adapt techniques to handle things like missing values instead of spending all the time cleaning the data.
  2. Visualizations should be clear and focused. Good designs help people understand the information better by removing clutter and emphasizing main points.
  3. There are emerging tools and techniques that can speed up scientific discovery through faster machine learning methods. This helps researchers process data in real time and make new discoveries.

The Fine-Turning an Open Source Language Model Journey Part One: Impetus

I'll Keep This Short • 5 implied HN points • 09 Oct 23
🕹 Technology AI Language Models Artificial Intelligence Software Development Online Content
  1. Large Language Models have seen significant growth and impact, with companies like OpenAI and Amazon heavily investing in them.
  2. Safety and alignment concerns with Artificial Intelligence are important, and it's valuable to work on practical solutions.
  3. The online space is crowded with repeated ideas and groupthink, contributing to a environment where unique and nuanced ideas are less common.
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Unlocking the Power of DALL·E 3 in C#: A Comprehensive Guide for .NET Enthusiasts

The AI Observer • 3 implied HN points • 14 Feb 24
🕹 Technology AI Programming Software Development Machine Learning APIs
  1. DALL-E 3 in C# allows for high-quality image generation from textual descriptions with unique features like text incorporation, landscape/portrait compatibility, and intricate prompt interpretation
  2. Implementing DALL-E 3 in C# requires understanding API parameters and making adjustments like model selection, image dimensions, and quality for tailored image generation
  3. To avoid rate limit issues, consider upgrading plans for higher limits and be mindful of pricing details for different image quality options with DALL-E 3 in C#

Burkhard on Qt Embedded Systems: No. 5

burkhardstubert • 39 implied HN points • 30 Apr 20
🕹 Technology Embedded Systems Software Development Open Source Linux Programming
  1. Using Docker can make it easier to manage different build environments for Qt applications. It allows you to hide the complexity of the build environment while still getting the same results.
  2. There are talks about potential delays in open-source Qt releases, which could impact the community. However, it seems like these discussions may just be negotiations for better licensing terms.
  3. Continuous delivery practices can help teams perform better without sacrificing quality. By focusing on smaller, manageable changes, teams can achieve both speed and stability in software delivery.

Episode 22: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 04 Oct 21
🕹 Technology Embedded Systems Software Development User Experience IOT Programming Languages
  1. Qt 6.2 has many new features that make developing applications easier, especially with QML modules and CMake support.
  2. Parking meters can be improved with mobile apps for payments, but they need to better serve user needs for a great experience.
  3. A good solution should be user-friendly, allowing payments without internet access, and making it easy to park without confusion.

Data Science Weekly - Issue 409

Data Science Weekly Newsletter • 19 implied HN points • 23 Sep 21
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Science Software Development Analytics
  1. Trees can teach us a lot about intelligence and ecology. They inspire new ways to think about nature and our relationship with it.
  2. Before jumping into machine learning, focus on gathering quality data and building a solid framework. This can often mean starting without machine learning in your first steps.
  3. Business intelligence tools are changing and should help everyone make sense of data easily. They need to provide clear answers to data questions for all kinds of users.

Data Science Weekly - Issue 408

Data Science Weekly Newsletter • 19 implied HN points • 16 Sep 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming
  1. Many PhD and Master students need to rethink their work habits formed by years of homework and tests. It's important to develop a more flexible approach to learning and working in data science.
  2. The quality of training data is crucial in machine learning. It's no longer just about crafting better models; having good data can be a game changer for performance.
  3. Effective marketing budget allocation can be informed by Media Mix Modeling. This helps companies understand which advertising channels yield the best results for customer acquisition.

Episode 21: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 07 Sep 21
🕹 Technology Embedded Systems Software Development Business Models Pricing Strategies
  1. Productised services combine a product with some service, allowing businesses to save time and offer fixed pricing. This approach makes it easier for customers to understand costs and simplifies the process for the provider.
  2. Advisory retainers let clients access expert advice on a subscription basis, enabling them to ask questions and solve problems without the expert doing the work for them. This helps clients gain independence while still having support when needed.
  3. Workshops and trainings can be adapted from common services offered to customers, providing a platform to share knowledge while attracting new clients. This method can lead to more development projects down the line.

Data Science Weekly - Issue 407

Data Science Weekly Newsletter • 19 implied HN points • 09 Sep 21
🕹 Technology Machine Learning Data science Artificial Intelligence Computing Software Development
  1. Machine learning compilers help improve the efficiency of ML models, especially for edge computing, by addressing compatibility and performance issues.
  2. Scikit-learn, a popular machine learning library, has reached a significant version milestone at 1.0.0, showcasing its growth and community support since it started back in 2007.
  3. Synthetic data is becoming more important in computer vision, and using 3D content from the gaming and film industries can greatly enhance the process of creating such data.

Data Science Weekly - Issue 405

Data Science Weekly Newsletter • 19 implied HN points • 26 Aug 21
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Software Development
  1. Data teams should treat what they create as a product for their colleagues, focusing on what the product should feel like to ensure effective collaboration.
  2. Financial machine learning has a high failure rate, but successful managers can achieve great results; knowing the common mistakes can help avoid failure.
  3. There's a lot of potential in using AI for complex tasks, like how DeepMind's agents can play new games without prior training, showcasing advancements in reinforcement learning.

Data Science Weekly - Issue 404

Data Science Weekly Newsletter • 19 implied HN points • 19 Aug 21
🕹 Technology AI Machine Learning Data science Software Development Programming
  1. Foundation models in AI are powerful tools that can be used for various tasks like language and vision, but they come with risks like misuse and ethical concerns.
  2. Causal inference helps us understand the effects of actions in data and can be applied in tech industries to personalize services and improve decision making.
  3. MLOps focuses on effectively implementing machine learning in real-world applications, bridging the gap between traditional computing and machine learning challenges.

Why Sorry Dave?

Sorry Dave • 1 HN point • 03 Mar 24
🕹 Technology AI Software Development Bug Fixing Data science
  1. According to MIT, over 100 errors exist in every thousand lines of code, which can have serious consequences like known human deaths.
  2. Software defects cost more than $2 trillion annually, emphasizing the need for better software development methods.
  3. While AI can assist in creating safer code, it's essential to explore new approaches beyond just relying on machine learning models.

Data Science Weekly - Issue 403

Data Science Weekly Newsletter • 19 implied HN points • 12 Aug 21
🕹 Technology Machine Learning Artificial Intelligence Data science Big Data Software Development
  1. Be careful with machine learning! There are common mistakes that researchers make. It's important to build models carefully and evaluate them properly.
  2. A court in Australia has decided that AI can be considered an inventor. This is a big change in how we think about inventions and who gets credit for them.
  3. Natural Language Understanding (NLU) with just big data might not work as well as we think. It's time to rethink how we approach this challenge.

Deep Learning vs XGBoost, MXNet, Hugging Face & more...

Sector 6 | The Newsletter of AIM • 19 implied HN points • 20 Jun 21
🕹 Technology AI Machine Learning Data science Analytics Software Development
  1. Deep learning is powerful for tasks like image and speech recognition due to its complex layers. It's great for understanding patterns in large datasets.
  2. XGBoost and MXNet are tools that can be very efficient for structured data and competitions, often requiring less data than deep learning.
  3. Hugging Face is popular for natural language processing, making it easy to use advanced models without needing deep expertise in AI.

Episode 20: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 02 Aug 21
🕹 Technology Embedded Systems Consulting Software Development Project management
  1. Value pricing focuses on what customers are willing to pay and guarantees results. This approach helps both the client and consultant by reducing uncertainties about costs and outcomes.
  2. Offering multiple pricing options increases the chances of acceptance. When customers can choose between different payment plans or benefits, they feel more in control and are more likely to say yes.
  3. Switching to pre-payment and reducing work hours allows more time for business operations and future planning. This means less stress and better business health for consultants.

Data Science Weekly - Issue 400

Data Science Weekly Newsletter • 19 implied HN points • 22 Jul 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analysis
  1. Deepfake technology raises ethical questions about the use of AI-generated content without disclosure, as seen in the documentary about Anthony Bourdain.
  2. The way we use data is changing. A modern cloud data stack is becoming essential for building new businesses and improving access to data.
  3. GitHub Copilot is transforming coding by generating code automatically, making it feel like a magical assistant, though some users are still figuring out how to best use it.

How the Agile Manifesto changed nothing.

On Engineering • 7 HN points • 26 Apr 23
🕹 Technology Software Development Industry Trends
  1. The Agile Manifesto introduced new software development principles in response to industry challenges and failures.
  2. Agile prioritizes flexibility, continuous delivery, and collaboration in software development.
  3. Despite widespread adoption of agile methodologies in the tech industry, many organizations struggle to embody the cultural values at the core of agile.

Data Science Weekly - Issue 398

Data Science Weekly Newsletter • 19 implied HN points • 08 Jul 21
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Software Development
  1. Data science is actively used in many areas like music analysis and causal inference for pricing strategies. These projects help us understand large datasets and make better decisions.
  2. Languages vary in how they describe colors, reflecting cultural differences. Some cultures have fewer color terms, which sparks curiosity about societal influences on language.
  3. Combining different models, like CNNs and Transformers in computer vision, can lead to better performance. This blend helps create more accurate and diverse predictions in image-related tasks.

Episode 19: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 05 Jul 21
🕹 Technology Embedded Systems Software Development User Experience Product Management
  1. Focusing on customer experience (CX) is key for developing smarter products. Businesses should prioritize improving CX over just technical advancements.
  2. Organizational and people challenges often matter more than technology issues in product development. Enhancing team knowledge and collaboration can drive better results.
  3. Using cross-platform tools can help streamline development processes and mitigate issues like the current chip shortage in the tech industry.

Data Science Weekly - Issue 396

Data Science Weekly Newsletter • 19 implied HN points • 24 Jun 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Cloud Computing
  1. Multi-task learning helps models make several predictions at once, making them smarter. It's better than sticking to just one task.
  2. Deep reinforcement learning is changing how industries like manufacturing work by teaching machines to take actions to achieve specific goals. This can really improve efficiency.
  3. The Netflix Prize taught Netflix valuable lessons, even if the main winning entry wasn't directly useful. It's a good reminder that competitions can offer more benefits than just the final prize.

Local Llama

ScaleDown • 5 implied HN points • 15 Aug 23
🕹 Technology Artificial Intelligence Machine Learning Software Development Data science Programming
  1. Running Local Llama models can be cost-effective compared to using commercial APIs, making AI more accessible to a broader range of users.
  2. By deploying LLMs locally, users have more control over the model, allowing them to bypass limitations and ensure efficient resource utilization.
  3. Local deployment of LLMs enhances privacy and security by keeping data on the user's machine, providing an additional layer of protection.

Stick to boring architecture for as long as possible.

Elevate • 1 HN point • 19 Feb 24
🕹 Technology Software Development Innovation Engineering
  1. Stick to well-established, 'boring' technologies at the start of a project and only use new, exciting tech when it significantly adds value.
  2. Avoid the Fear of Missing Out (FOMO) in technology decisions - prioritize solutions that solve specific problems and enhance your product.
  3. Focus on delivering value with software by keeping user needs at the forefront, rather than getting lost in the latest tools and technologies.

Data Science Weekly - Issue 392

Data Science Weekly Newsletter • 19 implied HN points • 27 May 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analysis
  1. Archaeologists are using a neural network to help sort pottery fragments. This combines tech and human expertise to improve artifact classification.
  2. JavaScript is now favored for data analysis on the web. It allows for easier collaboration and better communication of insights.
  3. Companies are focusing on AI compliance and risk management. There's a growing need for legal support to handle AI-related challenges.

#50

The Nibble • 2 implied HN points • 09 Mar 24
🕹 Technology AI Blockchain Cybersecurity Social media Software Development
  1. Amazon purchased a 100% nuclear-powered data center for $650M in Pennsylvania, highlighting a move towards clean energy but raising concerns about actual environmental impact.
  2. India's Ministry of Electronics and IT mandated significant AI firms to avoid bias and secure government approval before deploying AI models, sparking debates and criticism.
  3. Sony filed a patent for 'Super fungible tokens' for gaming, aiming to attach value to in-game items for potential real-money trading, introducing a new concept in gaming.

4 essential reads for devs this week

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

How to Create Email Sequences on Substack

Implementing • 1 HN point • 12 Feb 24
🕹 Technology Automation Software Development Software Engineering
  1. Automating email sequences on Substack requires a reverse engineering approach to understand platform communication and mimic manual steps with a bot.
  2. The email sequence system on Substack can be customized with various workflows and features like filtering subscribers, creating draft emails, and scheduling workflow executions.
  3. Successful case studies like Refactoring newsletter show how implementing automated email sequences can streamline tasks and engage subscribers effectively.

Data Science Weekly - Issue 383

Data Science Weekly Newsletter • 19 implied HN points • 25 Mar 21
🕹 Technology AI Data science Machine Learning Software Development Programming
  1. Artificial intelligence is making big strides in drug discovery, helping researchers tackle important problems more effectively. It's great to see technology playing a role in improving health outcomes.
  2. Jupyter notebooks are a popular tool among data scientists for data analysis and exploration, but some find them tricky to manage in production environments. It's a love/hate relationship for many users.
  3. Machine learning is becoming a key player in game development, helping to test and balance games more efficiently. This could lead to better gaming experiences for everyone.

LLMs are Level 2 Automation

Maestro's Musings • 7 HN points • 21 Feb 23
🕹 Technology AI Automation Machine Learning Artificial Intelligence Software Development
  1. Large Language Models like ChatGPT are currently at Level 2 Automation, not full self-driving.
  2. LLMs have limitations in handling end-to-end scenarios consistently and may require human guidance for accuracy.
  3. Utilizing LLMs effectively involves structuring applications around their limitations and validating outputs before high-stakes actions.

Microservices - Getting started

Overflow • 2 HN points • 20 May 23
🕹 Technology Software Development Architecture Web Development Coding
  1. Monolithic applications have tightly coupled code, making it difficult to add new features and scale beyond a point.
  2. Microservices architecture involves breaking down applications into smaller, independent services to solve problems like scalability and deployment dependencies.
  3. Common problems with monolithic applications include challenges in adding new features, intimidating codebase for new team members, and difficulties in updating technology stack.

Effective ChatGPT Prompting for software developers

Cooking Without Borders • 2 HN points • 18 May 23
🕹 Technology Software Development AI Machine Learning Programming Web applications
  1. Prompting LLMs, like ChatGPT, requires clear and specific instructions to get valuable output.
  2. Give the model time to 'think' by breaking down complex tasks into smaller steps for improved accuracy.
  3. Use techniques like few-shot prompting to guide the model towards desired outputs and prevent prompt injection.

The 5 Stages of a Software Teams Life Cycle

The Software Engineering Times • 2 HN points • 23 Feb 24
🕹 Technology Software Development
  1. Teams go through 5 stages in their life cycle: forming, storming, norming, performing, and adjourning.
  2. During the storming stage, teams face hurdles, disagreements, and lower performance, but overcoming these challenges is progress towards improvement.
  3. In the adjourning stage, a team may feel a mix of accomplishment and sadness as they complete their goals and may disband as members move on to other projects.

Episode 15: Burkhard on Qt Embedded Systems

burkhardstubert • 19 implied HN points • 28 Feb 21
🕹 Technology Embedded Systems Software Development Code Quality Programming
  1. There are events happening for Qt embedded systems, and the deadlines for presenting are coming up soon. If you want to share your work, make sure to submit your proposals on time!
  2. When writing code, it's important to make it readable by using good names and comments. Bad names should be replaced with clearer function names instead of relying on comments to explain them.
  3. Focus on breaking down your code into smaller, manageable functions. Each function should do one task well, which makes it easier to read and understand.