The hottest Software Engineering Substack posts right now

And their main takeaways
Category
Top Technology Topics

Details on the Newsletter giveaway.

Technology Made Simple • 0 implied HN points • 05 Dec 22
🕹 Technology Software Engineering
  1. The newsletter is offering a year of premium subscription for free to anyone interested, with no obligations or payments required.
  2. To participate, all you need to do is provide your email in a Google Form linked in the post.
  3. Current paid subscribers facing financial difficulties can reach out to the author to receive the newsletter for free.

Turbo Streams vs. consistency

AnyCable Broadcasts • 0 implied HN points • 19 Jul 22
🕹 Technology Software Engineering
  1. The episode discusses making Turbo Streams reliable and switching from 'at-most once' to 'exactly-once' delivery guarantees
  2. The importance of consistency in real-time applications and the pitfalls to avoid are highlighted
  3. A preview of an upcoming AnyCable feature on 'streams history support' is provided at the end of the episode

Generative AI Opportunities for Businesses 

Tributary Data • 0 implied HN points • 05 Mar 24
🕹 Technology Software Engineering
  1. Generative AI can help businesses drive innovation, efficiency, and success by leveraging cutting-edge data analytics and AI technologies.
  2. Large Language Models like Agatha can provide conversational interfaces, streamlining access to company knowledge and insights, leading to enhanced productivity and decision-making for employees.
  3. Agatha enables automation of tasks, such as generating personalized emails, summarizing transcripts, and generating code snippets, helping save time, improve efficiency, and foster creativity across various departments.

Hello World 👋

BauZen • 0 implied HN points • 28 Oct 23
🕹 Technology Software Engineering
  1. The author transitioned from a traditional daily blog to using Hugo, a static site generator, to make blogging easier and more efficient.
  2. By using Hugo, the author could focus on content creation without worrying about infrastructure management and enjoy benefits like reduced attack surface and generous free hosting tiers.
  3. The blog will cover technology decisions for startups, team management, translating tech to business, and bridging technical terms for business leaders, all while being written based on personal interests and experiences.

Elixir Enthusiasm: Insights from Senior Software Engineer Eduardo Borsa

Elixir & Erlang • 0 implied HN points • 03 Apr 24
🕹 Technology Software Engineering
  1. Eduardo Borsa shares his journey into software engineering and the factors that sparked his interest in Elixir.
  2. Listeners gain insights into the projects Eduardo is involved in at Loomis, Sayles & Company, and the technologies he finds exciting and challenging.
  3. Eduardo offers recommendations for staying up-to-date with developments in the field, including books, blogs, podcasts, and courses.
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RDEL #36: What are the most common conversations developers have with ChatGPT?

Research-Driven Engineering Leadership • 0 implied HN points • 01 Apr 24
🕹 Technology Software Engineering
  1. The most common conversations developers have with ChatGPT include code generation, conceptual questions, how-to guides, issue resolution, and code review.
  2. Developers often engage in multi-turn conversations with ChatGPT to refine or expand upon its responses.
  3. Sharing ChatGPT conversations with teammates can aid in knowledge transfer, clarification in code reviews, and issue discussions.

RDEL #30: How does imposter syndrome impact developer productivity?

Research-Driven Engineering Leadership • 0 implied HN points • 19 Feb 24
🕹 Technology Software Engineering
  1. Imposter syndrome affects software professionals significantly, with over half experiencing intense imposter feelings.
  2. Software engineers with imposter syndrome reported lower productivity across various dimensions, highlighting the negative impact on performance.
  3. Managers can help reduce imposter feelings in their team by prioritizing psychological safety, implementing mentorship programs, and addressing diversity to improve team dynamics and productivity.

RDEL #11: What is the relationship between self-reported productivity and system-measured productivity?

Research-Driven Engineering Leadership • 0 implied HN points • 25 Sep 23
🕹 Technology Software Engineering
  1. Combining self-reported data with system-measured data provides a more complete picture of productivity in software engineering.
  2. Long coding stretches can positively impact a developer's perception of productivity.
  3. Sharing productivity data with the team can empower engineers and improve overall productivity.

RDEL #8: How does nationality and team virtualization impact software teams?

Research-Driven Engineering Leadership • 0 implied HN points • 05 Sep 23
🕹 Technology Software Engineering
  1. Social dynamics are crucial for software engineering teams' productivity. Factors like team distribution and cultural diversity significantly impact how well teams work together.
  2. Teams with diverse nationalities experience fewer challenges and disagreements. Diversity leads to more acceptance of various viewpoints and procedures.
  3. Virtual teams face challenges like lack of experience due to the nature of remote work. Active efforts like creating dedicated time for team-building help reduce friction in remote collaborations.

RDEL #6: Software Engineering Productivity: How do you measure it? (Part II)

Research-Driven Engineering Leadership • 0 implied HN points • 21 Aug 23
🕹 Technology Software Engineering
  1. There is no single measure of productivity in software engineering; a combination of different metrics across satisfaction, performance, activity, communication/collaboration, and efficiency/flow are crucial.
  2. The SPACE framework, introduced by researchers at Microsoft in 2021, is a popular and effective way to measure developer productivity, focusing on dimensions like satisfaction, performance, activity, communication/collaboration, and efficiency/flow.
  3. When implementing the SPACE framework, start with a clear goal, choose metrics that cover diverse categories, include both system and perception metrics, conduct regular developer surveys, and engage the team to make productivity measurement a collective effort.

RDEL #5: Software Engineering Productivity: How do you define it? (Part I)

Research-Driven Engineering Leadership • 0 implied HN points • 14 Aug 23
🕹 Technology Software Engineering
  1. Software engineering productivity is challenging to define due to the complexity of human input in the software development process.
  2. Productivity in software engineering involves a balance of efficiency, effectiveness, and quality.
  3. Measuring a team's productivity requires evaluating both the efficiency of input and the effectiveness (including quality) of output.

16th Minute Newsletter 1.0.0

realkinetic • 0 implied HN points • 24 Jun 24
🕹 Technology Software Engineering
  1. 16th Minute newsletter covers a range of tech topics from compound AI systems to data structures.
  2. AI development is shifting towards compound AI systems where operations and systems thinkers play vital roles.
  3. Multi-tenancy in Kubernetes is an important area to explore for those working on enterprise software.

Data Engineering: A Software Engineer’s Approach

realkinetic • 0 implied HN points • 25 Jan 24
🕹 Technology Software Engineering
  1. The tech industry varies in its expectations of data engineers, leading to challenges in team performance and hiring.
  2. Companies today need to be data-driven, utilizing modern data stack tools, which necessitates a blend of data engineering and software engineering skills.
  3. Data engineering benefits from adopting software engineering principles like treating systems as products, clear communication, and implementing CI/CD pipelines.

The Power of Mathematics: Can we turn Software Engineers into Quantum Founders?

Deep-Tech Newsletter • 0 implied HN points • 18 Aug 20
🕹 Technology Software Engineering
  1. Future coders will need to embrace mathematics and data analysis for problem-solving.
  2. Quantum computing requires a solid mathematical background for significant contributions to quantum algorithms.
  3. Efforts are being made to train engineers in mathematics to create new quantum software companies and challenge the current academic dominance in the quantum startup ecosystem.

Quantum Formalism Batch I

Deep-Tech Newsletter • 0 implied HN points • 30 Jul 20
🕹 Technology Software Engineering
  1. The 12-week online course Quantum Formalism offers two tracks: one for those new to quantum computing and the other for professionals seeking advanced math topics.
  2. Upskilling in quantum software engineering can boost startups' access to talented engineers and counter the recruitment dominance of big tech companies like Google and IBM.
  3. The program emphasizes diversity and plans to expand student numbers to admit more women and people of color, while also seeking partnerships with incubators and quantum startups.

Beyond SOLID Principles

🔮 Crafting Tech Teams • 0 implied HN points • 14 Jun 23
🕹 Technology Software Engineering
  1. The SOLID principles in software engineering are important for creating understandable, flexible, and maintainable object-oriented designs.
  2. Many engineers are recommended to learn the SOLID principles early in their tech training, but some professionals have stopped recommending it.
  3. Crafting Tech Teams offers insights beyond the traditional SOLID principles, providing a fresh perspective on software design and development.

Your Estimates Are Wrong

Tribal Knowledge • 0 implied HN points • 16 Feb 23
🕹 Technology Software Engineering
  1. Estimation is a significant part of a software engineer's job, even though it might not always be explicitly stated in job descriptions.
  2. Uninformed estimates can lead to significant issues, such as underestimating the time needed due to lack of understanding or unforeseen challenges.
  3. Setting deadlines through estimates can promote productivity but may sometimes result in rushed work and a compromise on quality.

Iteration vs Planning

Tribal Knowledge • 0 implied HN points • 22 Jan 23
🕹 Technology Software Engineering
  1. Agile methodology focuses on iteration and adapting to changes, while Waterfall emphasizes on planning everything upfront. Both have their tradeoffs and can work well for different reasons.
  2. The iterative approach involves delivering incrementally, receiving constant feedback, and adjusting along the way. It allows for flexibility but can lead to bugs and customer frustration initially.
  3. The planning approach involves detailed upfront planning, building according to the plan, and announcing the product only after completion. It provides a structured process but may lead to delayed reactions to customer feedback and potential rework.

Newsletter #11: System Design for Machine Learning - Part I

Decoding Coding • 0 implied HN points • 04 May 23
🕹 Technology Software Engineering
  1. Before starting on a machine learning project, it's important to define clear goals and understand how ML can help achieve them.
  2. Setting up a data pipeline is crucial; it involves collecting, preparing, and analyzing data to see what features are useful for your model.
  3. When deploying machine learning models, you need to consider both hardware and software needs, including how to handle real-time data for ongoing training.

Google’s Q*?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 14 Dec 23
🕹 Technology Software Engineering
  1. Google's AlphaCode 2 has improved significantly, performing better than the earlier version by solving many coding challenges. It shows that Google's advancements in AI are making big leaps.
  2. AlphaCode 2 ranks in the 85th percentile among competitors, meaning it outperforms most human participants in coding competitions. This suggests that AI is becoming very capable in technical problem-solving.
  3. Many people are focused on Google's Gemini project, but AlphaCode 2 might be a game-changer in competitive coding, indicating a shift in how powerful AI tools can be for programmers.

How to Improve Android App Performance

Better Engineers • 0 implied HN points • 22 Apr 24
🕹 Technology Software Engineering
  1. Identify and monitor app performance issues, like slow rendering and frozen frames, to enhance user experience. It's important to keep the app responsive to avoid losing users.
  2. Use tools like Firebase Performance Monitoring to track performance data and identify problems, such as long UI thread operations that can cause jank.
  3. Implement strategies to improve performance, like using View Stubs for loading UI components on demand and avoiding long tasks on the main thread, which can make the app freeze.

Magic of Abstraction in Kotlin

Better Engineers • 0 implied HN points • 06 Jul 22
🕹 Technology Software Engineering
  1. Abstraction helps hide complex code, making it easier to manage and change later. This way, users don’t need to see all the details, which simplifies their experience.
  2. Using constants instead of magic numbers improves clarity and makes future changes easier. By giving a meaningful name to a constant, we can change its value without affecting the logic in our functions.
  3. Creating interfaces allows for flexibility in our code. We can build different implementations for the same interface, making it easier to adapt the software for different platforms or needs.

My Path to Become a Senior Software Engineer

Peak Performance by David Goudet • 0 implied HN points • 05 May 24
🕹 Technology Software Engineering
  1. Struggling is part of growth; even successful engineers face challenges and work hard to improve. It's important to embrace the journey, including failures, to reach your goals.
  2. Dealing with clients taught valuable lessons about the stress of the job and the importance of understanding different roles in business. This experience can change how you appreciate entrepreneurship.
  3. Preparing for senior software engineer roles goes beyond coding; it involves studying patterns, practicing regularly, and improving routines. A good mindset and consistent effort can lead to success in interviews and your career.

What hardware do I need to create a LLM?

Shrek's Substack • 0 implied HN points • 18 Apr 23
🕹 Technology Software Engineering
  1. Training large language models (LLMs) needs powerful hardware, often multiple A100 GPUs with 40GiB of VRAM each. Running them is cheaper than training.
  2. Different data types like FP16 and TF32 are crucial for handling model memory. New types help manage larger numbers while saving memory.
  3. For smaller models, single hardware can work, but bigger models need a lot of VRAM or multiple systems. There's a difference between training and running models efficiently.

Develop Generative Apps Locally

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Feb 24
🕹 Technology Software Engineering
  1. You can create generative apps that run completely on your own computer. This makes development easier and often faster.
  2. Using tools like HuggingFace and TitanML's TakeOff Server, you can access and manage small language models without needing an internet connection.
  3. Running inference locally improves speed, keeps your data private, and lets you work offline when needed.

LLM-Generated Self-Explanations

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Dec 23
🕹 Technology Software Engineering
  1. LLMs can make predictions and explain how they arrived at those predictions. This helps in understanding their reasoning better.
  2. Using a 'Chain of Thoughts' method can improve LLMs' ability to solve complex tasks, especially in areas like math and sentiment analysis.
  3. There's a need for better ways to evaluate the explanations given by LLMs because current methods may not accurately determine which explanations are effective.

Stop using UUIDs: The Modern ID Spec

Getting Traction • 0 implied HN points • 12 May 24
🕹 Technology Software Engineering
  1. UUIDs are not always the best choice for identifiers because they're long and hard to read. A new approach suggests using shorter, more human-friendly IDs that are easier to copy and work with.
  2. The modern ID format uses a prefix for the table and a suffix for uniqueness, allowing for better organization and user experience. This means URLs can be cleaner and easier to understand.
  3. Different tables can have different suffix lengths based on their volume and sensitivity, making it flexible. It also makes it easier to manage potential ID conflicts as your database grows.

Segregate resources and good hygiene between AWS accounts

Thoughts from the trenches in FAANG + Indie • 0 implied HN points • 06 Jun 23
🕹 Technology Software Engineering
  1. Using different AWS accounts for each project helps keep resources separate and makes billing easier. This way, it's simple to track costs for each project.
  2. Having separate accounts reduces confusion and complexity for engineers. It keeps projects organized, making it easier to find the resources they need and limits mistakes.
  3. Segregated accounts also improve security, as a problem in one account won't affect others. This protects against errors and minimizes potential damage from mismanaged access.

Internal Storage Design of Modern Key-value Database Engines [Part 3]

Practical Data Engineering Substack • 0 implied HN points • 13 Aug 23
🕹 Technology Software Engineering
  1. Compaction is an important process in key-value databases that helps combine and clean up data files. It removes old or unnecessary data and merges smaller files to make storage more efficient.
  2. Different compaction strategies exist, like Leveled and Size-Tiered Compaction, each with its own benefits and challenges. The choice of strategy depends on the database's read and write patterns.
  3. The RUM Conjecture explains the trade-offs in database optimization, balancing read, write, and space efficiency. Improving one aspect can worsen another, so it's key to find the right balance for specific needs.

Internal Storage Design of Modern Key-value Database Engines [Part 2]

Practical Data Engineering Substack • 0 implied HN points • 09 Aug 23
🕹 Technology Software Engineering
  1. Sorted Segment files, or SSTables, help databases manage data more efficiently by keeping key-value records in order. This sorting makes searching and accessing data faster.
  2. In-memory storage, called Memtables, acts like a buffer that groups new data before it's saved to disk. This keeps data organized and speeds up how quickly new information can be written.
  3. Using a structure called the LSM-Tree helps optimize how databases write and read data. It focuses on reducing the time and effort it takes to handle a lot of updates and inserts, which is common in many apps.

Simplify scaling to big customers with tags

CommandBlogue • 0 implied HN points • 28 May 24
💼 Business Software Engineering
  1. As companies grow, they often need to add complex features to their products, which can be overwhelming. A simple way to manage this complexity is by using tags to help organize information better.
  2. Tags allow users to categorize items in multiple ways, making it easier to find what they need. Unlike folders, tags let you label something with several different tags without duplication.
  3. Implementing a tagging feature is essential for products as they scale, providing flexibility and improving user experience. It helps keep things organized even when users have lots of content.

Range Partitioning: Zero to One

aspiring.dev • 0 implied HN points • 17 Mar 24
🕹 Technology Software Engineering
  1. Range partitioning splits data into key ranges to improve performance and scalability. This method helps databases manage heavy loads by distributing data efficiently.
  2. Unlike hash partitioning, range partitioning allows for easier scaling. You can adjust the number of ranges as needed without the hassle of rewriting data.
  3. While range partitioning is powerful, it can be tricky to implement and may struggle with very sequential workloads. Planning is necessary to avoid creating performance hotspots.

Frontlink: React realtime collaboration and updates with your backend

aspiring.dev • 0 implied HN points • 21 Feb 24
🕹 Technology Software Engineering
  1. With Frontlink, you can easily add real-time collaboration features to your React app. It allows you to share state and functions among users, making the experience interactive.
  2. You can bring your own backend when using Frontlink, which gives you full control over your app's operations. This means you can tailor the features exactly to your needs without relying on third-party services.
  3. Setting up Frontlink is straightforward, requiring just a few lines of code to start. You can seamlessly integrate it into your existing React app and manage shared states efficiently.

[in case you missed it] Data Science Weekly - Issue 470

Data Science Weekly Newsletter • 0 implied HN points • 27 Nov 22
🕹 Technology Software Engineering
  1. Recommender systems often focus on increasing user engagement, but this can lead to unintended negative effects like addiction. A new understanding of user preferences could help create better recommendations.
  2. GitLab's Data Team Handbook shares valuable information on how data is used in various business functions. It's organized into helpful sections that explain dashboards, team operations, and current projects.
  3. Deep learning is being used to test video games like Candy Crush for more human-like gameplay. This approach is explored by researchers from gaming companies, highlighting the potential for better game design.

[in case you missed it] Data Science Weekly - Issue 468

Data Science Weekly Newsletter • 0 implied HN points • 13 Nov 22
🕹 Technology Software Engineering
  1. Before leaving Twitter, it's a good idea to download and save your data. This way, you can analyze important trends and insights you might miss if you just leave.
  2. The command line can make data processing easier and more readable. New tools like SPyQL help bridge familiarity with SQL and Python for better data analytics.
  3. Federated learning allows multiple users to train models without sharing their raw data. This technology can enhance privacy while still allowing valuable insights from diverse data sources.

[in case you missed it] Data Science Weekly - Issue 460

Data Science Weekly Newsletter • 0 implied HN points • 18 Sep 22
🕹 Technology Software Engineering
  1. Data scientists need soft skills like communication and teamwork. These skills help them work better with others and tell stories from data.
  2. There's a lot of free, live-streamed data science content available on Twitch. This makes it easier for everyone to learn and connect with the data science community.
  3. Understanding how to use AI tools for content generation can open up new creative possibilities. These tools can help enhance projects in various ways.

[in case you missed it] Data Science Weekly - Issue 416

Data Science Weekly Newsletter • 0 implied HN points • 14 Nov 21
🕹 Technology Software Engineering
  1. ML platforms are crucial for turning models into valuable tools, and each tech company has its own approach and tools to integrate machine learning effectively.
  2. While Kubernetes has advantages for managing data engineering, it's not always necessary and can be frustrating for engineers just wanting to help the business use data better.
  3. New large language models are emerging, making GPT-3 less unique; people are working on creating similar models that could soon be available.