The hottest Software Engineering Substack posts right now

And their main takeaways
Category
Top Technology Topics

Math for Software Engineering[Math Mondays]

Technology Made Simple • 139 implied HN points • 21 Mar 23
🕹 Technology Mathematics Software Engineering Data Analysis Algorithms Machine Learning
  1. Linear Algebra is crucial for software engineers, especially for operations involving vector and matrix operations. Understanding the basics is key for most developers.
  2. Probability and Statistics play a significant role in analyzing data, and even non-AI professionals can benefit from grasping concepts like causal inference. Focus on foundational principles before diving deeper.
  3. Calculus, though important, may not be essential for all software engineers. Studying up to Calc-2 is generally adequate, as it appears in various other topics.

Weekly Top Picks #67

The Algorithmic Bridge • 116 implied HN points • 18 Mar 24
🕹 Technology AI Tech news Artificial Intelligence Software Engineering Data science
  1. The post discusses Nvidia GTC keynote, BaaS in science, Apple's potential collaboration with Google Gemini, and more key AI topics of the week.
  2. It features conversations between Sam Altman and Lex Friedman, touches on jobs in the AI era, and examines the response from NYT to OpenAI.
  3. There's a question about whether OpenAI's Sora model is trained using YouTube videos, among other intriguing topics.

Engineering Managers Should Not Exist

Victor’s Substack • 41 HN points • 26 Mar 24
🕹 Technology Management Software Engineering Project management HR Consulting
  1. Software engineering managers should not exist as they generally take on multiple roles poorly, whereas specialists could excel at each task.
  2. Engineering managers often were mediocre engineers who compensated by picking up non-engineering tasks and ended up in managerial roles.
  3. Best teams often function well without an engineering manager observing their every move, allowing engineers to focus and be more productive.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 497

Data Science Weekly Newsletter • 199 implied HN points • 02 Jun 23
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Statistical Analysis
  1. Data drift doesn't always hurt model performance, so it's important to analyze the context before reacting to it.
  2. Work on solving bigger problems as you grow in your career, instead of waiting for difficult tasks to be handed to you.
  3. To improve a model's reasoning skills, reward it for each correct step in problem-solving, not just the final answer.

A deeper than average look into Stacks [Math Mondays]

Technology Made Simple • 99 implied HN points • 21 Nov 23
🕹 Technology Programming Data Structures Algorithms Computer Science Software Engineering
  1. Stacks are powerful data structures in software engineering and can be modified extensively to suit different use cases.
  2. Implementing Stacks using a Singly Linked List can be beneficial for dynamic resizing, though Arrays are often preferred due to memory considerations.
  3. Exploring variations like Persistent Stacks, Limiting Stack Size, Ensuring Type Safety, Thread Safety, Tracking Min/Max, and Undo Operations can enhance the functionality and efficiency of Stacks in various scenarios.

Exploring recent Python repositories

Mostly Python • 314 implied HN points • 22 Jun 23
🕹 Technology Programming Open Source Web Development Software Engineering APIs
  1. Use the GitHub API to explore popular new Python projects and find potential projects to contribute to.
  2. Consider filtering out AI-focused projects when exploring Python repositories to discover a variety of coding projects.
  3. Pruning repositories using specific terms can help identify non-AI Python projects to work on, providing valuable learning opportunities.

3 Techniques to help you optimize your code bases[Technique Tuesdays]

Technology Made Simple • 119 implied HN points • 26 Apr 23
🕹 Technology Coding Optimization Software Engineering Developer Tools
  1. Compile time evaluation can help execute functions at compile time instead of run time, saving memory and CPU time.
  2. Dead code elimination removes unused code, enhancing code readability and reducing executable size.
  3. Strength reduction is a compiler optimization technique that replaces expensive operations with simpler ones, making localized code changes easier.

AI and Knowledge Work

Tanay’s Newsletter • 119 implied HN points • 22 Feb 24
🕹 Technology AI Software Engineering Customer Support Consulting Productivity
  1. AI is enhancing productivity and quality in knowledge work like software engineering and customer support.
  2. AI benefits are not uniform; it tends to help lower performers more, but can also assist top performers by reducing menial tasks.
  3. AI is not a cure-all; it has limitations and understanding when to use it is crucial for optimal results.

The Future of Programming: Copilots vs. Agents (Part I)

East Wind • 11 implied HN points • 12 Nov 24
🕹 Technology AI Development Software Engineering Tech industry Product Design
  1. The competition to create better AI coding tools is intense. Companies are racing to attract developers and dominate a huge market.
  2. AI coding tools can be divided into three types: copilots, agents, and custom models. Each type has its own approach to helping programmers finish their work.
  3. User experience is very important for these tools. Small differences in how they function can greatly affect how easy they are to use.

Measuring PR throughput—perspectives from SPACE author Brian Houck

Engineering Enablement • 8 implied HN points • 03 Dec 24
🕹 Technology Developer Tools Productivity Software Engineering AI Tools Remote work
  1. PR throughput is a useful metric for understanding the health of a software system. It can highlight issues that developers face while coding, helping teams identify where improvements can be made.
  2. It's important to use PR throughput as part of a larger set of metrics. This approach helps ensure that you get a balanced view of productivity, developer satisfaction, and overall system efficiency.
  3. When measuring PR throughput, context matters. A rise in this metric can mean different things, like increased workloads or improved processes, so it's essential to look deeper into the reasons behind the changes.

Global Incremental ID

Hung's Notes • 79 implied HN points • 13 Dec 23
🕹 Technology Data Structures Distributed Systems Software Engineering Database Management
  1. Global Incremental IDs are important for preventing ID collisions in distributed systems, especially during tasks like data backup and event ordering.
  2. UUID and Snowflake ID are two common types of global IDs, each with unique advantages and disadvantages. For instance, UUIDs are larger but widely used, while Snowflake IDs are smaller but more complex to generate.
  3. Different systems, like Sonyflake and Tinyid, offer specialized methods for generating IDs, helping to ensure performance and avoiding database bottlenecks.

Changing my mind on whether managers should keep coding

peoplefirstengineering • 8 implied HN points • 05 Dec 24
🕹 Technology Software Engineering Management Empathy Leadership Coding
  1. It's important for managers to stay connected to coding, as it helps them empathize with their team. Being hands-on can improve understanding of the challenges engineers face.
  2. Empathy in leadership goes beyond just understanding tasks; it involves recognizing individual emotions and how they affect performance. Good managers should tailor their support based on team members' unique situations.
  3. Challenges in coding create a shared experience among team members. Managers who code can better relate to the ups and downs, building trust and a stronger team dynamic.

Why "bias for action" is not good enough

The Caring Techie Newsletter • 11 implied HN points • 12 Nov 24
🕹 Technology Software Engineering Project management Leadership Decision-making Risk management
  1. Having a 'bias for action' can be good, but it's not always the right approach. Sometimes, acting without enough thought can lead to bigger problems.
  2. In situations where you don't fully understand the problem, it might be better to wait and gather more information before jumping to conclusions.
  3. Instead of rushing into decisions, take a moment to think things through. Thoughtful action can help you make better choices.

Tech Talks Weekly #9 ☀️ ☀️ ☀️

Tech Talks Weekly • 39 implied HN points • 04 Apr 24
🕹 Technology Software Engineering Conferences Web Development Programming Community Building
  1. Recent tech talks from big conferences like GOTO and KubeCon are highlighted. It's a great opportunity to learn from industry experts.
  2. There's a new section showcasing popular talks from past issues. This helps viewers discover content that others found useful.
  3. The newsletter encourages sharing it with friends and coworkers to build a community around tech talks. More recommendations mean more engagement and learning together.

Research -> Reality. AI Engineers.

potentialmind • 19 implied HN points • 18 May 24
🕹 Technology Machine Learning Data Engineering Software Engineering Product Management
  1. The demand for AI Engineers is skyrocketing due to advancements in AI, making it a high-demand engineering job of the decade.
  2. To excel in AI Engineering, practical knowledge and hands-on experience are prioritized over traditional academic qualifications like PhDs or specific courses like PyTorch.
  3. Modern applied AI is changing the landscape, making it easier for software engineers and product managers to leverage large language models and AI frameworks without extensive data collection.

How to be safe from infinite loops[Technique Tuesdays]

Technology Made Simple • 99 implied HN points • 28 Jun 23
🕹 Technology Coding Programming Software Engineering Cybersecurity Innovation
  1. The Jolt Approach provides a simple yet powerful method to detect and escape infinite loops by monitoring program progress and identifying loop iterations that produce the same state, which is an indication of being stuck in a loop.
  2. It's crucial to address the limitations of the Jolt Approach, especially in scenarios where programs cycle between different states, by implementing strategies like storing each state, breaking if a seen state recurs, and having code that accounts for these situations.
  3. To safeguard against infinite loops, ensure your code progresses in every step, have clearly defined exit strategies, use kill-switches like iteration limits, and implement thorough logging for observability.

The Pigeonhole Principle [Math Mondays]

Technology Made Simple • 99 implied HN points • 11 Apr 23
🚌 Education Math Algorithm Design Computer Science Software Engineering
  1. The Pigeonhole Principle states that if you have more items than containers, at least one container must hold more than one item.
  2. In software engineering, the principle ensures the correctness and efficiency of algorithms, especially in large-scale system design.
  3. The Pigeonhole Principle can also be used to prove non-existence, such as showing the impossibility of a universal lossless compression algorithm.

3 Techniques to Make your Machine Learning more efficient[Technique Tuesdays]

Technology Made Simple • 99 implied HN points • 04 Apr 23
🕹 Technology Machine Learning AI Software Engineering Tech industry Data science
  1. Reducing the number of features in your data can improve performance and keep costs down in machine learning processes.
  2. Active learning focuses on prioritizing data points for efficient machine learning model training.
  3. Using filters and simpler models for specific tasks can lead to better performance and cost savings compared to always using large, powerful models in AI.

How to save your systems from collapsing- Graceful Degradation [Technique Tuesdays]

Technology Made Simple • 99 implied HN points • 03 May 23
🕹 Technology Software Design System reliability Web Development Software Engineering AI
  1. Graceful Degradation is a design principle that ensures systems maintain limited functionality even when parts are rendered inoperative.
  2. Implement the 80-20 principle while integrating Graceful Degradation to focus on backing up critical components and keeping systems active.
  3. Combine Progressive Enhancement with Graceful Degradation to provide essential content to all users while delivering the best possible experience to modern browsers.

🗓 Your invitation to weekly craftsmanship topics

🔮 Crafting Tech Teams • 59 implied HN points • 08 Jan 24
🕹 Technology Software Engineering Architecture Design
  1. Weekly craftsmanship topics cover Modern Frontend Engineering Culture and Software Engineering Fundamentals Series. They delve into architecture, design techniques, and XP disciplines.
  2. Upcoming events include discussions on Modern Frontend Development and Object-Oriented Programming systems.
  3. Subscribe to Crafting Tech Teams for a 7-day free trial to access full post archives and continue learning about these topics.

An Introduction to DAOS- The overlooked side of AI [Math Mondays]

Technology Made Simple • 79 implied HN points • 14 Nov 23
🕹 Technology AI Computer Science Storage Software Engineering
  1. DAOS is integral to High-Performance Computing and offers cutting-edge storage capabilities for next-generation computing.
  2. There is a serious lack of talent in developing DAOS products, making it a lucrative field for skill development and career advancement.
  3. The architecture of DAOS utilizes next-generation NVM technology and operates in user space with full OS bypass, offering lightweight and high-performance storage solutions.

Do You Want to Remove "If" Statements From Your Java Code?

Weekend Developer • 19 implied HN points • 11 May 24
🕹 Technology Programming Refactoring Software Engineering Error Handling Testing
  1. To reduce reliance on 'if' statements in Java code, use guard statements at the beginning of methods or blocks to quickly exit when conditions aren't met.
  2. Consider using ternary operators as a concise way to replace 'if-else' statements within methods, but be cautious of overusing them for readability.
  3. Separate logic into reusable policy rules, encapsulating business rules into classes or methods, to promote code organization and maintainability.

Breaking into ML as a New Grad

Kndrej’s Substack • 3 HN points • 14 Aug 24
🕹 Technology AI Machine Learning Software Engineering Data science Job Market
  1. Breaking into machine learning (ML) requires not just basic knowledge but also a deep understanding of the math and engineering behind models. Completing online courses is only a starting point.
  2. Internships and real project experience are crucial for landing a job in ML. It's important to have skills that stand out, like publications or open-source contributions.
  3. Interview preparation is key; practicing coding challenges and understanding ML concepts is necessary to succeed. Networking and applying quickly to job postings can improve your chances.

5 Easy Tips for Writing Cleaner Code [Technique Tuesdays]

Technology Made Simple • 119 implied HN points • 11 Jan 23
🕹 Technology Coding Software Engineering Clean Code Tips
  1. Clean code is essential for software engineering success, especially in large companies where code reviews play a crucial role in promotions.
  2. Using pure functions, named parameters, and meaningful variable names are key techniques to ensure cleaner code.
  3. Avoid hard-coding values and utilize default values to improve code readability, maintainability, and reduce complexity.

Breaking orthodoxies in venture capital

SarHaribhakti's Newsletter • 355 implied HN points • 25 Feb 23
💼 Business Venture Capital Software Engineering
  1. Root Ventures breaks traditional VC norms by focusing on hard tech, staying close to seed roots, and having all engineer partners.
  2. It's important for investors to avoid overfitting their models and to keep building new projects to stay relevant.
  3. Technical founders should focus on hiring employees who fit the startup environment and prioritize results over corporate measures.

A neat way to catch unrelaible data [Technique Tuesdays]

Technology Made Simple • 79 implied HN points • 07 Jun 23
🕹 Technology Machine Learning Software Engineering Computer Science
  1. Feature Drift occurs when the distribution of the features being tracked changes, and it is a subset of Data Drift.
  2. Detecting Feature Drift can be tricky when tracking numerous variables, potentially leading to detrimental outcomes over time.
  3. A technique to catch Feature Drift involves creating artificial target variables based on old and new data sets, then using a simple Supervised Learning algorithm to identify drifting features.

Software Engineering is Coming for More Than Data Tools/Practices: It's Coming for Identity

Sung’s Substack • 79 implied HN points • 10 Jul 23
🕹 Technology Software Engineering Data Tools Identity
  1. Software engineering is evolving to impact more than just data tools and practices - it's influencing identity within the industry.
  2. The data industry is experiencing a significant shift towards merging software and data engineering, requiring a new level of ownership and empathy between the two.
  3. The goal is to create a world where data pipelines are more proactive than reactive, data as a product is ubiquitous and pain within the industry is minimized, leading to personal and professional growth.

The Network Effect- The phenomenon enabling trillion dollar social media industry [Math Mondays]

Technology Made Simple • 79 implied HN points • 20 Jun 23
🕹 Technology Mathematics Business Social media Software Engineering
  1. The Network Effect refers to a concept where the value of a product/service increases as more people use it, making the network more valuable for each participant.
  2. The power of the Network Effect can be understood mathematically; as more individuals join a network, the connections exponentially increase, making the system more useful for outsiders.
  3. Businesses/systems built around the Network Effect are powerful due to factors like increased value with more users, a growing network, and the ability to reshape industries and drive innovation.

RDEL #28: What type of interruptions impact developer productivity most?

Research-Driven Engineering Leadership • 39 implied HN points • 05 Feb 24
🕹 Technology Productivity Software Engineering Research Developer Tools
  1. Self-interruptions (voluntary task-switching) are more disruptive for developers than external interruptions.
  2. Contextual factors like interruption type (self vs external) and time of day have a stronger impact on disruption than task-specific factors like priority.
  3. Developers are more vulnerable to task-switching and interruptions when switching between programming and testing tasks compared to other development tasks.

#92

The Nibble • 2 implied HN points • 07 Jan 25
🕹 Technology AI Crypto Web Development Market Trends Software Engineering
  1. Blinkit is launching an ambulance service in India that includes essential medical equipment and trained staff. This can really help improve emergency response for a lot of people.
  2. Nvidia introduced new chips at CES 2025, creating excitement about advancements in consumer tech. Their new offerings could greatly enhance gaming and other applications.
  3. China is tightening regulations on crypto transactions, aiming to track them closely. This shows their ongoing concern about cryptocurrencies despite being a significant holder of Bitcoin.

What's happening at the intersection of ML and Engineering.

Arkid’s Newsletter • 17 HN points • 30 Sep 24
🕹 Technology Machine Learning Software Engineering Data science Artificial Intelligence Infrastructure
  1. AI and machine learning are creating a lot of hype, but it's important to separate the noise from the real value. Just like in the dot-com boom, there will be winners, but it won't be easy to find them.
  2. Many companies are wasting money on consultants who offer little help without delivering real results. To succeed in AI, businesses need to focus on building intelligent products that can learn and iterate based on user feedback.
  3. There's concern about AI taking over jobs in software and machine learning, but skilled professionals will still be needed. It’s crucial for entry-level workers to build solid expertise in their field and adapt to new developments in AI.

How to ARCHITECT a search engine like Google Search[System Design Sundays]

Technology Made Simple • 99 implied HN points • 29 Jan 23
🕹 Technology System Design Machine Learning Search Engine Software Engineering
  1. Design complex systems by layering multiple smaller solutions for better results instead of focusing on individually engineered tasks.
  2. Building a search engine like Google involves accommodating various types of search results like images, text, gifs, and videos while ensuring search quality.
  3. Handling the massive scale of data in Google's search engine system involves using semi-supervised labeling techniques to manage unlabeled data efficiently.