The hottest Software Engineering Substack posts right now

And their main takeaways
Category
Top Technology Topics

Building a Simple Live Streaming Platform[System Design Sundays]

Technology Made Simple • 119 implied HN points • 18 Mar 24
🕹 Technology System Design Live Streaming Platforms Coding Software Engineering
  1. When designing a live streaming platform like Twitch, key steps include ingestion, transcoding, packaging, CDN utilization, and database management.
  2. Challenges like low latency, scalability, and reliability must be addressed for the success of a live streaming platform.
  3. To enhance a streaming service further, consider advanced technologies like adaptive bitrate algorithms, advanced caching, and community features.

2024: the year in review

The Engineering Manager • 13 implied HN points • 29 Dec 24
🕹 Technology AI Tools Software Engineering Productivity Leadership Work Culture
  1. Efficiency is really important now. Companies need to do more with less and find ways to be productive without hiring more people.
  2. AI tools are becoming essential. Embracing technology like LLMs can boost productivity and help engineers work smarter.
  3. There’s a generational divide. Staying updated with technology is crucial, or you risk being left behind, both personally and for your company.
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Grow Your Software Engineer Career

Tech Ramblings • 19 implied HN points • 21 Jul 24
🕹 Technology Software Engineering Career development Tech industry Education Communication Skills
  1. Many young software engineers make common mistakes that can hold back their careers. It’s important to recognize these traps early on.
  2. Good communication skills are essential for solving problems and sharing ideas effectively. Learning to articulate your thoughts can make a big difference.
  3. Experience in different domains, like academia and tech companies, can provide valuable insights. Be open to learning from various industries to grow your career.

Navigating the Software Engineering career ladders

system bashing • 275 implied HN points • 20 Jun 23
🕹 Technology Software Engineering Career development
  1. Software engineering career paths differ based on company size and age, so titles like "Senior Software Engineer" can vary widely.
  2. In early-stage startups, titles like "Senior" may simply imply a higher level of autonomy, not necessarily a specific rank.
  3. As companies grow, the tech team pyramid evolves, introducing new levels and roles like SDE1, SDE2, SDE3, VPs, and EMs.

Notes on Service Level Objectives

A Small, Good Thing • 19 implied HN points • 24 Mar 25
🕹 Technology Software Engineering Product Management Organizational Culture
  1. Service Level Objectives (SLOs) are important for understanding if services are reliable, but many organizations find them hard to use effectively. It's like a tool that sounds great but often doesn't work as well in practice.
  2. Adopting and managing SLOs usually requires a lot of effort and support from the whole team, not just the SREs. If the company culture isn't ready for it, SLOs often get ignored.
  3. There's a big gap between the theory of SLOs and how they're applied in real companies. Many teams struggle with choosing the right metrics and getting everyone to care about reliability over new features.

VTEX scales to 150 million metrics using Amazon Managed Service for Prometheus

VTEX’s Tech Blog • 99 implied HN points • 10 Mar 24
🕹 Technology Cloud Computing Software Engineering Data Analytics DevOps
  1. VTEX successfully scaled its monitoring system to handle 150 million metrics using Amazon's Managed Service for Prometheus. This helped them keep track of their numerous services efficiently.
  2. By adopting this system, VTEX cut its observability expenses by about 41%. This shows that smart choices in technology can save money.
  3. The new architecture allows VTEX to respond to problems faster and reduces the chances of system failures. It increased the reliability of their metrics, making everyday operations smoother.

New Layer of Abstraction, Not the End of Software Engineering

Adam’s Notes • 255 implied HN points • 17 Feb 23
🕹 Technology Software Engineering AI Productivity Programming Languages Career development
  1. AI tools will enhance software developers' productivity and create new possibilities.
  2. Historically, productivity increases in software engineering have occurred with advancements like high-level programming languages, open-source culture, and cloud computing.
  3. Lower barriers to coding will attract more people to software engineering, leading to new opportunities, growth, and products.

Release Management

Software Engineering Tidbits • 255 implied HN points • 26 Apr 23
🕹 Technology Software Engineering Deployment CI/CD
  1. Ensure all necessary steps are taken before landing a pull request to the main branch, such as passing all tests and code reviews.
  2. Deploy new software versions gradually to production, starting with a small number of machines first.
  3. Consider implementing CI/CD for continuous deployment to improve observability, but balance it with on-demand deployments to ensure all changes are attended to.

Going Faster is the Greatest UX

Sung’s Substack • 79 implied HN points • 26 Mar 24
🕹 Technology Data Engineering Software Engineering Open Source
  1. Civilization advances by extending the number of important operations which we can perform without thinking about them.
  2. In data engineering, the focus on speed is increasing, with the need for tools to actually make users go faster, not just show possibilities.
  3. To improve workflow efficiency, demand every element to be faster without compromises.

Edge 439: SSMs with Attention, Understanding Zamba

TheSequence • 112 implied HN points • 15 Oct 24
🕹 Technology AI Machine Learning Data science Software Engineering Computer Science
  1. Combining state space models (SSMs) with attention layers can create better hybrid architectures. This fusion allows for improved learning capabilities and efficiency.
  2. Zamba is an innovative model that enhances learning by using a mix of Mamba blocks and a shared attention layer. This approach helps it manage long-range dependencies more effectively.
  3. The new architecture reduces the computational load during training and inference compared to traditional transformers, making it more efficient for AI tasks.

Software Engineering Myths Wasting Your Time

Brain Bytes • 139 implied HN points • 10 Jan 24
🕹 Technology Software Engineering
  1. Software engineering myths include the idea that you have to learn everything in the field, but it's more practical to focus on specific areas and have a general understanding of others.
  2. The belief that adding more programmers speeds up development isn't always true; it can lead to more delays due to increased need for communication and management.
  3. Software development involves more than just writing code; it includes tasks like planning, testing, deploying, and maintaining software.

Data Science Weekly - Issue 535

Data Science Weekly Newsletter • 99 implied HN points • 23 Feb 24
🕹 Technology Data science Artificial Intelligence Machine Learning Software Engineering Data Engineering Statistical Analysis
  1. Scaling AI tools like ChatGPT involves overcoming many engineering challenges to handle large user demands. It's important to manage growth effectively to keep users satisfied.
  2. There's a lot of information out there about generative AI, making it hard to keep up. A guidebook can help condense this information and provide practical insights.
  3. Linear regression is still a valuable tool in data science. Sometimes going back to basics can yield better results than relying on complex models.

How We Built a Self-Healing System to Survive a Terrifying Concurrency Bug At Netflix

Push to Prod • 5 HN points • 27 Aug 24
🕹 Technology Software Engineering Systems Design Incident Management
  1. At Netflix, there was a serious concurrency bug causing CPU problems, and they needed a quick solution. They couldn't fix it right away and had to come up with a way to keep their systems running through the weekend.
  2. Instead of manually fixing everything, they created a self-healing system. They randomly killed a few server instances every 15 minutes, replacing them with fresh ones, which allowed the team to relax during the crisis.
  3. This situation taught them that sometimes unconventional solutions are necessary. Prioritizing the team's well-being can be just as important as fixing technical issues.

The Data-Conscious Software Engineer

Data Products • 3 implied HN points • 28 Jan 25
🕹 Technology Data science Software Engineering Data Management Data products Machine Learning
  1. Data teams need to learn best practices from software engineering, but that's not enough. They also need engineers who understand how data works and can work well with them.
  2. Collaboration between data teams and software engineers is really important for success. If they don't communicate well, they can struggle to implement necessary changes and solve issues together.
  3. The idea of a 'data-conscious software engineer' is becoming essential. These engineers understand the value of data and can help improve how both teams work together, making both sides more efficient.

The Messy Page

Register Spill • 216 implied HN points • 07 May 23
🕹 Technology Software Engineering Programming Debugging AI
  1. The author prefers messy projects over greenfield projects because they provide more certainty and direction.
  2. Having clear product-market fit and defined requirements make a project enjoyable to work on.
  3. The author finds debugging appealing due to its clear requirements and the assurance that efforts won't be wasted.

How to scale yourself

Software Engineering Tidbits • 216 implied HN points • 11 Apr 23
🕹 Technology Software Engineering
  1. One way to scale yourself in a professional setting is to schedule specific office hours for addressing requests.
  2. Another method to scale yourself is to create a comprehensive internal search system to easily access knowledge resources.
  3. Delegating tasks to team members and managers is essential for freeing up time, reducing bottlenecks, and fostering growth opportunities.

Beauty in Simplicity

Mindful Matrix • 119 implied HN points • 21 Jan 24
🕹 Technology Software Engineering Principles Design Simplicity Innovation
  1. Simplicity in software engineering is crucial for elegant solutions. Simple code is easier to maintain, read, and collaborate on.
  2. Prioritizing simplicity leads to streamlined debugging, improved scalability, and lower technical debt. It makes adapting and deploying software faster and more user-centric.
  3. Applying simplicity principles involves starting simple, avoiding premature optimization, focusing on core features, implementing incrementally, and leveraging existing tools. Embracing simplicity in coding doesn't mean avoiding complexity entirely, but finding beauty and efficiency in straightforward solutions.

These 7 Software Engineering Skills Give You an Unfair Advantage

Brain Bytes • 119 implied HN points • 17 Jan 24
🕹 Technology Software Engineering Cybersecurity Automation Version Control Cloud Computing
  1. Thinking like a hacker helps in identifying and fixing security flaws before they are exploited, crucial in today's cybersecurity landscape.
  2. Understanding different devices through cross-platform critical thinking gives a competitive edge and promotes reusability of business logic.
  3. Scripting and automation for repetitive tasks enhances productivity by ensuring consistency, accuracy, and freeing up time for more complex work.

Data Science Weekly - Issue 492

Data Science Weekly Newsletter • 379 implied HN points • 28 Apr 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Software Engineering
  1. There is a new Slack community for paid subscribers focused on learning new tools and techniques in data science and career growth. It's a good place for support and sharing information.
  2. A/B testing is important for experiments and there are recommended resources to help design and run successful tests. Proper planning and communication are key to making A/B testing effective.
  3. Large Language Models (LLMs) are becoming more useful, and several resources are available for learning how to work with them. Understanding how they operate can help create valuable applications.

Some ways Software Engineers can 10x results with Bayesian Thinking [Math Mondays]

Technology Made Simple • 199 implied HN points • 13 Jun 23
🕹 Technology Software Engineering Machine Learning Inference Debugging Decision-making
  1. Bayesian Thinking can improve software engineering productivity by updating beliefs with new knowledge.
  2. Bayesian methods help in tasks like prioritizing, A/B testing, bug fixing, risk assessment, and machine learning.
  3. Using Bayesian Thinking in software engineering can lead to more efficient and effective decision-making.

So... what is Stateless Architecture in Software Engineering[System Design Sundays]

Technology Made Simple • 199 implied HN points • 04 Jun 23
🕹 Technology Software Engineering Cloud Computing Architecture
  1. To understand stateless architecture, it's important to know the background of traditional client-server patterns and why moving towards stateless is beneficial.
  2. The concept of state in an application is crucial, and stateless architecture outsources state handling to more efficient systems like using cookies and shared instances for storing state.
  3. Stateless architecture simplifies state management, enhances client-side performance, and makes server scaling easier, aligning well with modern computing capabilities.

How Vector Databases enable Generative AI [Math Mondays]

Technology Made Simple • 199 implied HN points • 06 Jun 23
🕹 Technology Programming Databases AI Software Engineering Math
  1. Vector databases store data as high-dimensional vectors to enable advanced AI like Gen AI.
  2. Vectors are crucial for AI applications like language processing, computer vision, and recommendation systems.
  3. Vector databases offer flexibility in handling complex datasets, allowing AI models to interact more effectively.

RDEL #24: How does technical debt impact software engineering morale?

Research-Driven Engineering Leadership • 119 implied HN points • 08 Jan 24
🕹 Technology Software Engineering Technical Debt Productivity Engineering Leadership Research
  1. Technical debt negatively impacts developers' morale by reducing their confidence and hindering their progress
  2. Proper management of technical debt can have a positive influence on developers' morale as it is associated with progress and gratitude
  3. Dealing with technical debt thoughtfully and having a plan to repay it frequently can help minimize its negative impacts on engineering teams

Challenges In Adopting Retrieval-Augmented Generation Solutions

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 01 Apr 24
🕹 Technology AI Development Language Models Data Management User Experience Privacy Concerns Software Engineering
  1. Retrieval-Augmented Generation (RAG) uses contextual learning to improve responses and reduce errors, making it useful for Generative AI.
  2. RAG systems are easier to maintain and less technical, which helps keep them updated with changing needs.
  3. However, RAG can have shortcomings like poor retrieval strategies and issues with data privacy, leading to incomplete or incorrect answers.

Looking at the Datastructures- Trees [Math Mondays]

Technology Made Simple • 179 implied HN points • 18 Jul 23
🕹 Technology Data Structures Software Engineering Machine Learning AI Recursion
  1. Trees are powerful data structures that are great for efficient organization and retrieval of data in software engineering.
  2. Recursion works well with trees due to their recursive substructure, making implementation of recursive functions easier.
  3. Decision trees in AI excel at discerning complex patterns, providing interpretable results, and are versatile in various domains such as finance, healthcare, and marketing.

Learnings from yet another 'blitzscaling' stage hiring excercise

system bashing • 176 implied HN points • 01 Jul 23
🕹 Technology Software Engineering Recruitment Leadership Productivity Coding
  1. During a hiring process, it's important to assess candidates based on coachable vs non-coachable gaps to align with the team's needs.
  2. For junior engineers, watch out for extreme design decisions like overly complex or overly simplistic solutions, as they may indicate a lack of awareness.
  3. When interviewing, consider candidates' coding nature, such as the balance between writing clean code and practical functionality testing, as it reflects their approach to software development.

Let's get rid of security reviews

Frankly Speaking • 254 implied HN points • 16 Nov 23
🕹 Technology Security Development Software Engineering
  1. The current security review process is outdated and not aligned with modern development practices.
  2. Implementing efficient and effective security measures may involve integrating software engineers with security teams.
  3. Scaling security efforts requires a rethink of traditional security review processes towards more collaborative and contextual approaches.

The stream processing model behind Google Cloud Dataflow

VuTrinh. • 39 implied HN points • 27 Apr 24
🕹 Technology Data processing Cloud Computing Big Data Software Engineering Stream Processing
  1. Google Cloud Dataflow is a service that helps process both streaming and batch data. It aims to ensure correct results quickly and cost-effectively, useful for businesses needing real-time insights.
  2. The Dataflow model separates the logical data processing from the engine that runs it. This allows users to choose how they want to process their data while still using the same fundamental tools.
  3. Windowing and triggers are important features in Dataflow. They help organize and manage how data is processed over time, allowing for better handling of events that come in at different times.

An intro to the Normal Distribution [Math Mondays]

Technology Made Simple • 159 implied HN points • 23 May 23
🚌 Education Math Software Engineering Statistics Finance Technology
  1. The Normal Distribution is a probability distribution used to model real-world data, with a bell-shaped curve and key points located at the center.
  2. The Normal Distribution is essential as it is commonly used in various fields to model real-world phenomena, calculate probabilities, and make informed decisions in software development.
  3. Understanding and using the Normal Distribution in software can help in making approximations for performance, making the right sacrifices, and optimizing solutions based on real-world data.

Software Liability for Armchair Quaterbacks

Cybersect • 78 implied HN points • 06 Feb 24
🕹 Technology Cybersecurity Software Engineering Regulation
  1. Armchair experts in both football and software development have strong opinions without real expertise.
  2. Software bugs are complex and not solely due to moral weakness, but rather the inherent difficulty of preventing them.
  3. Proposed software regulations may not improve cybersecurity but instead burden smaller companies and benefit larger corporations.

Productivity in Software Engineering: Beyond lines of code, the importance of experience in the development cycle

VTEX’s Tech Blog • 1 HN point • 18 Sep 24
🕹 Technology Software Engineering Productivity Developer Experience Quality Assurance Performance Metrics
  1. Productivity in software engineering is not just about how much code you write. It's more important to focus on code quality and how well the software works.
  2. At VTEX, they listen to developers to improve their work experience. This helps boost productivity by addressing the challenges developers face.
  3. Combining feedback from developers with quantitative data can help understand the impact of changes in tools and processes on productivity.

Smarter Agents, Self-Aware LLMs, and Knowledge from Videos

HackerPulse Dispatch • 2 implied HN points • 24 Jan 25
🕹 Technology AI Machine Learning Data science Video Processing Software Engineering
  1. New techniques can shrink the size of data storage without losing accuracy, which helps in finding information faster.
  2. Language models are getting better at learning from their own mistakes, making them smarter and more self-aware.
  3. AI can now learn complex skills just by watching videos, which shows that reading text isn't always necessary for advanced learning.