The hottest Software Engineering Substack posts right now

And their main takeaways
Category
Top Technology Topics

[Video] A Brief Introduction to Kafka (with analogy)

Weekend Developer • 1 HN point • 06 Jul 24
🕹 Technology Software Engineering Microservices Fault Tolerance Scalability
  1. Kafka ensures system consistency in the microservices world by allowing events to be recorded and processed consistently even during service downtime.
  2. Kafka enables a decoupled, event-driven approach to microservices communication, providing fault tolerance and scalability as the number of services grows.
  3. The benefits of Kafka in microservices include event-driven architecture, fault tolerance, and scalability, all contributing to a reliable and consistent system.

Premature Optimization and Rapid Prototyping

Load-bearing Tomato • 8 implied HN points • 23 Apr 24
🕹 Technology Game Development Software Engineering Prototyping Coding Practices System Design
  1. Games need to start with quick experiments to see what works, this is called rapid prototyping. Flexibility is important so designers can try new ideas without being held back.
  2. Code doesn’t have to be perfect; it just needs to be good enough for what the game requires. Sometimes a simpler solution works best and saves time.
  3. It's crucial to know when to optimize code. If the game is running well and meeting its needs, there might not be a need to improve it right away.

Systems Distributed '24

Eventually Consistent • 1 HN point • 02 Jul 24
🕹 Technology Software Engineering Software Testing Systems Thinking
  1. Systems engineering is more than programming - it's about understanding complex systems and critical thinking. Engineers with systems thinking skills are becoming increasingly valuable in the industry.
  2. Developing new software abstractions can enhance developer experience and lead to concrete technological innovations. It's important to focus on improving software design patterns and solving problems on the right layers of the stack.
  3. Ensuring safe and correct software remains a significant challenge in building distributed systems. Innovative approaches to testing, such as deterministic hypervisors and model checking techniques, are crucial for uncovering hidden bugs and enhancing productivity.

The Nerdiest Possible Way to Make Sourdough

Tim's Tech Things • 2 HN points • 09 May 24
🕹 Technology AI Computer Vision Software Engineering Python Open Source
  1. Creating a healthy sourdough starter involves feeding it with flour and water until it's ready to use in baking, which contributes to the delicious taste and texture of the bread.
  2. Monitoring the rise of sourdough starter is crucial to ensure there are enough active yeast cells to create CO2 bubbles, which make the bread light and fluffy.
  3. Using computer vision with Python, ffmpeg, and algorithms like rolling averages and derivatives can help automate the process of determining when sourdough is ready for baking.
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Stayin’ Alive

Sector 6 | The Newsletter of AIM • 19 implied HN points • 02 Feb 23
🕹 Technology Web Development Programming Artificial Intelligence Software Engineering Digital innovation
  1. JavaScript became popular in web development because it made websites more dynamic and interactive. This popularity helped it grow and become a dominant player in the programming world.
  2. As web applications got bigger and more complicated, people started looking for alternatives to JavaScript. The way developers were using JavaScript wasn't always the best solution for larger projects.
  3. The ongoing evolution of technology means that even popular tools like JavaScript sometimes face challenges. Developers need to adapt and find new tools to handle complex requirements efficiently.

Does Github CoPilot increase developer productivity?

Brick by Brick • 9 implied HN points • 07 Feb 24
🕹 Technology AI Software Engineering Developer Tools Productivity Data Analysis
  1. Microsoft reported significant growth with GitHub CoPilot, reflecting high adoption and productivity among developers
  2. An experiment showed developers using CoPilot completed tasks 55.8% faster, raising questions about generalizability
  3. Assessing the true impact of CoPilot on productivity requires rigorous experiments tailored to individual engineering organizations

[Solution]Problem 51:Happy Number[IBM]

Technology Made Simple • 19 implied HN points • 11 Aug 22
🕹 Technology Coding Problem Solving Math Algorithm Software Engineering
  1. A happy number is a number defined by a specific process that ends with the number 1, while an unhappy number will loop endlessly without reaching 1.
  2. When facing a problem, break down the definitions given in the problem as this can provide insights and help formulate mathematical rules for quick problem-solving.
  3. In problem-solving, looking for patterns, mathematical or algorithmic statements can give a competitive advantage and aid in solving or optimizing problems efficiently.

An Intro to Finite State Machines[Math Monday]

Technology Made Simple • 19 implied HN points • 08 Aug 22
🕹 Technology Programming Software Engineering Computer Science Tutoring Algorithms
  1. Finite State Machines (FSMs) are like directed graphs that help in understanding the flow of a program. Nodes represent states and edges show reachable states.
  2. FSMs are useful for filtering input based on rules and when a system is defined by a set of conditions, like in Regex applications.
  3. Mastering FSMs involves patience, practice, and hands-on coding of theoretical concepts to understand and implement them effectively.

Is your bucket leaky?

Freelance Footprints • 8 HN points • 20 Feb 24
🕹 Technology Algorithms Web Development Data Structures Networking Software Engineering
  1. The leaky bucket algorithm helps manage the rate of requests a web application can handle. It uses the idea of a bucket that can fill up and overflow if too many requests come in at once.
  2. In this algorithm, there are two key settings: the maximum number of requests allowed at a time and the rate at which requests are processed. This controls how quickly requests are dealt with and prevents overload.
  3. The leaky bucket algorithm is widely used in tech, such as by companies like SeatGeek for their waiting room systems, to ensure smooth user experiences without exceeding server limits.

Uncertainty in machine learning deployments

Denis’s Substack • 7 HN points • 07 Jun 23
🕹 Technology Machine Learning Data science Software Engineering API Automation
  1. Many machine learning projects never make it to production due to various reasons like lack of stakeholder buy-in and data quality issues.
  2. The traditional linear process of analyzing, extracting data, modeling, deploying, and operating models can be naive and not reduce uncertainty.
  3. Embracing uncertainty in machine learning deployments can involve starting the deployment phase before data extraction, leading to constant value addition throughout the process.

Data Science Weekly - Issue 469

Data Science Weekly Newsletter • 19 implied HN points • 17 Nov 22
🕹 Technology AI Machine Learning Data science Robotics Software Engineering
  1. Learning machine learning can be accomplished without an engineering background. It often requires hard work, perseverance, and adopting good software engineering practices.
  2. Robotics and AI are being increasingly used in fulfillment processes at companies like Amazon. These technologies face challenges but also provide innovative solutions for package handling.
  3. Large language models are evolving to act like agents that make decisions. This shift towards action-driven models may make them resemble artificial general intelligence (AGI) more closely.

Making Peace with LLM Non-determinism

The Finest Tuners • 5 HN points • 07 Apr 24
🕹 Technology Machine Learning Artificial Intelligence Natural Language Software Engineering
  1. Non-determinism in language models can be frustrating because you can't always expect the same output each time you input the same prompt. This unpredictability often stems from the way language itself works.
  2. You can reduce some of this unpredictability by using techniques like seeding and selecting better models. These methods help control how outputs are generated and make them more consistent.
  3. Understanding that language is inherently complex can help you see the random outputs as part of the model's nature, not just flaws. Embracing this chaos can lead to surprising and interesting results.

Data Science Weekly - Issue 460

Data Science Weekly Newsletter • 19 implied HN points • 15 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Data Visualization
  1. Soft skills are super important for data scientists. Being able to communicate well and work in a team can make a big difference in their effectiveness.
  2. There are great resources available online for learning data science, including live streams on platforms like Twitch. It’s a fun way to learn and engage with others.
  3. Use the right fonts and designs in data visualizations. They can greatly affect how your data is understood and appreciated.

Problem 30: Find Distance to nearest Exit[Google]

Technology Made Simple • 19 implied HN points • 24 Mar 22
🕹 Technology Coding Problem Solving Google Software Engineering
  1. The problem involves finding the distance to the nearest exit on a 2D grid with obstacles and gates.
  2. The solution requires filling each empty room with the distance to its nearest gate, considering obstacles and walls.
  3. This question is a favorite problem asked by Google to test problem-solving skills and the ability to recognize the right approach.

Don't build your application on a single LLM call

Generating Conversation • 5 HN points • 14 Mar 24
🕹 Technology Artificial Intelligence Application development Software Engineering User Experience
  1. Avoid building your application solely on a single Large Language Model (LLM) call. Break down your problem into multiple steps for better results and efficiency.
  2. Long, detailed prompts can confuse even advanced LLMs like GPT-4, leading to issues in instruction following, debugging, and user experience.
  3. Different tasks may require different models, so breaking your application into multiple steps allows you to choose the best tool for each task, improving application quality and reducing latency and cost.

Data Science Weekly - Issue 459

Data Science Weekly Newsletter • 19 implied HN points • 08 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Analytics
  1. Organizations need to invest in creating better data to gain an advantage over competitors. Good data can drive value and improve decision-making.
  2. The activation layer of the modern data stack helps you use data in a more impactful way. This allows for personalized experiences rather than just viewing dashboards.
  3. Using standard formats like ONNX for model exporting makes your machine learning models more portable across different programming environments, reducing dependencies on specific languages.

Good Comment, Bad Comment

David Reis on Software • 5 implied HN points • 09 Mar 24
🕹 Technology Software Engineering Code Quality Best Practices Programming Languages Developer Tools Technical Writing
  1. Many new programmers think that not commenting code is a sign of good practice because of the idea that 'clean code has no comments.' This leads to less readable code.
  2. Good code should be easily understood, but comments can help clarify complex parts when necessary. It's okay to use comments to explain why something is done a certain way.
  3. Writers should be careful with popular ideas that seem easy and convenient, as they can sometimes oversimplify important concepts and lead people to misunderstand or misuse them.

Short post: A look at Devin, the AI-powered software engineer

Fikisipi • 4 HN points • 12 Mar 24
🕹 Technology AI Software Engineering Machine Learning Innovation Benchmarks
  1. Devin is an AI-powered software engineer with features like a built-in terminal, IDE, website preview, and a text assistant.
  2. Devin demonstrated capabilities like finding and fixing bugs in GitHub repos and running tests on code, showing potential for automating debugging tasks.
  3. Cognition Labs, the company behind Devin, has notable supporters like Thiel's Founders Fund and founders with strong backgrounds in software engineering and machine learning.

They should have fired him

A Perfectly Cromulent Software Engineer • 1 HN point • 21 Apr 24
🕹 Technology Software Engineering Career development Work Environment Management Productivity
  1. Transitioning to a traditional job from freelance work can be a significant change in routine and responsibilities.
  2. Challenges and growth opportunities can arise when tasked with larger, more ambiguous projects that test technical abilities.
  3. Recognizing toxic behavior in oneself or others, such as being uncooperative and rude, is essential in maintaining a positive work environment.

To C or not to C

Blog System/5 • 4 HN points • 21 Feb 24
🕹 Technology Programming Languages Memory management Software Engineering
  1. Knowing C well involves dealing with pointers, memory management, system calls vs. library functions, and understanding the FFI
  2. Knowledge of memory, system calls vs. library functions, and FFI gained from knowing C can be applied to many programming languages
  3. While you don't need to know C to be a good programmer, learning it can help you with understanding fundamental programming concepts

Data Science Weekly - Issue 433

Data Science Weekly Newsletter • 19 implied HN points • 10 Mar 22
🕹 Technology Artificial Intelligence Data science Machine Learning Automation Software Engineering
  1. Deep learning is facing challenges, and experts are exploring what it needs to improve. It's important for AI to overcome these hurdles to progress further.
  2. MLOps, or machine learning operations, is currently complicated, but it's a growing field that promises future innovations. New tools and methods are emerging rapidly, making it tricky for newcomers to find their way.
  3. Visualizing data effectively is essential for making sense of complex information. Standards are being developed to help create better visuals, which makes it easier for everyone to understand data.

Data Science Weekly - Issue 432

Data Science Weekly Newsletter • 19 implied HN points • 03 Mar 22
🕹 Technology AI Machine Learning Data science Software Engineering Natural Language Processing
  1. AI art has evolved quickly, becoming more relatable and controllable thanks to advancements in technology. Many people, even experts, are surprised by how realistic and detailed AI-generated images can now be.
  2. Conversational agents, like chatbots, are becoming more common and can serve different purposes, from casual chats to helping users complete specific tasks. However, understanding their impact on society is important as they become more integrated into daily life.
  3. The CX-ToM framework improves explainable AI by creating a dialogue between machines and humans for better understanding. This approach focuses on the intentions of both the user and the machine, making AI decisions clearer.

Problem 2(Microsoft): Find Substring Occurences

Technology Made Simple • 19 implied HN points • 21 Aug 21
🕹 Technology Coding Software Engineering
  1. The post discusses a coding problem from Microsoft that involves finding starting indices of a pattern within a given string.
  2. Readers are encouraged to sign up for the newsletter to access solutions to such coding problems and improve their coding interview skills with practical examples.
  3. The post provides a link to share interesting problems or solutions with the author for a chance of a shoutout and additional subscription time.

Technical Cantilevers

Rabbit Thoughts • 2 HN points • 10 Jan 24
🕹 Technology Software Engineering Coding Practices
  1. The term "technical cantilever" is proposed as a better alternative to "technical debt" in the context of software engineering.
  2. Software development can be categorized into three main aspects: foundation, internal libraries, and applications, each with varying levels of formal engineering and testing.
  3. A technical cantilever can function and extend smoothly until a point where it requires immense effort to further extend, unlike technical debt which accumulates gradually and can be paid down slowly.

Is Devin the future of Coding?

Machine Economy Press • 3 implied HN points • 15 Mar 24
🕹 Technology Artificial Intelligence Coding Startups Machine Learning Software Engineering
  1. Devin, a tool by Cognition AI, is being hailed as a breakthrough in computer reasoning, utilizing generative AI like GPT-4.
  2. Despite claims that Devin can make thousands of decisions, recall context, learn, and correct code mistakes, skepticism exists among software engineers.
  3. The tech sector is witnessing an increase in AI startups and coding assistants/agents like Devin, showcasing the growing interest in machine learning, particularly among Asian developers.

Data Science Weekly - Issue 412

Data Science Weekly Newsletter • 19 implied HN points • 14 Oct 21
🕹 Technology AI Machine Learning Data science Statistics Software Engineering
  1. Machine learning is much more than just nonparametric statistics. It involves complex principles that go beyond what you learn in basic statistics.
  2. The State of AI Report 2021 highlights important areas like research, talent supply, industry applications, politics, and future predictions for AI. It's a comprehensive look at how AI is evolving.
  3. Self-supervised learning is becoming a major player in AI research. It allows models to learn from data without needing labeled examples, which can lead to significant advancements.