The hottest Software Engineering Substack posts right now

And their main takeaways
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Top Technology Topics

Do You Have to Learn Math for Machine Learning?

The Palindrome • 2 implied HN points • 12 Jul 25
🕹 Technology Software Engineering
  1. You don't have to learn math for machine learning, but it's a good idea. Understanding the basics can help you troubleshoot better when things go wrong.
  2. Many advanced math concepts are hidden behind software libraries. This makes using machine learning easier, but you might miss out on understanding how things really work.
  3. Using machine learning without a solid math foundation is like exploring a new country without knowing the language. You might get by, but understanding will help you navigate better.

Good Comment, Bad Comment

David Reis on Software • 13 implied HN points • 09 Mar 24
🕹 Technology Software Engineering
  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.

Data Science Weekly - Issue 433

Data Science Weekly Newsletter • 19 implied HN points • 10 Mar 22
🕹 Technology 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.
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Data Science Weekly - Issue 432

Data Science Weekly Newsletter • 19 implied HN points • 03 Mar 22
🕹 Technology Software Engineering
  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 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
  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.

Data Science Weekly - Issue 412

Data Science Weekly Newsletter • 19 implied HN points • 14 Oct 21
🕹 Technology 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.

Cross-Dysfunctional Product Teams

TeamCraft • 13 implied HN points • 23 Oct 23
🕹 Technology Software Engineering
  1. Spotify's cross-functional product squads champion autonomy and decoupled releases.
  2. Local cross-pollination in product teams enables holistic problem-solving.
  3. Startups often adopt parts of the Spotify framework but struggle due to lack of trust and selective implementation.

Premature Optimization and Rapid Prototyping

Load-bearing Tomato • 8 implied HN points • 23 Apr 24
🕹 Technology Software Engineering
  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.

Does Github CoPilot increase developer productivity?

Brick by Brick • 9 implied HN points • 07 Feb 24
🕹 Technology Software Engineering
  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

The Data-Conscious Software Engineer

Data Products • 3 implied HN points • 28 Jan 25
🕹 Technology Software Engineering
  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.

Three domains in Data Science

Laszlo’s Newsletter • 16 implied HN points • 19 Apr 23
🕹 Technology Software Engineering
  1. Domains in data science help break up complex systems for easier comprehension and focus.
  2. Boundaries between domains help prevent misunderstandings and allow for clear communication.
  3. Having clear separation of three domains in data science aids in assigning concerns correctly and focusing effectively.

Data Science Weekly - Issue 391

Data Science Weekly Newsletter • 19 implied HN points • 20 May 21
🕹 Technology Software Engineering
  1. Major League Baseball is testing an automated ball and strike calling system to help umpires make faster and more accurate calls during games.
  2. Twitter has updated its image cropping algorithm to be fairer and more equitable in how it represents different images to users.
  3. Reinforcement learning is gaining interest among big companies, but it's still a developing area compared to other machine learning techniques.

Data Science Weekly - Issue 390

Data Science Weekly Newsletter • 19 implied HN points • 13 May 21
🕹 Technology Software Engineering
  1. A crossword-solving AI named Dr. Fill has shown that machines can solve puzzles like humans, but humans still have their unique strengths.
  2. The concept of 'trees' in biology is more complex, as many plants we call trees don't fit a simple definition, mixing in non-trees in their evolutionary history.
  3. Advancements in synthetic data generation allow for the creation of realistic images, making it useful for training models even when real data is scarce.

Is your bucket leaky?

Freelance Footprints • 8 HN points • 20 Feb 24
🕹 Technology 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.

How Sound Works In Games

Load-bearing Tomato • 5 implied HN points • 06 Aug 24
🕹 Technology Software Engineering
  1. Sound in games is made to work in real-time, which means it can't be exactly like in real life. Instead, game developers use simplified calculations to make it feel realistic without needing huge amounts of computer power.
  2. To create sound effects in games, developers use Emitters to play sounds and Listeners to receive them. This setup helps in determining how loud a sound is based on the distance and direction from where it is coming.
  3. Using Rooms and Portals helps organize sounds in the game environment. This makes it easier for the game to decide what sounds the player can hear and adjust them accordingly, leading to a more immersive experience.

How to Create Email Sequences on Substack

Implementing • 1 HN point • 12 Feb 24
🕹 Technology 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.

Measuring Developer Productivity

Fish Food for Thought • 10 implied HN points • 13 Sep 23
🕹 Technology Software Engineering
  1. Measuring developer productivity involves considering effort, output, outcome, and impact.
  2. Impact is crucial to measure, but it should be team-based rather than individual-based to avoid unhealthy competition.
  3. Software development should focus on ensuring people are well, working within constraints, and achieving impactful outcomes.

Data Science Weekly - Issue 369

Data Science Weekly Newsletter • 19 implied HN points • 17 Dec 20
🕹 Technology Software Engineering
  1. Companies are changing how they share information because of AI. They're making their reports easier for machines to read, which can influence market behavior.
  2. Monitoring machine learning models is essential for maintaining accuracy. It's important to detect issues like outliers and changes in data patterns in real-time.
  3. Deep learning research often helps engineers tackle real-world problems effectively. Insights from recent research can guide better practices in building and deploying models.

Data Science Weekly - Issue 366

Data Science Weekly Newsletter • 19 implied HN points • 26 Nov 20
🕹 Technology Software Engineering
  1. Pinterest improved its machine learning signals by updating its data infrastructure. They moved from a Lambda architecture to a Kappa architecture for better real-time performance.
  2. DoorDash built a feature store to handle the massive amounts of data needed for its machine learning models. This helps them manage costs and maintain fast performance when retrieving data.
  3. When choosing between a data lake, warehouse, or lakehouse, it's important to consider the specific needs of your data platform. The right choice depends on the tools that best fit your project requirements.

Data Science Weekly - Issue 358

Data Science Weekly Newsletter • 19 implied HN points • 01 Oct 20
🕹 Technology Software Engineering
  1. Data quality is very important for machine learning (ML) operations. It helps ensure that ML systems produce reliable results and builds trust with stakeholders.
  2. The State of AI Report highlights recent developments in AI, focusing on research breakthroughs, talent supply, industry applications, and future predictions.
  3. Diversity in AI and supporting applied statistics students are crucial for improving representation and effectiveness in data science and machine learning fields.

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

HackerPulse Dispatch • 2 implied HN points • 24 Jan 25
🕹 Technology 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.

Data Science Weekly - Issue 353

Data Science Weekly Newsletter • 19 implied HN points • 27 Aug 20
🕹 Technology Software Engineering
  1. Effective testing is crucial for machine learning systems. It's important to understand that these systems require different testing strategies compared to traditional software.
  2. There are hidden challenges in becoming a machine learning engineer. Many of these insights come from the experiences of those already in the field, beyond what you learn in books.
  3. New resources and courses are constantly being developed in data science. For example, fast.ai just released a new deep learning course and libraries, which can help beginners get started.

Making Peace with LLM Non-determinism

The Finest Tuners • 5 HN points • 07 Apr 24
🕹 Technology 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.

#92

The Nibble • 2 implied HN points • 07 Jan 25
🕹 Technology 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.

Data Science Weekly - Issue 344

Data Science Weekly Newsletter • 19 implied HN points • 25 Jun 20
🕹 Technology Software Engineering
  1. As AI systems become more common, it’s important to think about who is responsible when things go wrong. Recent incidents raise questions about how to share accountability between people, companies, and governments.
  2. Scientists are learning more about years of small earthquakes in California, and they found that fluids moving through the ground might have caused them. This shows how understanding these events can help with studying earthquakes around the world.
  3. There are many tools for machine learning, but the landscape is still developing. A study looked at over 200 tools to find out what works best and what challenges people face when using them.

Don't build your application on a single LLM call

Generating Conversation • 5 HN points • 14 Mar 24
🕹 Technology Software Engineering
  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 342

Data Science Weekly Newsletter • 19 implied HN points • 11 Jun 20
🕹 Technology Software Engineering
  1. Recent studies show that there hasn't been a significant change in the types of jobs that get automated, despite the rise of new technology. It seems that many jobs remain unaffected by automation trends.
  2. Tools like OpenAI's API allow easy integration of advanced language tasks without needing extensive data. This makes it simpler for developers to use powerful language models.
  3. Feature engineering and managing technical debt are crucial in machine learning development. Good practices can help to avoid messy code and ensure smoother transitions from development to production.

Data Science Weekly - Issue 331

Data Science Weekly Newsletter • 19 implied HN points • 26 Mar 20
🕹 Technology Software Engineering
  1. The AI field has a serious gender imbalance that can lead to inequalities in AI systems. It's important to address this issue to avoid harming underrepresented groups.
  2. Remote work can be tough for data science teams due to challenges in communication and feelings of isolation. It's crucial to create effective systems to keep the team engaged and productive.
  3. New data-sharing approaches, like HealthMap for coronavirus monitoring, can greatly enhance our ability to respond to public health crises. This represents a shift in how we collect and share important data.