The hottest Software Engineering Substack posts right now

And their main takeaways
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Top Technology Topics

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

Technology Made Simple • 119 implied HN points • 26 Apr 23
🕹 Technology Software Engineering
  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.

The one where we vibe code holiday cards | Season 5 Finale

Dev Interrupted • 9 implied HN points • 23 Dec 25
🕹 Technology Software Engineering
  1. MCP agents need strong safeguards: treat actions on a spectrum of reversibility and consequence, and require a human in the loop for irreversible or high‑risk operations.
  2. Engineers are still responsible for delivering proven code, not just generating it — every line of AI‑produced code must be verified and tested before shipping.
  3. Rigid engineering dogmas like mandatory review for every PR and slavish sprint rituals slow teams down. Teams should let senior engineers self‑merge low‑risk changes and audit whether safeguards prevent bugs or just block work.

I'm Moving to the Edge of Bangalore

Kartick’s Blog • 52 implied HN points • 02 Aug 25
🕹 Technology Software Engineering
  1. Living in a busy city can lead to stress and health issues. Long commutes and traffic can make daily life feel heavy and overwhelming.
  2. Smaller cities often offer a better quality of life with cleaner environments and less chaos. Sometimes, the hustle and bustle of a megacity isn't worth the trade-offs.
  3. Finding a balance between work and personal life is important. A better living situation can help improve overall well-being and performance at work.

From Kubernetes to AI maximalism | Stacklok's Craig McLuckie

Dev Interrupted • 14 implied HN points • 25 Nov 25
🕹 Technology Software Engineering
  1. Treat AI like engineering — insist on reproducibility, audit trails, and measurable quality so models aren’t just probabilistic parrots.
  2. Use AI to amplify good habits, not hide gaps — have models critique your solutions Socratically and keep humans in charge of architecture to avoid accelerating technical debt.
  3. Replace the "glue person" with composable AI workflows and agent-assisted cleanup, and measure adoption and impact so you can reclaim focus and reduce coordination toil.

Global Incremental ID

Hung's Notes • 79 implied HN points • 13 Dec 23
🕹 Technology Software Engineering
  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.
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Cursor vs Anthropic multi-agent coding experiments

Peter’s Substack • 2 implied HN points • 06 Feb 26
🕹 Technology Software Engineering
  1. Use a hierarchical decomposition where high-level planners break goals into subplanners and isolated workers so complex coding tasks are split, owned, and driven to completion recursively.
  2. Coordination and correctness are the main bottlenecks for parallel agents: naive locking and expecting perfect commits cause conflicts and serialization, so robust coordination and tolerance for imperfect commits are needed to scale.
  3. Human input still matters a lot—clear, prioritized instructions, tests, and failure analysis are essential to guide agents, enforce performance and resource limits, and catch subtle bugs agents miss.

Tech Talks Weekly #9 ☀️ ☀️ ☀️

Tech Talks Weekly • 39 implied HN points • 04 Apr 24
🕹 Technology Software Engineering
  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.

Acceleration With Consequences

Kathy PM • 7 implied HN points • 03 Jan 26
🕹 Technology Software Engineering
  1. AI is shifting from one-off features to ongoing relationships, so tools will be judged by how they behave and fit into users' lives over weeks, not just by single outputs.
  2. Agency and control matter more than raw intelligence; the hardest design choices are about when an AI should act, when it should stay quiet, and who gets to decide.
  3. Working code alone won’t win — teams need understandable, maintainable systems and clear mental models, because loss of trust and confusing handoffs will drive people away faster than bugs.

The Chatbot Trap

Artificial Ignorance • 176 implied HN points • 14 Nov 24
🕹 Technology Software Engineering
  1. Using chatbots for AI interactions can be confusing and hard work. They require a lot of mental effort to figure out what to input and understand the output, making simple tasks feel complicated.
  2. Good design for AI tools should allow for easy, direct manipulation of tasks. Instead of a chat interface, we should use designs that show clear options and let users interact with the AI in a simpler, more visual way.
  3. The future of AI products will focus on tailored interfaces that fit specific needs. These will provide ways to access AI's power more directly and intuitively, similar to how we moved from basic mobile sites to advanced apps.

Research -> Reality. AI Engineers.

potentialmind • 19 implied HN points • 18 May 24
🕹 Technology Software Engineering
  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 Software Engineering
  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 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 Software Engineering
  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 Engineering
  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.

Joe's Nerdy Rants #3

Joe Reis • 98 implied HN points • 03 Jun 23
🕹 Technology Software Engineering
  1. In many companies, there is a divide between software engineering and data teams.
  2. Data is becoming more integrated into applications, blurring the lines between data and software.
  3. The divide between software and data teams will eventually disappear as data becomes more critical to businesses.

🗓 Your invitation to weekly craftsmanship topics

🔮 Crafting Tech Teams • 59 implied HN points • 08 Jan 24
🕹 Technology Software Engineering
  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 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.

Interfaces all the way down

Jinay's Substack • 94 HN points • 23 Jul 23
🕹 Technology Software Engineering
  1. Designing high-quality interfaces is crucial for software engineers as they advance in their careers.
  2. Maintaining independence between software components through well-constructed interfaces is important to avoid technical debt.
  3. Crafting interfaces with the end user, other developers, in mind can lead to more intuitive software design.

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

Weekend Developer • 19 implied HN points • 11 May 24
🕹 Technology Software Engineering
  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 Software Engineering
  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.

Edition #2: Agent Security Standards + Identity/Authorization + Secure Agent Engineering + SecureVibes Update

Boring AppSec • 7 implied HN points • 17 Dec 25
🕹 Technology Software Engineering
  1. AI agent systems need new standards to measure and manage risk because traditional vulnerability scoring and IAM are built for deterministic, human-scoped sessions. Risk models must account for agent autonomy and enforce task-scoped, intent-aware access.
  2. Building secure agents means defending against prompt injection and goal-manipulation, providing explicit contextual inputs, sandboxed error handling, and continuous evals and tracing to tame probabilistic behavior.
  3. Product security should favour practical, incremental fixes and context-aware prioritization, using AI to propose patches but validating downstream impacts and tribal knowledge. Strengthening agent-to-agent protocols and supply-chain controls is essential to prevent abuse and preserve trust.

5 Easy Tips for Writing Cleaner Code [Technique Tuesdays]

Technology Made Simple • 119 implied HN points • 11 Jan 23
🕹 Technology Software Engineering
  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.

The slow death of the hands-on engineering manager

Leading Developers • 139 implied HN points • 12 Nov 24
🕹 Technology Software Engineering
  1. Many engineering managers want to code more, but their roles shift them away from hands-on work. Finding even a few hours a week to code can help stay engaged with the team.
  2. Choosing small, impactful tasks can keep managers involved in coding. Projects that help the team or solve annoying issues can be both beneficial and satisfying.
  3. Creating internal tools, like a chatbot for documentation, can improve efficiency and learning. Such projects can bring value to the team while allowing managers to practice their skills.

Star Attention: Efficient LLM Inference over Long Sequences

Gonzo ML • 126 implied HN points • 09 Dec 24
🕹 Technology Software Engineering
  1. Star Attention allows large language models to handle long pieces of text by splitting the context into smaller blocks. This helps the model work faster and keeps things organized without needing too much communication between different parts.
  2. The model uses what's called 'anchor blocks' to improve its focus and reduce mistakes during processing. These blocks are important because they help the model pay attention to the right information, which leads to better results.
  3. Using this new approach, researchers found improvements in speed while preserving quality in the model's performance. This means that making these changes can help LLMs work more efficiently without sacrificing how well they understand or generate text.

A neat way to catch unrelaible data [Technique Tuesdays]

Technology Made Simple • 79 implied HN points • 07 Jun 23
🕹 Technology Software Engineering
  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
  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 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 Software Engineering
  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.

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

Technology Made Simple • 99 implied HN points • 29 Jan 23
🕹 Technology 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.

📩 Your invite to the live streams—Wed: Tailwind Thu: Refactoring

🔮 Crafting Tech Teams • 39 implied HN points • 22 Jan 24
🕹 Technology Software Engineering
  1. There are live streams on Wednesdays focusing on Tailwind and on Thursdays with Software Engineering Fundamentals Series.
  2. The streams cover fundamentals of architecture and design techniques, with plans to delve into XP disciplines in February.
  3. Upcoming events include live coding sessions on NextJS, React, and Tailwind, as well as discussions on the Economics of Refactoring.

I used AI to analyze 10,000 Leetcode Discussion Posts

Peak Performance by David Goudet • 2 HN points • 19 Aug 24
🕹 Technology Software Engineering
  1. Successful software developers are often very dedicated and passionate about their craft. They put in years of practice, learning, and perseverance to achieve their goals.
  2. Common trends in tech interviews include a mix of coding challenges, system design discussions, and behavioral questions. Practicing through platforms like Leetcode can give developers a clear idea of what to expect.
  3. Many programmers experience imposter syndrome, but facing challenges can lead to growth. Staying resilient and reaching out for support can help in tough times.

Evaluating LLM Agents and Applications

LLMs for Engineers • 79 implied HN points • 11 Jul 23
🕹 Technology Software Engineering
  1. Evaluating large language models (LLMs) is important because existing test suites don’t always fit real-world needs. So, developers often create their own tools to measure accuracy in specific applications.
  2. There are four main types of evaluations for LLM applications: metric-based, tools-based, model-based, and involving human experts. Each method has its strengths and weaknesses depending on the context.
  3. Understanding how well LLM applications are performing is essential for improving their quality. This allows for better fine-tuning, compiling smaller models, and creating systems that work efficiently together.

What's JavaScript?

Technically • 34 implied HN points • 15 Jul 25
🕹 Technology Software Engineering
  1. JavaScript is the most popular programming language today and initially started as a way to make websites interactive.
  2. It works alongside HTML and CSS; HTML is for structure, CSS is for style, and JavaScript makes things interactive, like buttons and animations.
  3. JavaScript can now be used for both the front end and back end of applications thanks to tools like Node.js and TypeScript, making it a powerful all-in-one language.

What is Encapsulation anyway?

🔮 Crafting Tech Teams • 39 implied HN points • 04 Jan 24
🕹 Technology Software Engineering
  1. Effort is often wasted on debating the visibility of code elements without understanding the why and when behind such practices.
  2. Software Engineering Fundamentals series aims to provide a strong foundational knowledge for building upon in 2024.
  3. Crafting Tech Teams publication offers a 7-day free trial for readers to access full post archives.

o3 is important, but not because of benchmarks

Artificial Ignorance • 92 implied HN points • 23 Dec 24
🕹 Technology Software Engineering
  1. OpenAI's new model, o3, shows impressive benchmark performance, particularly in tasks that are tough for AI, but it's more about how AI is evolving rather than just hitting high scores.
  2. The way AI systems process information is changing. Instead of needing huge amounts of data and time upfront, they can now improve their performance during use, making development faster and cheaper.
  3. Even though o3 is advanced, it doesn't mean we've reached artificial general intelligence (AGI). It's a step in that direction, but more improvements and different benchmarks are needed to really understand AI's potential.