The hottest Software Engineering Substack posts right now

And their main takeaways
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Top Technology Topics

Data Science Weekly - Issue 492

Data Science Weekly Newsletter • 379 implied HN points • 28 Apr 23
🕹 Technology 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.

The AI Divide

Engineering Enablement • 16 implied HN points • 23 Dec 25
🕹 Technology Software Engineering
  1. Most AI experiments stall before they deliver real business value; teams that succeed pick narrow, workflow-specific use cases, give ownership to domain leaders, and embed AI into existing tools and processes.
  2. Buying and partnering with external AI vendors reaches production much more often than building everything in-house; successful buyers treat vendors as partners, demand customization, and focus on measurable outcomes and integration.
  3. AI augments engineers rather than replacing them — it speeds up routine tasks but struggles with complex, context-heavy work, so engineers retain responsibility for architecture, correctness, and higher-level design and decision-making.

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
  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
  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.
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Everything New Has Bugs

Fish Food for Thought • 23 implied HN points • 03 Dec 25
🕹 Technology Software Engineering
  1. When you speed up releases or adopt new systems, bugs and incidents will usually rise at first — it’s a natural tradeoff between velocity and stability.
  2. Give teams slack and real ownership so they can fix problems, learn, and improve quality instead of just reacting to fires.
  3. Invest in supporting systems and feedback loops like CI/CD, observability, error budgets, and postmortems so you can absorb turbulence and restore quality faster.

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

Research-Driven Engineering Leadership • 119 implied HN points • 08 Jan 24
🕹 Technology Software Engineering
  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

3 Years of ChatGPT

Olshansky's Newsletter • 22 implied HN points • 03 Dec 25
🕹 Technology Software Engineering
  1. AI is already here as an amplifier of human intelligence and is being used daily across personal and professional tasks; agent-driven tools have massively increased productivity, especially for coding.
  2. High-quality, unique data and expert-labeled "golden" datasets are the most valuable assets for building useful AI systems; simple benchmarks and naive fine-tuning are limited, while reinforcement fine-tuning and dedicated context engineering will drive real gains.
  3. Practical changes are coming in the next few years: local inference stations, agentic e-commerce, consolidation of tooling, and new roles like context engineers and AI bootcamps; foundational roles like architects will remain and superintelligence isn’t expected soon.

The Measurement Problem in Software Engineering

Maestro's Musings • 17 implied HN points • 15 Dec 25
🕹 Technology Software Engineering
  1. Counting artifacts like lines of code, story points, or PR counts has repeatedly failed; these proxies miss real value, are easy to game, and can harm organizations.
  2. AI both breaks traditional metrics—making code volume meaningless and often increasing churn and bugs—and widens perception gaps where developers feel faster than measured results show.
  3. A promising path is semantic, context-aware measurement that uses AI to understand what changes actually do and synthesize those findings into simple narratives for leaders, aiming for "good enough" insight that’s harder to game.

The software engineering "squeeze"

Leading Developers • 109 implied HN points • 24 Jun 25
🕹 Technology Software Engineering
  1. Software engineering was once an easy path to a high-paying job, but many engineers are now feeling the pressure due to high competition and the rise of AI.
  2. There are a lot of average engineers in the field, which is causing a squeeze; companies are looking for those who truly stand out and have a mix of skills beyond just coding.
  3. It's important for engineers to continuously improve their skills and take initiative instead of waiting for job offers, as the demand for great engineers remains strong.

Challenges In Adopting Retrieval-Augmented Generation Solutions

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 01 Apr 24
🕹 Technology 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.

How to grow from a mid-level to senior Data Engineer

SeattleDataGuy’s Newsletter • 694 implied HN points • 14 Feb 24
🕹 Technology Software Engineering
  1. To grow from mid to senior level, it's important to continuously learn and improve, share new knowledge, work on code improvements, and become an expert in a certain domain.
  2. Making the team better is crucial - focus on mentoring, sharing knowledge, and creating a positive team environment. Think beyond individual tasks to impact the overall team outcomes.
  3. Seniority includes building not just technical solutions, but solutions that customers love. Challenge requirements, understand the business and product, and take initiative in problem-solving.

Looking at the Datastructures- Trees [Math Mondays]

Technology Made Simple • 179 implied HN points • 18 Jul 23
🕹 Technology Software Engineering
  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.

DevProd headcount benchmarks, Q1 2026

Engineering Enablement • 10 implied HN points • 07 Jan 26
🕹 Technology Software Engineering
  1. Most companies dedicate about 2–6% of engineering headcount to centralized developer productivity, averaging roughly 4.7%, and that percentage tends to shrink as organizations grow past ~1,000 engineers because tooling, automation, and leverage reduce headcount needs.
  2. The benchmark counts only narrowly-defined DevProd teams (internal developer platforms, DevEx/Productivity, build & release, test infra, and developer education/support) and excludes SRE, general cloud, security, and product-facing platform teams.
  3. Treat these numbers as a guideline, not a quota: use them to set initial headcount for a center of excellence and pair them with measurement (for example, the Core 4) to confirm the team is actually reducing developer friction.

Managing for Abundance, not Scarcity

Anant’s Newsletter • 8 implied HN points • 14 Jan 26
🕹 Technology Software Engineering
  1. Writing code is now cheap because of AI, so the real constraints are context, taste, and decision-making — shift from protecting developer hours to enabling rapid experimentation and customer outcomes.
  2. Middle managers and leaders need to get hands-on and write code; pure people managers should no longer be acceptable, and everyone should be expected to be a builder.
  3. Restructure teams toward a 'diamond' model with more senior builders who can wield AI end-to-end, kill spec-first culture in favor of working prototypes, and measure success by iterations and customer outcomes instead of time estimates.

Learnings from yet another 'blitzscaling' stage hiring excercise

system bashing • 176 implied HN points • 01 Jul 23
🕹 Technology Software Engineering
  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.

How To Fast Track Your Career As An Engineer

SeattleDataGuy’s Newsletter • 930 implied HN points • 12 Aug 23
💼 Business Software Engineering
  1. Focusing on impact in your work can accelerate your career growth and lead to more satisfying outcomes.
  2. To have more impact in tech, run towards unsolved problems, be scrappy in finding solutions, and prioritize ruthlessly.
  3. Impact can be achieved by reducing costs or increasing revenue, and understanding how your work contributes to these areas is essential for career advancement in engineering.

The stream processing model behind Google Cloud Dataflow

VuTrinh. • 39 implied HN points • 27 Apr 24
🕹 Technology Software Engineering
  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 Software Engineering
  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 Software Engineering
  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.

The age of the human bottleneck is ending. The age of human intent is beginning

Brick by Brick • 18 implied HN points • 27 Nov 25
🕹 Technology Software Engineering
  1. AI will replace the old human-centric development pipeline with compact "Engine Room" teams where autonomous agents build, test, and deploy most of the product.
  2. This makes companies far more productive and lean — much higher revenue per employee, much faster shipping cycles, and many startups intentionally capping headcount because they simply don’t need more people.
  3. Human roles will shift from writing code to defining strategic intent, tuning and auditing AI systems, and handling judgment, ethics, and risk.

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
  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.

Compound AI is AGI

Generating Conversation • 233 implied HN points • 13 Dec 24
🕹 Technology Software Engineering
  1. The debate about whether we've achieved AGI (Artificial General Intelligence) is ongoing. Many people don't agree on what AGI really means, making it hard to know if we've reached it.
  2. The argument is that current AI models can work together to perform tasks at a human-like level. This teamwork, or 'compound AI,' could be seen as a form of general intelligence, even if it's not from a single AI model.
  3. Not all forms of intelligence are the same, and AI systems can do things that humans can’t, but that doesn't mean they can't be considered intelligent. The future potential of AI isn't just about mimicking human intellect; it may also involve different types of skills and knowledge.

Pratik's Year in Review: 2025

Pratik’s Pakodas 🍿 • 8 implied HN points • 02 Jan 26
🕹 Technology Software Engineering
  1. AI agents and skill-based subagents let you run many tasks in parallel and move work forward continuously. This shifts the role from single-threaded coder to an orchestrator who delegates, evaluates, and scales output.
  2. Building a community depends on habits and critical mass more than platform features; migrating people to a new platform requires heavy outreach, patience, and active admins. Tools help, but sustained engagement is earned through social habits, not just better functionality.
  3. Measure and design life around 'great days' by engineering conditions that produce them — prioritize sleep, consistent healthy habits, slack time, and small experiments. Small rituals and intentional choices matter more for long-term happiness than chasing big achievements.

Edition 30: The SDLC is changing and so will AppSec (again)

Boring AppSec • 69 implied HN points • 22 Jul 25
🕹 Technology Software Engineering
  1. Software development is changing with new tools, especially those powered by AI. This means that AppSec will also need to adapt to keep up with these changes.
  2. The way we manage software development and security must evolve, focusing on how to handle code prompts and automated reviews more effectively.
  3. As non-developers start writing more code using AI tools, we need to be careful because this code might be less secure. Therefore, engaging with all team members involved in code creation is important.

AI and productivity: Year-in-review with Microsoft, Google, and GitHub researchers

Engineering Enablement • 13 implied HN points • 17 Dec 25
🕹 Technology Software Engineering
  1. Lines of code is a poor measure of AI’s value — more output doesn’t equal more impact. Use broader measures like satisfaction, performance, collaboration, and efficiency to judge whether AI actually helps.
  2. AI is changing the developer role from code producer to director and validator of AI-assisted work, so hiring, career paths, and training must prioritize AI fluency, systems thinking, and judgment. Juniors might learn end-to-end problem solving faster, but only if teams preserve mentorship and opportunities to collaborate.
  3. The real wins come from enablement and focusing AI on real bottlenecks or tedious work, not from constantly switching tools or models. Also, don’t trust simple headlines — dig into context, and design tools to boost creativity and meaningful automation rather than just raw speed.

The hidden costs of pre-computing data | Chalk's Elliot Marx

Dev Interrupted • 14 implied HN points • 09 Dec 25
🕹 Technology Software Engineering
  1. Pre-computing and storing large volumes of derived data wastes money and adds latency because most of it is never used. Shifting to real-time, incremental pipelines means you only compute what users actually need.
  2. Owning the full stack (hardware, training, and cloud) creates a competitive moat and can change leaderboard dynamics quickly. Design your systems to be model-agnostic and flexible so you don’t get locked into one provider.
  3. Typical engineering metrics like velocity or lines of code are often misleading; measure what exposes real friction, bottlenecks, and business outcomes. Use metrics to make the system legible and actionable, not just to produce executive reports.

Tech Debt vs Quality

Fish Food for Thought • 14 implied HN points • 10 Dec 25
🕹 Technology Software Engineering
  1. Tech debt and bugs are different: bugs are immediate errors to fix, while tech debt is the future cost of taking shortcuts and can be intentional or accidental, so decide and plan when to incur it.
  2. Make debt visible and economic: track where it slows work, measure the "interest" it charges in developer time or incidents, and prioritize paying down high-interest items rather than treating all debt equally.
  3. Leadership and culture matter: embed maintenance into planning, keep slack for cleanup, use retrospectives and metrics to shorten recovery time, and design continuous improvement cycles so velocity and quality compound over time.

Math for Software Engineering[Math Mondays]

Technology Made Simple • 139 implied HN points • 21 Mar 23
🕹 Technology Software Engineering
  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.

AI made us faster. That’s not the same as better.

Brick by Brick • 9 implied HN points • 24 Dec 25
🕹 Technology Software Engineering
  1. AI coding tools have evolved into a diverse, faster set of assistants with different interaction styles, and engineers now choose which tool to use for each task.
  2. These tools speed up work but rarely produce code that’s clearly better — most AI-generated code still needs human review, polishing, or refactoring before it’s ship-ready.
  3. Engineers use AI selectively and responsibly: they get productivity and satisfaction gains while maintaining ownership of code quality and long-term maintenance.

One month of using Devin

Generating Conversation • 163 implied HN points • 23 Jan 25
🕹 Technology Software Engineering
  1. Devin is good for fixing small, specific coding tasks quickly, saving time for developers. It works best when given straightforward instructions on simple issues.
  2. However, Devin struggles with more complex tasks that require understanding and linking multiple components together. In those cases, it can produce confusing or unusable results.
  3. Although Devin shows promise in AI-assisted programming, it's still not at the level of a junior software engineer. There's definitely room for improvement as the technology develops.

Engineering Managers Should Not Exist

Victor’s Substack • 41 HN points • 26 Mar 24
🕹 Technology Software Engineering
  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.

Data Science Weekly - Issue 497

Data Science Weekly Newsletter • 199 implied HN points • 02 Jun 23
🕹 Technology Software Engineering
  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 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.