The hottest Software Engineering Substack posts right now

And their main takeaways
Category
Top Technology Topics

Skills, Tools and MCPs - What’s The Difference?

Artificial Ignorance • 172 implied HN points • 24 Jan 26
đź•ą Technology Software Engineering
  1. Tools let models perform real actions by calling functions or APIs, but each integration is bespoke and coordinating multiple tools quickly becomes hard to scale.
  2. MCP standardizes discovery and access to capabilities so connectors can be reused across models, but it raises security, auditability, and decision-quality risks that standardization alone doesn't solve.
  3. Skills package human expertise as reusable prompts and workflows so models know when and how to use tools, and together tools + MCP + skills form a stack for AI-native experiences even though the primitives and standards are still evolving.

Claude Code Is MS-DOS. Who Builds Windows?

Enterprise AI Trends • 232 implied HN points • 04 Jan 26
đź•ą Technology Software Engineering
  1. Claude Code is powerful because the agent can roam your computer’s file system and use your project files, SOPs, and history as emergent memory instead of a separate memory service.
  2. Its command-line interface and low-level primitives like skills and agents live in hidden folders, so it’s great for developers but too technical for most knowledge workers and won’t scale as-is.
  3. Enterprises need a new, user-friendly layer—the "Windows of AI"—that preserves file-system-powered agency while making it accessible, because chat-only interfaces alone won’t enable mass adoption and will leave adoption K-shaped.

One Million Checkboxes

eieio games • 399 implied HN points • 26 Jun 24
đź•ą Technology Software Engineering
  1. There is a website called One Million Checkboxes that has a million checkboxes on it.
  2. When you check a box, it gets checked for everyone using the site, creating a shared experience.
  3. The site has become very popular, and the creator plans to show how many boxes have been checked once things settle down.

Common SQL queries through a non-1NF lens

Minimal Modeling • 202 implied HN points • 12 Jan 26
đź•ą Technology Software Engineering
  1. Model joins by attaching a nested dataset to each outer row and then flattening by duplicating the outer row for each inner row; if the inner set is empty you skip the outer row for INNER JOIN or replace it with a single NULL row for LEFT JOIN.
  2. The inner part of a query becomes very simple: INNER JOIN is just a filtered SELECT, GROUP BY is an aggregated filtered SELECT, and LEFT JOIN is a filtered SELECT plus a conditional UNION ALL NULL row, so no special-casing is needed.
  3. Splitting queries into an outer table and a per-row inner dataset gives a clear, teachable mental model and a single canonical flattening rule you can reuse to reason about more complex SQL patterns like correlated subqueries.

Continuous Software Engineering: Part 2 - Continuous Planning & Prioritization

The Healthy Engineering Leader • 19 implied HN points • 19 Sep 24
đź•ą Technology Software Engineering
  1. Continuous Planning means regularly updating your plans as things change. This helps teams stay effective and respond quickly to new information.
  2. Continuous Prioritization allows teams to adjust their focus based on what’s most important at any moment. This ensures they always work on tasks that matter the most.
  3. Both continuous planning and prioritization make teams more adaptable. They can shift their strategies easily and keep delivering value, even in changing environments.
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How I code going into 2026

Olshansky's Newsletter • 183 implied HN points • 05 Jan 26
đź•ą Technology Software Engineering
  1. Most coding is now delegated to AI agents, so engineers spend their time orchestrating agent personalities and guiding work rather than writing code by hand.
  2. Practical workflows matter: use Makefiles as a stable CLI, leave TODOs instead of side quests, maintain prompts/skills, write short copy-paste friendly docs, and review critical diffs on GitHub.
  3. Team roles and skills are shifting: leaders must be hands-on translators of intent into agent-driven work, focusing on system design, taste, and continuously improving agent behavior.

Under the microscope: Roadsters (Dreamcast, PlayStation)

Rings of Saturn • 87 implied HN points • 28 Jan 26
đź•ą Technology Software Engineering
  1. The same game uses completely different cheat systems on each platform, so the N64, Dreamcast, and PlayStation versions each have unique ways to unlock hidden features and content.
  2. On Dreamcast, pressing face buttons on the title screen fills a buffer and matching specific eight-button sequences triggers secrets; these unlock a Pong mini-game, extra goofy cars, a free-flight camera, five turbo boosts, the staff roll, and one sequence that appears to do nothing.
  3. On PlayStation, two distinct eight-button title-screen sequences give big rewards: one sets your Roadster Trophy cash to ten million and the other unlocks Category A/B cars, and entering both also marks several championship trophies as completed.

Levels of greatness

next big thing • 37 implied HN points • 12 Feb 26
đź•ą Technology Software Engineering
  1. Greatness exists in distinct layers, and the gap between each level can be enormous — someone who’s great at one level can be thoroughly outclassed by the next.
  2. Many systems follow a power-law pattern where a tiny number of people, companies, or places capture most of the attention, wealth, or returns.
  3. AI, especially models that can help build and improve themselves, is accelerating that concentration, so a small set of firms is likely to pull much farther ahead.

o3, Oh My

Don't Worry About the Vase • 3852 implied HN points • 30 Dec 24
đź•ą Technology Software Engineering
  1. OpenAI's new model, o3, shows amazing improvements in reasoning and programming skills. It's so good that it ranks among the top competitive programmers in the world.
  2. o3 scored impressively on challenging math and coding tests, outperforming previous models significantly. This suggests we might be witnessing a breakthrough in AI capabilities.
  3. Despite these advances, o3 isn't classified as AGI yet. While it excels in certain areas, there are still tasks where it struggles, keeping it short of true general intelligence.

25 Unpopular and Contradictory Opinions on Data and Engineering

The Data Jargon Newsletter • 138 implied HN points • 23 Aug 24
đź•ą Technology Software Engineering
  1. If your data product isn't making money, it's really just an internal tool. It's important to focus on projects that add real value.
  2. Having a good Business Intelligence team can often bring more benefits than trying to make fancy data products. Simple tools can lead to effective data use.
  3. More data engineers can improve your data platform, but just adding analysts might not directly make your data team better. It's all about how the team fits with the organization.

Top 10 Books we read in 2025

Graphs For Science • 105 implied HN points • 10 Jan 26
đź•ą Technology Software Engineering
  1. A strong theme is practical engineering: many books show how to turn LLM demos into working agents using RAG, embeddings, knowledge graphs, tool use, and prompt patterns to make outputs more reliable and auditable.
  2. There’s a clear focus on hands-on playbooks and trade-offs—quick-starts, checklists, code examples, and patterns for prototyping, retrieval, latency/cost decisions, multi-agent orchestration, and production concerns.
  3. The collection balances technical how-to guidance with broader perspectives on responsible use, human uniqueness, organizational strategy, and interdisciplinary science, highlighting ethics, norms for academics, and big-picture questions about life and intelligence.

Nobody is shipping your agent’s code (yet) | Predictions from LinearB’s Ori Keren

Dev Interrupted • 56 implied HN points • 03 Feb 26
đź•ą Technology Software Engineering
  1. AI has erased the blank-page problem and speeds up code generation, but those upstream gains are being lost to chaotic code reviews, testing, and integration unless teams build proper infrastructure.
  2. Agentic tools that can control your local machine (like OpenClaw/Moltbot) show huge power but create major security and governance risks, so most organizations won’t give them autonomous control yet.
  3. The economics of software are shifting: survival favors substrate-efficient tools and firms with unique data or "insight compression," and the current "dark flow" of vibe coding can make teams feel faster while actually introducing hidden bugs, so risk-aware pipelines and better testing are essential.

Beyond the Code

The Engineering Leader • 59 implied HN points • 15 Sep 24
đź•ą Technology Software Engineering
  1. Top software engineers excel not just in coding but in understanding the bigger picture of their projects. They focus on why they're building something, making sure it meets real needs.
  2. Effective communication and collaboration are key traits of great engineers. They share knowledge with their teams and explain their ideas clearly, making work smoother for everyone.
  3. It's important for engineers to keep learning beyond just coding skills. The best engineers adapt to new challenges, use innovative tools like AI, and think creatively to solve problems.

All That Is Solid Melts into Code

Daniel Pinchbeck’s Newsletter • 29 implied HN points • 14 Feb 26
đź•ą Technology Software Engineering
  1. AI has reached an inflection point where models can rapidly automate broad white‑collar cognitive tasks. This is already eroding entry‑level jobs and changing roles like software engineers into architects and debuggers.
  2. If human labor becomes optional, the economy could see extreme wealth concentration and mass unemployment unless we redesign how abundance and income are shared. Without policy changes, the link between work and survival may break for many people.
  3. Powerful self‑improving AI brings huge opportunities—faster creativity and the collapse of old knowledge hierarchies—but also serious risks like cyberattacks or engineered harms, so urgent governance and planning are needed.

Will AI kill SaaS? The case for and against disruption

Brick by Brick • 45 implied HN points • 03 Feb 26
đź•ą Technology Software Engineering
  1. AI that generates code and autonomous agents is collapsing the upfront cost of building software and can replace much of the human labor that SaaS products currently coordinate, threatening the old SaaS economic model.
  2. Big frictions—like high switching costs, regulatory and accountability needs, data gravity, and organizational inertia—make wholesale replacement of incumbent SaaS slow and hard.
  3. Disruption will be uneven and gradual: tools that automate repetitive, text-heavy workflows are most at risk, and winners will be challengers who target high-toil use cases or incumbents who proactively adopt agentic solutions.

Time to Welcome Claude 3.7

Don't Worry About the Vase • 2419 implied HN points • 26 Feb 25
đź•ą Technology Software Engineering
  1. Claude 3.7 is a new AI model that improves coding abilities and offers a feature called Extended Thinking, which lets it think longer before responding. This makes it a great choice for coding tasks.
  2. The model prioritizes safety and has clear guidelines for avoiding harmful responses. It is better at understanding user intent and has reduced unnecessary refusals compared to the previous version.
  3. Claude Code is a helpful new tool that allows users to interact with the model directly from the command line, handling coding tasks and providing a more integrated experience.

The AI Manager's Schedule

Artificial Ignorance • 105 implied HN points • 16 Jan 26
đź•ą Technology Software Engineering
  1. AI turns many maker tasks into delegated work, so your day shifts from long deep blocks to lots of short five-to-fifteen minute management intervals and juggling multiple agents.
  2. New top skills are clear vision, smart delegation, and orchestration — you need to know the end state, break work into bite-sized chunks, and run or coordinate multiple agents, and you must keep strong taste and bullshit detection to judge AI output.
  3. The change can speed up shipping and hugely amplify experienced people, but it also brings risks like micromanagement fatigue, juniors not learning, and initial slowdowns from debugging AI output; over time tools should reduce overhead and make these managerial skills broadly valuable.

Code Was Just My Paintbrush

On Engineering • 44 implied HN points • 08 Feb 26
đź•ą Technology Software Engineering
  1. AI is turning code into a tool rather than the destination, shifting work away from wrestling with syntax and boilerplate toward creating user value.
  2. The most valuable role becomes a product engineer who brings taste, empathy, and vision — deciding what to build and why, not just how to code it.
  3. With the barrier between idea and implementation collapsing, the winners will be the people who can envision meaningful products, not just write code the fastest.

AI links, 11/23/2025

In My Tribe • 227 implied HN points • 23 Nov 25
đź•ą Technology Software Engineering
  1. AI can improve signals like cover letters, but it can also dilute their value if everyone uses it equally well. If the best candidates leverage AI effectively, the signal can get stronger instead.
  2. Using AI tools like ChatGPT can hinder learning if students rely on them too much. It's better for students to think independently first before using AI to enhance their work.
  3. Teams are using AI creatively to boost productivity in unique ways. They're not just doing their jobs but finding better ways to optimize their workflow continuously.

The feature backlog has gone *poof*

next big thing • 32 implied HN points • 08 Feb 26
đź•ą Technology Software Engineering
  1. AI coding agents have recently crossed a threshold and are letting developers and multi-agent setups write and ship a lot more product, so many teams are seeing their feature backlogs disappear.
  2. Companies are at different adoption stages, and engineering teams need to become fluent with agentic tools or risk falling behind; startups that use these tools can amplify their speed and focus.
  3. Public SaaS and companies aiming to IPO must show they leverage agentic engineering to drive faster feature delivery, revenue growth, and better margins, because easier software development risks commodifying existing offerings and hurting valuations.

Developer experience at scale – lessons from Dropbox

Engineering Enablement • 14 implied HN points • 25 Feb 26
đź•ą Technology Software Engineering
  1. Productivity is a sociotechnical problem. You need to invest in reliable systems and tooling while also changing culture, meeting structures, and leadership alignment so engineers can do deep, uninterrupted work.
  2. Roll out AI alongside developer experience work and make sure build, test, and telemetry systems are strong so developers trust AI-assisted workflows. Use exec-level signals to accelerate adoption, enable fast experiments, offer multiple tools, and build internal platforms when third-party tools don’t scale.
  3. The big unsolved challenge is linking productivity gains to business outcomes. AI frees capacity that often goes to migrations and tech debt, but companies lack the instrumentation to show how that work turns into revenue or faster customer value.

5 engineering dogmas it's time to retire

Leading Developers • 139 implied HN points • 16 Dec 25
đź•ą Technology Software Engineering
  1. Don’t automatically reach for a third‑party package; weigh the security, maintenance, and reliability costs of a dependency against writing and owning the code yourself.
  2. Rigid rules like mandatory reviews for every PR and fixed 2–4 week sprints can slow teams and kill creativity; trust skilled engineers, consider pair programming, and try alternative ways of working that fit your team.
  3. Use feature flags judiciously because they add complexity and testing burden, and don’t be dogmatic about comments—short, clear comments can save future developers a lot of time.

Don't be an asshole

Tech Ramblings • 39 implied HN points • 25 Aug 24
đź•ą Technology Software Engineering
  1. Being a good software engineer is not just about coding. It's also important to have writing and social skills.
  2. Most project failures happen due to human issues, not technical ones. Understanding people and reducing conflicts is key to project success.
  3. Having empathy, showing respect, and evaluating ideas fairly are important for teamwork. Treat others well and focus on solving business problems.

The YOLO Shield: Why Autonomous AI Needs a “Temporal Kill Switch”

Phoenix Substack • 14 implied HN points • 24 Feb 26
đź•ą Technology Software Engineering
  1. Giving an AI agent full live permissions is risky because any destructive or exfiltration action can become permanent in a static environment.
  2. Use a temporal sandbox that regularly wipes and recreates infrastructure and rotates network identities and tokens mid-session so damage is erased and attacker tunnels are broken before they persist.
  3. Don’t rely on slow detection; assume systems will drift and enforce deterministic hygiene by resetting to a known-good state so you can preserve agent autonomy without lasting harm.

Latency is a silent killer.

System Design Classroom • 299 implied HN points • 16 May 24
đź•ą Technology Software Engineering
  1. Getting timeouts right is important. If you wait too long, your system slows down, but if you timeout too fast, you might miss a successful call.
  2. Circuit breakers help manage failures. They quickly stop requests to a failing service, allowing your system to recover faster.
  3. Bulkheads keep parts of your system separate. If one part fails, the others keep working, preventing a complete shutdown of the system.

Rebuilding Google Maps

Elizabeth Laraki • 659 implied HN points • 23 Feb 24
đź•ą Technology Software Engineering
  1. Google Maps had to change a lot because it was getting too complicated with too many features. The team decided to redesign it so users could find what they needed easily.
  2. The redesign focused on making the map easier to use by creating one main search box instead of many tabs for different tasks. This helped simplify the user experience.
  3. It's important for products to keep evolving. By regularly checking how users interact with the product and making improvements, it can grow and stay relevant.

What developers can do to combat the growing threat of rogue AI agents

Dev Interrupted • 32 implied HN points • 05 Feb 26
đź•ą Technology Software Engineering
  1. AI agents can go rogue by repeatedly or unpredictably calling APIs, chaining actions, or accessing data outside their intent, so permissive or poorly scoped endpoints become big operational risks.
  2. Treat agents as first-class API consumers: use clear, spec-driven contracts, structured schemas, and least-privilege identities with short-lived tokens so agent behavior is predictable and easy to revoke.
  3. Practical guardrails like rate limits, schema validation, anomaly detection, and strong observability are essential to spot and contain misbehavior, and keep deterministic systems separate from agentic workflows to reduce risk.

A framework for ML Design Interviews

Recommender systems • 86 implied HN points • 10 Jan 26
đź•ą Technology Software Engineering
  1. A repeatable ML design interview framework can greatly improve your success in FAANG-level interviews and has led to many offers.
  2. A good framework helps you pace the discussion, create a coherent narrative, and signal to interviewers what you would have covered with more time.
  3. The full framework is only shared privately on request instead of being posted publicly, so you need to message on Substack to receive it.

Formabble Prepares to Go Open Source

Formabble’s Substack • 2 HN points • 01 Oct 24
đź•ą Technology Software Engineering
  1. Formabble is going open source soon, which will make it more accessible for developers. This shift aims to encourage transparency and collaboration in game development.
  2. The platform uses AI to help developers create games more easily. Its features include automating coding tasks and offering intelligent suggestions, making game design simpler and more creative.
  3. Formabble's new design promotes better teamwork, especially for multiplayer games. It allows players to sync their game data in real-time and even continue playing offline, improving the overall gaming experience.

How to Be a 30x AI Engineer with a Taste

Pratik’s Pakodas 🍿 • 10 implied HN points • 19 Feb 26
đź•ą Technology Software Engineering
  1. Taste — the ability to evaluate work, choose what to build, and foresee what will matter — is now the most valuable engineering skill because AI can generate code itself.
  2. Engineers with strong taste make compounding decisions about product, architecture, and quality that drive outsized impact and pay, and that depends on adjacent skills like product thinking, user empathy, and clear communication.
  3. Taste can be developed deliberately through practice: study great products and papers, do side-by-side critiques, prototype rapidly, and run projects like evaluation rubrics, onboarding redesigns, or timeboxed product builds to train recognition, compass, and vision.

Data Science Weekly - Issue 528

Data Science Weekly Newsletter • 799 implied HN points • 05 Jan 24
đź•ą Technology Software Engineering
  1. Data Science Weekly shares curated news and articles each week related to data science, AI, and machine learning. This helps readers stay updated on important trends and topics.
  2. Deepnote emphasizes using its own platform for building data infrastructure, showcasing how versatile tools can simplify data tasks. It highlights the importance of a universal computational medium.
  3. A reliable A/B testing system is essential for businesses to make informed decisions and optimize performance. Companies that use effective experimentation platforms can significantly improve their outcomes and reduce manual work.

How Unix Spell Ran in 64kB RAM

Confessions of a Code Addict • 1683 implied HN points • 12 Jan 25
đź•ą Technology Software Engineering
  1. Unix engineers faced a big challenge in fitting a large dictionary into just 64kB of RAM. They came up with clever ways to compress the data and use efficient structures to make everything fit.
  2. A key part of their solution was the Bloom filter, which helped quickly check if words were in the dictionary without needing to look up every single word, saving time.
  3. They also used innovative coding methods to further reduce the size of the data needed for the dictionary, allowing for fast lookups while staying within the strict memory limits of their hardware.

Cultivating Excellence in Teams with Continuous Software Engineering — Part 1: Defining Excellence through 5 Characteristics

The Healthy Engineering Leader • 99 implied HN points • 09 Jul 24
đź•ą Technology Software Engineering
  1. An effective team knows what its customers want and focuses on building the right product. They prioritize features based on customer needs and data.
  2. High craftsmanship involves a team that produces quality work while minimizing bugs. They continuously learn and share knowledge to improve their software.
  3. Good communication and collaboration create a cohesive team environment. Everyone feels safe to share ideas, which helps solve problems together.

A Software Engineer's Guide to Reading Research Papers

Confessions of a Code Addict • 1467 implied HN points • 28 Jan 25
đź•ą Technology Software Engineering
  1. Research papers are important for software engineers to keep up with new technologies and fill knowledge gaps. It helps to stay current with developments in your field, like time series analysis.
  2. Many people find reading research papers hard because they can be dense and technical. A lack of a research background can make it seem even more intimidating.
  3. With time and practice, anyone can learn to read and understand research papers. Finding a personal approach or framework can make the process easier.

Tidying: Canonical Order

Software Design: Tidy First? • 132 implied HN points • 28 Nov 25
đź•ą Technology Software Engineering
  1. The piece looks at 'canonical order' in tidying code — how to pick a consistent order for things like variable declarations.
  2. A tiny example (int x, y vs int y, x) shows that order can change and asks whether a basic principle should decide which order is correct.
  3. The detailed discussion is behind a paid subscriber paywall, and the author also offers to give talks to teams or organizations.

are we ready?

Poems, Short stories and other things.. • 14 implied HN points • 17 Feb 26
đź•ą Technology Software Engineering
  1. AI tools are already automating large parts of software development, turning work that once took weeks into hours and making many traditional coding tasks far less central. This means coding-as-a-job is being fundamentally reshaped.
  2. Many roles—developers, product people, support, analysts, managers, and admins—will be disrupted and need to shift to higher-order work like creativity, domain knowledge, and mastering AI tools. Adapting to these new responsibilities is essential to stay relevant.
  3. Adoption is uneven, so people and companies who try and master advanced tools now will gain a big advantage as workflows automate at scale. The pace of change is accelerating, so quick adaptation matters.

AI multi-agents with corporate structures

Alex’s Substack • 66 HN points • 25 Jul 24
đź•ą Technology Software Engineering
  1. Having multiple teams competing against each other leads to better results for AI agents, just like it does in big companies.
  2. A system that relies on one leader to make decisions tends to perform worse, as it can create weak points if the leader fails.
  3. The way teams are organized influences how well they solve problems, and using effective structures can improve AI performance.

Anyone Else Struggling to Keep Up With Data Tools

SeattleDataGuy’s Newsletter • 447 implied HN points • 31 Jul 25
đź•ą Technology Software Engineering
  1. Focus on mastering just a couple of technologies each year instead of trying to learn everything at once. It’s better to really understand a few tools well than to have a surface-level knowledge of many.
  2. Start with the basics that won’t go away, like SQL and core principles of data management. New tools can come and go, but some fundamentals will always be important.
  3. Build side projects or engage in real work opportunities to apply what you've learned. Practical experience is one of the best ways to deepen your understanding of data tools.

Under the microscope: Ford Mustang: The Legend Lives & Ford vs. Chevy (PlayStation 2, Xbox)

Rings of Saturn • 58 implied HN points • 03 Jan 26
đź•ą Technology Software Engineering
  1. You can uncover hidden cheat codes in old PS2 and Xbox games by inspecting runtime memory and controller input ring buffers with tools like Ghidra and runtime breakpoints; many games check a recent-input buffer each frame to match special sequences.
  2. In Ford Mustang: The Legend Lives, entering Up, Up, Up, Left, Down, Up, Right, Down, Down, Left, Right, Right, Up on the Profile screen unlocks all cars and all track variants on both PS2 and Xbox.
  3. In Ford vs. Chevy, pressing Up, Down, Left, Right at the main menu opens an Enter Cheat screen, and typing the word "POINTS" grants 625,000 points to buy cars; this cheat-buffer code is reused across multiple Eutechnyx games.