The hottest Software Engineering Substack posts right now

And their main takeaways
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Under the microscope: Kyuutenkai (Saturn, PlayStation)

Rings of Saturn • 72 implied HN points • 23 Dec 25
đź•ą Technology Software Engineering
  1. Four secret passwords were found that trigger special effects on both PlayStation and Saturn versions, including one that gives nine chances, near-max points, all clear crystals, and access to the final boss and ending.
  2. The notable special passwords are SWSH YUTN MD (nine chances + max-ish points + clears), CHANCEGAX9 (nine chances), S1TENCLEAR (all clear crystals), and ABNA1YABA1 (zero chances).
  3. PlayStation and Saturn use different encoding phrases so most "natural" passwords aren’t compatible across systems; the reverse engineering showed the Saturn check uses a bitwise NOT against a static table and normal passwords are formed by XOR-ing game state with one of four platform-specific phrases.

Analyst reactions: How AI is reshaping engineering organizations

Engineering Enablement • 11 implied HN points • 18 Feb 26
đź•ą Technology Software Engineering
  1. Hiring is shifting toward AI‑fluent roles like “AI Engineer,” and companies are putting much more emphasis on code quality because AI makes writing code easier but often produces sloppy output that reviewers must catch.
  2. Early, fragmented AI experiments are being centralized into platform-level models (AI Centers of Excellence or hub-and-spoke), so platform teams now own governance, orchestration, and making AI a standard developer tool.
  3. A new operational layer—LLMOps—is emerging to run models, ship integrations, and create reusable prompts, while human challenges like security training, unclear ROI, and uncontrolled developer experimentation remain the biggest risks.

The Sequence Opinion #786: The Great Absorption: When System Code Becomes Model Weights

TheSequence • 56 implied HN points • 08 Jan 26
đź•ą Technology Software Engineering
  1. Many system and agent capabilities that used to live in external orchestration code are being internalized into model weights, so models now handle tasks once implemented by separate scripts and pipelines.
  2. Hand‑coded scaffolding like prompt chains, vector DB glue, and custom parsers is increasingly at risk of becoming obsolete whenever a new frontier model checkpoint appears, so expect rapid disruption.
  3. Product teams need to distinguish permanent infrastructure from temporary scaffolding and architect systems to tolerate or embrace model internalization, or else large parts of their stack can be replaced overnight.

Web standards and our future as developers

Bite code! • 978 implied HN points • 04 Mar 25
đź•ą Technology Software Engineering
  1. Web development needs a balance between standardization and diversity. If everything is too standard, creativity suffers; too much diversity leads to chaos. Finding the right mix is key.
  2. History shows us that monopolies in web browsers can lead to stagnation and problems for developers. Just like with Internet Explorer 6, when one browser dominates, innovation can slow down.
  3. We should support alternatives to Chrome to prevent the rise of another monopoly. Using and promoting different browsers helps keep the web healthy and encourages a variety of options for developers.

Two types of software engineers

Register Spill • 825 implied HN points • 09 Apr 23
đź•ą Technology Software Engineering
  1. There are two types of software engineers based on how they perceive the difficulty of problems.
  2. Type 1 engineers believe non-technical problems are easy because people can just do X, while Type 2 engineers find them hard due to people being involved.
  3. Type 2 engineering embraces building with and for people, recognizing and accepting the messiness that comes with human involvement.
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5 Lessons I learned the hard way from 10+ years as a software engineer

High Growth Engineer • 1462 implied HN points • 03 Nov 24
đź•ą Technology Software Engineering
  1. Always learn from your mistakes, as they can teach valuable lessons for your career. Embracing failure can help you grow and improve.
  2. Networking is important; make connections in your industry. Relationships often open doors to new opportunities and collaborations.
  3. Keep your skills updated and be open to new technologies. The tech field is constantly evolving, and staying current helps you stay relevant.

Skills: The Missing Piece in AI Security Tooling

Boring AppSec • 23 implied HN points • 23 Jan 26
đź•ą Technology Software Engineering
  1. Generic threat modeling tools miss risks unique to multi‑agent AI systems, so one‑size‑fits‑all methods like STRIDE are insufficient.
  2. Skills are modular, LLM‑native knowledge packages that let agents detect agentic patterns and find context‑specific threats (like cascade failures and goal hijacking) that generic rules miss.
  3. Skills are portable and quick to create and share, so teams can build reusable, relevant expertise that yields better findings than lots of generic noise.

More Efficiency, More Demand

Fish Food for Thought • 47 implied HN points • 31 Dec 25
đź•ą Technology Software Engineering
  1. When tools make tasks cheaper and easier, we usually do more of those tasks, not less; efficiency expands demand and creates new uses.
  2. Automation tends to shift work, not eliminate it — machines handle repetitive parts while people take on harder, higher-value tasks like interpretation, edge cases, and oversight.
  3. AI will grow opportunities for engineers and data scientists by increasing the amount of software and systems to build, maintain, secure, and govern, shifting work toward architecture, judgment, and integration rather than rote coding.

Should everyone do everything now?

Anant’s Newsletter • 6 implied HN points • 22 Feb 26
đź•ą Technology Software Engineering
  1. AI tools have made it easy to do credible work in neighboring roles, collapsing the old boundaries between engineering, design, and product.
  2. That ease creates a Dunning‑Kruger risk where people reach superficial competence and ship work that misses many subtle but important details and edge cases.
  3. The right response is to learn other disciplines deeply enough to know where your judgment ends, use AI to help but exercise restraint, and defer to specialized craft knowledge when needed.

The Sliding Window Technique[Technique Tuesdays]

Technology Made Simple • 279 implied HN points • 28 Feb 24
đź•ą Technology Software Engineering
  1. The sliding window technique is a powerful algorithmic model used for problem-solving in coding interviews and software engineering, offering efficiency and practicality.
  2. Benefits of using the sliding window technique include reducing duplicate work, maintaining consistent linear time complexity, and its utility in AI feature extraction processes.
  3. Spotting the sliding window technique involves identifying keywords like maximum, minimum, longest, or shortest, dealing with continuous elements, and converting brute-force approaches into efficient solutions.

Setting up NextJS with GitHub Actions and feature flags using Flagsmith

The Open Source Expert • 59 implied HN points • 05 Jul 24
đź•ą Technology Software Engineering
  1. Using NextJS helps streamline your project with standardized setups, making it easier to onboard and rapidly develop features.
  2. Automating tasks with GitHub Actions can save time and reduce errors, giving you quick feedback on your code changes.
  3. Feature flags from Flagsmith allow you to control which features are visible without needing to redeploy your app, making it easier to manage updates and A/B tests.

The 'delayed opinions givers' - engineering teams everybody hates

Leading Developers • 70 implied HN points • 02 Dec 25
đź•ą Technology Software Engineering
  1. Prioritize unblocking other teams and put their urgent needs before your own most of the time, because being helpful builds trust and speeds up the whole company.
  2. Don’t give delayed attention — slow reviews and late answers cause wasted developer weeks, messy merges, technical debt, and demoralized engineers, so respond promptly to requests you agree to handle.
  3. Make work visible and set boundaries: use simple trackers so requests don’t get lost, help teams the first few times while teaching them to do better, and escalate or block repeat abusers.

What makes a software engineer effective?

Elevate • 477 implied HN points • 29 Nov 23
đź•ą Technology Software Engineering
  1. Effectiveness in software engineering is about focusing on what matters most and delivering value to users, the business, and career with the available time.
  2. Traits that help software engineers be effective include caring about user needs, being a good problem solver, and keeping things simple while prioritizing quality.
  3. To excel as an exceptional software engineer, embrace change, balance technical debt and innovation, and emphasize continuous learning and teamwork.

Lost in Bugspace

Breaking Smart • 27 implied HN points • 10 Jan 26
đź•ą Technology Software Engineering
  1. Software implementation has a one-way time asymmetry: you can usually tell the minimum time needed, but there is no reliable upper bound. Rare, heavy-tailed bugs create a "bugspace" where time stretches and effort stops correlating with progress.
  2. Debugging becomes fundamentally harder as many independent factors combine — skewed defect distributions, NP‑hard diagnosis, poor observability, human cognitive limits, and organizational frictions — turning implementation into costly search and diagnosis. Tools and heuristics can collapse complexity briefly, but they fail when their assumptions break, producing long stalls and regime shifts.
  3. When stuck there are three pragmatic exits: restart and discard history, ship an expedient imperfect solution, or embrace yak‑shaving and expand scope for internal integrity. Each choice trades off predictable delivery, internal quality, and environmental robustness, so you need to pick explicitly which clock you’re answering to.

The Sequence AI of the Week #805: Goodfire and the Era of AI Interpretability

TheSequence • 14 implied HN points • 11 Feb 26
đź•ą Technology Software Engineering
  1. Modern AI is built by optimizing huge datasets with gradient descent, which produces powerful but opaque "black box" models.
  2. Relying only on prompts and RLHF is like doing behavioral psychology on an alien mind because we don't understand the model's internal workings; without interpretability tools, reliability and safety are limited.
  3. Interpretability efforts like feature steering and agent internals are pushing toward a "Software 3.0" where engineers can intentionally design a model's internal behavior, and investor interest shows the industry is shifting from alchemy to intentional, inspectable AI.

Software engineering for security: Performance Tradeoffs

Frankly Speaking • 305 implied HN points • 10 Jul 25
đź•ą Technology Software Engineering
  1. Security and engineering need to talk the same language about performance tradeoffs. If security teams understand the technical decisions engineers make, they can suggest solutions that actually work.
  2. Different security decisions involve risks. For example, faster systems might use more memory, or stricter access controls can slow things down. It's important to weigh these risks carefully.
  3. Having security engineers understand both the risks and the tech helps make processes smoother. They can address problems directly and bridge the gap between security needs and engineering realities.

Advanced Prompting Guide for AI Engineering

Engineering Enablement • 13 implied HN points • 04 Feb 26
đź•ą Technology Software Engineering
  1. Structured prompting is required for complex, high‑risk engineering work; techniques like graph‑based prompts help reveal hidden dependencies, prioritize rules, and manage changing state.
  2. Use controlled validation loops and dual‑implementation strategies to improve governance and reduce risk, and apply diff‑only refactoring to make large code changes less invasive and more token‑efficient.
  3. The guide is vendor‑agnostic and practical, with Do/Don't scenarios and full prompt/code examples, and it’s useful to engineers and non‑engineers working with coding assistants, agents, or spec‑driven workflows.

The Sequence Radar #763: Last Week AI Trifecta: Opus 4.5, DeepSeek Math, and FLUX.2

TheSequence • 70 implied HN points • 30 Nov 25
đź•ą Technology Software Engineering
  1. Claude Opus 4.5 is impressively smart and can handle complex coding tasks, making it feel like a senior engineer rather than just a chatbot.
  2. DeepSeek Math V2 shows how AI can self-correct and improve its mathematical reasoning, hitting new highs in performance and reliability.
  3. FLUX.2 brings amazing visual quality and features for generative media, proving that open models can achieve top-notch results without being locked down.

Bretton Goods is becoming Speculative Decoding

Bretton Goods • 38 implied HN points • 27 Dec 25
đź•ą Technology Software Engineering
  1. The blog is changing focus from explaining why countries get rich to studying AI — especially how to tell what AI systems are actually doing.
  2. The author shifted careers from policy and macroeconomics to computer science and now works on AI evaluations and reducing hallucinations through internships and a job at Elicit.
  3. Bretton Goods will be archived and its audience moved to a new Substack, Speculative Decoding, with a commitment to roughly one post a month about AI evaluations, safety, policy, and related research.

SpatialBench: Can Agents Analyze Real-World Spatial Biology Data?

LatchBio • 41 implied HN points • 26 Dec 25
🔬 Science Software Engineering
  1. SpatialBench is a realistic suite of 146 verifiable spatial biology problems across five platforms and seven task types that recreates real analyst workspaces using snapshots of data and images.
  2. Current agent models perform poorly overall (roughly 20–38% accuracy) and vary widely by task and platform, and the choice of execution harness or wrapper can change outcomes as much as changing the base model.
  3. Inspecting agent trajectories reveals clear failure modes and productive strategies, showing that detailed traces help explain performance and that benchmarks like this are a practical first step toward engineering agents that can reliably automate spatial biology analysis.

Frameworks for Managing Up as a Software Engineer

High Growth Engineer • 624 implied HN points • 09 Feb 25
đź•ą Technology Software Engineering
  1. Building trust with your manager is essential. Focus on being sincere, reliable, caring, and competent in your work.
  2. You need to speak your manager's language. Understand their goals and priorities to communicate effectively and prevent misunderstandings.
  3. Regular updates matter. Create a system for updates that keeps your manager informed without overwhelming them, ensuring that every communication is valuable.

Technically Monthly (February 2026)

Technically • 14 implied HN points • 05 Feb 26
đź•ą Technology Software Engineering
  1. Modern generative models mirror pathways in the human brain, and many researchers believe leveraging that similarity could be key to much stronger AI.
  2. Real cloud-spend data shows the fastest-growing AI use cases are coding agents, low-latency LLM inference, and computational biology, while AI art and video generation have plateaued as the market professionalizes.
  3. Models overuse em dashes mainly because of their training data and tokenization quirks—older texts and auto-converted punctuation make the em dash common—and this highlights how dataset quality and representativeness drive model behavior.

Yes, you need to duplicate your frontend business logic on the server

Bite code! • 978 implied HN points • 13 Oct 24
đź•ą Technology Software Engineering
  1. Always have your business logic on both the frontend and the server. If you only trust the client side, you risk getting incorrect data.
  2. Your server needs to handle requests from various sources, including non-standard browsers and bots. These can bypass your frontend checks if they're not replicated on the server.
  3. Any important checks for security and data integrity should happen on the server to prevent unexpected issues. This means you'll often have to duplicate checks that you already do on the frontend.

How Capital One supports 14,000 technologists with one pipeline | Ameesh Paleja

Dev Interrupted • 28 implied HN points • 06 Jan 26
đź•ą Technology Software Engineering
  1. Standardizing build and deployment pipelines and automating SRE tasks removes repetitive work so large engineering teams can move like startups and focus on high‑value problems.
  2. AI in 2026 shifts from demos to real procurement: organizations will budget heavily for AI and should prioritize applying models to new workflows while enforcing strong security and governance.
  3. Pausing deploys (like Friday freezes) often increases risk by accumulating untested changes; regular, practiced deployments build resilience and reduce surprise failures.

2026 - the year of AGI

davidj.substack • 23 implied HN points • 13 Jan 26
đź•ą Technology Software Engineering
  1. AGI means an AI that can learn many different tasks and perform many things at least as well as a typical human — it doesn't require sentience or being a superintelligence.
  2. Progress toward AGI will rely more on post-training learning: agents that can learn after deployment, retain skills, and build or use tools, rather than just bigger pretraining runs.
  3. Narrow AGI will appear in specific domains soon via agents that learn and share useful skills while keeping private data local, but these systems will still have clear limits and won't replace all human abilities.

Claude 4 and Anthropic's bet on code

Democratizing Automation • 332 implied HN points • 27 May 25
đź•ą Technology Software Engineering
  1. Claude 4 is a strong AI model from Anthropic, focused on coding and software tasks. It has a unique personality and improved performance over its predecessors.
  2. The benchmarks for Claude 4 might not look impressive compared to others like ChatGPT and Gemini, which could affect its market position. It's crucial for Anthropic to show real-world utility beyond just numbers.
  3. Anthropic aims to lead in software development, but they fall behind in general benchmarks. This may limit their ability to compete with bigger players like OpenAI and Google in the race for advanced AI.

You can't teach someone to swim when they're drowning

Resilient Cyber • 119 implied HN points • 16 Apr 24
đź•ą Technology Software Engineering
  1. It's important to build software with security in mind from the start, rather than trying to add it in later. This 'Secure-by-Design' approach can prevent many issues down the line.
  2. Software suppliers should take responsibility for the security of their products, as their decisions affect a lot of users. Customers shouldn't always have to 'patch and fix' flawed products themselves.
  3. The rapid growth of known software vulnerabilities is overwhelming for organizations. Instead of just telling them to fix everything quickly, we should push for better, more secure products from the beginning.

A quick introduction to Memory Pools [Math Mondays]

Technology Made Simple • 179 implied HN points • 27 Feb 24
đź•ą Technology Software Engineering
  1. Memory pools are a way to pre-allocate and reuse memory blocks in software, which can significantly enhance performance.
  2. Benefits of memory pools include reduced fragmentation, quick memory management, and improved performance in programs with frequent memory allocations.
  3. Drawbacks of memory pools include fixed-size blocks, overhead in management, and potential for memory exhaustion if not carefully managed.

Attitude: The Secret Ingredient in Software Engineering and Beyond

Mindful Matrix • 219 implied HN points • 29 Jan 24
đź•ą Technology Software Engineering
  1. Having a growth mindset is essential in software engineering and life. Viewing challenges as opportunities for growth helps in overcoming obstacles and achieving success.
  2. Failure should be seen as a learning experience. Embracing mistakes, analyzing them, and using them as lessons leads to resilience and growth.
  3. Receiving feedback with an open mind and using it as a tool for improvement contributes to rapid skill development and fosters a collaborative work environment.

7 Actionable Tips to Become Great at Software Engineering

Brain Bytes • 238 implied HN points • 03 Jan 24
đź•ą Technology Software Engineering
  1. Set a horizon goal to guide your learning and career decisions; it could be mastering a specific area, building a professional network, or creating something new.
  2. Stay updated with new technologies and coding methodologies to remain relevant in the tech industry; consider leveraging tools like AI to enhance your work.
  3. Reflect on your career as if looking back 100 years from now to focus on meaningful, long-lasting contributions; prioritize mastering programming basics, continuous learning, soft skills, and understanding software development methodologies.

The Sequence AI of the Week #781: The Amazing GLM 4.7

TheSequence • 28 implied HN points • 31 Dec 25
đź•ą Technology Software Engineering
  1. GLM-4.7 is built to act like an "employee" rather than a chatty companion, prioritizing reliable task execution over conversational flair.
  2. Its architecture—mixing a mixture-of-experts design with a "Preserved Thinking" approach—is optimized for long-context loops, terminal error recovery, and stateful reasoning to handle real-world workflows.
  3. As an open-weight model focused on engineering and autonomous workflows, it’s positioned to become a standard choice for software development and task automation in 2026.

Beyond Guesswork: The Art and Science of Project Estimation

Mindful Matrix • 179 implied HN points • 08 Feb 24
đź’Ľ Business Software Engineering
  1. Project estimation is a critical skill influencing project success; it involves setting realistic expectations, aligning efforts, and managing resources effectively.
  2. Key considerations in estimation include understanding project scope, conducting risk analysis, and utilizing estimation strategies like historical analysis and buffer times.
  3. Transparency and communication are crucial in estimation; transparency helps manage stakeholder expectations while effective communication ensures clarity and trust in the estimation process.

Improving the product

André Casal's Substack • 19 implied HN points • 31 Jul 24
đź•ą Technology Software Engineering
  1. Getting user feedback is really important. Talking to customers helps understand their needs, especially beginners in tech.
  2. Watching a seasoned developer use the product can reveal issues and areas for improvement. It's a great way to learn about friction points.
  3. Making things easier for users is key. Simplifying processes and providing good documentation can really help users get started faster and reduce confusion.

Data Science Weekly - Issue 513

Data Science Weekly Newsletter • 359 implied HN points • 21 Sep 23
đź•ą Technology Software Engineering
  1. There's a new newsletter focusing on AI safety in China, showing that the country is more invested in AI safety than many think.
  2. A podcast discusses how startups can run better AI models without needing to upgrade their hardware—a big challenge in the field.
  3. An online event is coming up for those looking to secure data science jobs in big tech, focusing on interview strategies and market insights.