The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

Taking a New Direction

In My Tribe • 486 implied HN points • 04 Jun 25
🚌 Education Software Development
  1. The focus is shifting towards developing an AI-assisted seminar, which aims to improve higher education through new technology. This will involve creating a learning environment where students can interact with AI characters instead of traditional lectures.
  2. The project is driven by concerns that current universities are not equipped to innovate or improve their practices, especially after past mistakes. The hope is that AI can help create a better educational model without the need to build completely new institutions.
  3. The developer is learning about modern software development and working step-by-step with a software partner to create a functional product. They plan to share progress updates and insights from this journey, showcasing both the potential of AI and the seminar format.

Briefing: Shopify Magic, Vision Pro Zoom, AX vs UX

Department of Product • 393 implied HN points • 01 Feb 24
🕹 Technology Software Development
  1. MultiOn and Arc Browser are challenging the assumption that users interacting with products are human by automating browsing tasks
  2. Shopify introduced over 100 new product updates in its Winter Edition, including a media editor for generating product images instantly
  3. Google, PayPal, Zoom, TikTok, and OpenAI all revealed new features and products this week, showcasing advancements in technology

GroupBy #36: Agoda- How We Solve Load Balancing Challenges in Apache Kafka, How to reduce your Snowflake cost

VuTrinh. • 139 implied HN points • 21 May 24
🕹 Technology Software Development
  1. Working on pet projects is fun, but it's important to have clear learning goals to actually gain knowledge from them.
  2. When using tools like Spark or Airflow, always ask what problem they solve to understand their value better.
  3. To make your projects more effective, think like a user and check if they get what they need from your data systems.

Field notes: What I learned building a Mac app with AI assistance

Kathy PM • 26 implied HN points • 28 Jan 26
🕹 Technology Software Development
  1. Start with a visual design or mockup so the AI and you share a clear reference point, which keeps implementation and thinking grounded.
  2. AI makes it possible to tackle lower-level or unfamiliar technical work and add polish that used to feel impractical. Expect the final 10%—debugging, edge cases, and performance tuning—to still take most of the time.
  3. You still need coding fluency and platform knowledge, so be explicit about APIs and UI components, do research on libraries, and use logging and detailed in-code comments to debug and avoid regressions.
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Weak Soft Skills: Why you are stuck at the Senior engineer level

Path to Staff Engineer • 35 HN points • 04 Aug 24
🕹 Technology Software Development
  1. Soft skills are important for engineers to move from senior to staff levels. They help in communicating and working well with others.
  2. Mastering communication includes writing clearly, speaking confidently, and being aware of body language. This helps ensure your ideas are understood.
  3. Being adaptable and knowing how to handle challenges is key. Flexibility and good problem-solving skills are highly valued in teams.

Additional Testing After Refactoring

Software Design: Tidy First? • 950 implied HN points • 20 Jan 25
🕹 Technology Software Development
  1. It's important to write more tests after refactoring. This helps improve accuracy and confidence in your code.
  2. When you break down a big piece of code into smaller parts, consider writing smaller tests for those parts, especially if you plan to reuse them.
  3. You might face a dilemma on whether to keep redundant tests after refactoring. It's good to regularly review tests to make sure you have the best approach for checking your code.

It's time to come to grips with AI

Silver Bulletin • 922 implied HN points • 27 Jan 25
🕹 Technology Software Development
  1. AI is becoming very powerful and it could change many things in society. We need to talk about its risks and benefits honestly.
  2. The left is not fully engaging in discussions about AI, which is concerning as this technology is rapidly evolving. Everyone should be part of the conversation to shape its future.
  3. Dismissing AI as overhyped is misguided; rather, we should explore its potential impacts and work together to ensure it benefits everyone.

INTRODUCING ATIA'S LEGACY — THE AXIE MMO

The Lunacian • 782 implied HN points • 06 Mar 25
🕹 Technology Software Development
  1. Axie Infinity: Atia's Legacy is a new MMO set in its unique universe where players can explore and create communities. It's designed for both mobile and PC gaming.
  2. Players can pre-register for play testing and earn rewards by referring friends and creating content. This will start around Summer 2025.
  3. The game will feature squad-based combat, dynamic progression, and social interactions, all focusing on player-owned assets to enrich the gaming experience.

Monthly Python Data Engineering, September 2024

Monthly Python Data Engineering • 2 HN points • 26 Sep 24
🕹 Technology Software Development
  1. A new free book called 'How Data Platforms Work' is being created for Python developers. It will explain the inner workings of data platforms in simple terms, with one chapter released each month.
  2. The Ibis library has removed the Pandas backend and now uses DuckDB, which is faster and has fewer dependencies. This change is expected to improve performance and usability.
  3. Several popular libraries in Python, such as GreatTables and Shiny, have released updates with new features and improvements, focusing on better usability and integration with modern technologies.

What comes next with reinforcement learning

Democratizing Automation • 435 implied HN points • 09 Jun 25
🕹 Technology Software Development
  1. Reinforcement learning (RL) is getting better at solving tougher tasks, but it's not easy. There's a need for new discoveries and improvements to make these complex tasks manageable.
  2. Continual learning is important for AI, but it raises concerns about safety and can lead to unintended consequences. We need to approach this carefully to ensure the technology is beneficial.
  3. Using RL in sparser domains presents challenges, as the lack of clear reward signals makes improvement harder. Simple methods have worked before, but it’s uncertain if they will work for more complex tasks.

The Over-Engineering Trap

Tech Ramblings • 39 implied HN points • 28 Jul 24
🕹 Technology Software Development
  1. Simplicity and maintainability are more important than performance and complexity in software design. Focus on creating code that's easy for others to understand and work with.
  2. Avoid over-complicated platforms like microservices, especially if your application doesn't need them. Start with basic tools and scale only when necessary.
  3. Your goal as a software engineer should be to deliver a product that customers can use easily. Keeping things simple helps with maintenance and helps new team members get up to speed faster.

💥 Tech Talks Weekly #25

Tech Talks Weekly • 59 implied HN points • 01 Aug 24
🕹 Technology Software Development
  1. Tech Talks Weekly shares fresh talks from over 100 conferences every week. It's a great way to stay updated without sifting through a lot of content.
  2. This edition highlights talks from major events like ReactConf and JCON Europe. The featured talks include exciting topics like new features in React and domain-driven design.
  3. Readers are encouraged to fill out a form to help improve content and to spread the word about the newsletter. It's all about building a community around tech discussions!

Is It Time to Say Goodbye to Data Engineers?

SeattleDataGuy’s Newsletter • 812 implied HN points • 06 Feb 25
🕹 Technology Software Development
  1. Data engineers are often seen as roadblocks, but cutting them out can lead to major problems later on. Without them, the data can become messy and unmanageable.
  2. Initially, removing data engineers may seem like a win because things move quickly. However, this speed can cause chaos as data quality suffers and standards break down.
  3. A solid data strategy needs structure and governance. Rushing without proper planning can lead to a situation where everything collapses under the weight of disorganization.

Does data quality matter?

benn.substack • 1099 implied HN points • 22 Nov 24
🕹 Technology Software Development
  1. Data quality is important for making both strategic and operational decisions, as inaccurate data can lead to poor outcomes. Good data helps companies know what customers want and improve their services.
  2. AI models can tolerate some bad data better than traditional methods because they average out inaccuracies. This means these models might not break as easily if some of the input data isn’t perfect.
  3. Businesses now care more about AI than they used to about regular data reporting. This shift in focus might make data quality feel more important, even if it doesn’t technically impact AI model performance as much.

Under the microscope: Andretti Racing & NASCAR Heat (Saturn, PlayStation)

Rings of Saturn • 58 implied HN points • 18 Dec 25
🕹 Technology Software Development
  1. Andretti Racing (Saturn and PlayStation) and NASCAR Heat (PlayStation) hide previously undocumented Easter eggs that are activated by entering special player names.
  2. Andretti Racing’s name cheats swap the normal pre-race portrait for hidden pictures — the Saturn build has eight images (including production staff, a family photo, and a cartoon alien) while the PlayStation build contains 33 mostly staff photos.
  3. NASCAR Heat recognizes eight specific aliases to add bonus drivers who are the development staff photoshopped into a DICE racing suit, and those drivers race in a special DICE-branded car.

Tech gamble

Alex Ewerlöf Notes • 353 implied HN points • 25 Jan 24
🕹 Technology Software Development
  1. Tech gamble is about paying the price of hypothetical future tech debt upfront without proper data or insight, leading to waste and friction for the product.
  2. Symptoms of tech gamble include complex technical solutions for simple problems, big bang improvement projects cancelled mid-execution, and rewriting systems without clear pragmatic checkpoints.
  3. Tech debt is reactive, while tech gamble is proactive, with tech debt giving engineers a bad conscience and tech gamble representing naive ambition or malice.

Finding Data Bugs in dbt Pull Requests

clkao@substack • 39 implied HN points • 17 Aug 24
🕹 Technology Software Development
  1. Data bugs can be costly for companies, with bad data potentially costing up to 25% of their revenue. These issues often arise from problems in data-centric systems like dbt.
  2. Using dbt allows data engineers to implement software practices like version control and testing, helping to ensure the correctness of their data transformations. However, relying solely on post-processing tests has its limits.
  3. Manual spot checks are still crucial in ensuring data accuracy during code reviews. Tools like Recce aim to streamline this process, making it easier for developers to validate and document their changes.

Relational modeling challenge

Minimal Modeling • 811 implied HN points • 02 Feb 25
🕹 Technology Software Development
  1. A key goal in data modeling is to make sure invalid data states cannot be created. This means designing systems where incorrect data combinations are impossible.
  2. The challenge presented involves creating a way to track daily coffee consumption while preventing contradictory data entries, like recording that a user both had coffee and was coffee-free on the same day.
  3. Using common database features, the task is to develop a solution that complies with standard relational model rules, avoiding the use of tricks like JSON data types or triggers.

Claire Vo: The CPO who automates herself with AI

CommandBlogue • 19 implied HN points • 19 Aug 24
🕹 Technology Software Development
  1. AI is changing how product managers work. It helps them complete tasks much faster, which could mean fewer PMs are needed in the future.
  2. The role of PMs might shift more towards being makers, meaning they will need to have skills in design and engineering to stay relevant.
  3. To break into product management, it's important to show what you can do by building something real for the companies you're interested in, rather than just sending a resume.

2023, the Year of AI; 2024?

Sector 6 | The Newsletter of AIM • 399 implied HN points • 01 Jan 24
🕹 Technology Software Development
  1. 2023 saw major advancements in AI technology, leading to exciting stories and developments. The growth of AI in various sectors sparked interest and engagement from the public.
  2. Microsoft announced a significant investment in OpenAI, marking the third phase of their partnership. This collaboration aims to enhance AI supercomputing and make breakthroughs in technology.
  3. As we move into 2024, there is anticipation for more innovative AI content and opportunities. The community looks forward to exploring how AI can further evolve and impact our lives.

Data Science Weekly - Issue 526

Data Science Weekly Newsletter • 419 implied HN points • 22 Dec 23
🕹 Technology Software Development
  1. Generative AI is changing how we work with tools, improving the Human-Tool Interface. This can help us use technology in ways we never could before.
  2. Support Vector Machines (SVMs) can be very effective for prediction tasks, often outperforming other models in error rates. However, they aren’t as commonly used, possibly due to their complexity.
  3. Deep multimodal fusion is useful in surgical training. It helps classify feedback from experienced surgeons to trainees by combining different types of data like text, audio, and video.

Speed Without Understanding - One of the Biggest Risks in Data Engineering

SeattleDataGuy’s Newsletter • 329 implied HN points • 30 Jun 25
🕹 Technology Software Development
  1. Speed in data engineering can be risky. Acting fast without fully understanding the consequences can lead to mistakes, like accidentally deleting important data.
  2. Every new tool or change can add complexity. If something breaks, it may cause confusion for others, so it’s important to think carefully about what you build.
  3. Having a mix of experienced and new team members is really helpful. It encourages sharing knowledge and can prevent big errors when someone leaves the team.

💥 Tech Talks Weekly #24

Tech Talks Weekly • 59 implied HN points • 26 Jul 24
🕹 Technology Software Development
  1. Tech Talks Weekly is a free email newsletter that shares recent talks from dozens of tech conferences. It's a great way to catch up on what you missed!
  2. Readers can participate by filling out a short form to help improve the content. This makes it a community-driven resource.
  3. The newsletter highlights popular talks each week, making it easier for people to discover valuable insights from experts in tech.

Data Science Weekly - Issue 545

Data Science Weekly Newsletter • 139 implied HN points • 03 May 24
🕹 Technology Software Development
  1. Reusing data analysis work can save time and help teams focus on building new capabilities instead of just repeating old ones.
  2. Open-source models can be a better choice than proprietary ones for developing AI applications, making them cheaper and faster.
  3. Causal machine learning helps predict treatment outcomes by personalizing clinical decisions based on individual patient data.

That Time I Tried To Get Johnathan Blow To Publish a Game to Windows 8

Meanwhile, on the other side of my brain... • 99 implied HN points • 29 May 24
🕹 Technology Software Development
  1. Setting realistic goals is crucial for success, rather than unachievable targets that can lead to frustration.
  2. Building genuine relationships and solving real problems with developers can lead to lasting goodwill.
  3. Understanding and meeting the needs of developers is key to successful developer relations, instead of focusing solely on unreachable goals.

Self-documenting Makefiles

Blog System/5 • 827 implied HN points • 10 Jan 25
🕹 Technology Software Development
  1. Using Makefiles can help stitch together complex build processes easily. They allow you to create a command dispatcher with minimal code.
  2. By implementing a 'make help' command, you can provide users with a clear overview of available actions and necessary configuration, reducing confusion.
  3. Documenting both targets and user-settable variables in Makefiles can make them more user-friendly. This helps users know how to interact with the project without getting lost.

Data Exfiltration in OpenAI Agent Builder via MCP

PromptArmor Blog • 138 implied HN points • 14 Oct 25
🕹 Technology Software Development
  1. There's a risk with AI applications passing the responsibility of security to users. Many people don't know how to protect themselves from prompt injection attacks, which makes this a big issue.
  2. Even with safety features like Guardrails, attackers can still trick AI systems into leaking sensitive data. This shows that current protections aren't foolproof.
  3. AI models might recognize malicious prompts but still process them, allowing harmful instructions to be passed through multiple steps in a workflow. This can lead to serious security issues.

Large language models, explained with a minimum of math and jargon

The Counterfactual • 599 implied HN points • 28 Jul 23
🕹 Technology Software Development
  1. Large language models, like ChatGPT, work by predicting the next word based on patterns they learn from tons of text. They don’t just use letters like we do; they convert words into numbers to understand their meanings better.
  2. These models handle the many meanings of words by changing their representation based on context. This means that the same word could have different meanings depending on how it's used in a sentence.
  3. The training of these models does not require labeled data. Instead, they learn by guessing the next word in a sentence and adjusting their processes based on whether they are right or wrong, which helps them improve over time.

Open LLMs don’t need to beat OpenAI

The AI Frontier • 119 implied HN points • 09 May 24
🕹 Technology Software Development
  1. Open LLMs, like Llama 3, are getting really good and can perform well in many tasks. This improvement makes them a strong option for various applications.
  2. Fine-tuning open LLMs is becoming more attractive because of their improved quality and lower costs. This means smaller, specialized models can be more easily developed and used.
  3. However, open models likely won't surpass OpenAI's offerings. The proprietary models have a big advantage, but open LLMs can still thrive by focusing on efficiency and specific use cases.

o3: The grand finale of AI in 2024

Democratizing Automation • 815 implied HN points • 20 Dec 24
🕹 Technology Software Development
  1. OpenAI's new model, o3, is a significant improvement in AI reasoning. It will be available to the public in early 2025, and many experts believe it could change how we use AI.
  2. The o3 model has shown it can solve complex tasks better than previous models. This includes performing well on math and coding benchmarks, marking a big step for AI.
  3. As the costs of using AI decrease, we can expect to see these models used more widely, impacting jobs and industries in ways we might not yet fully understand.

Authorization in microservice architecture, P3: The Policy Language

Hung's Notes • 39 implied HN points • 18 Jul 24
🕹 Technology Software Development
  1. A Domain-Specific Language (DSL) helps create clear and precise authorization policies for microservices. It makes it easier for everyone involved, from developers to managers, to understand authorization rules.
  2. The new policy language is designed to overcome performance issues by allowing lazy loading and efficient management of large datasets. This means it doesn't grab unnecessary data upfront, speeding up processes.
  3. Using YAML instead of complex formats makes the policies more readable and easier for non-engineers to understand. This helps ensure that more people can participate in and review authorization rules effectively.

Triplex — a SOTA LLM for Knowledge Graph Construction

Owen’s Substack • 59 implied HN points • 19 Jul 24
🕹 Technology Software Development
  1. Triplex is a new tool that helps create knowledge graphs quickly and cheaply. It's much cheaper to use than older methods, making it easier for more people to utilize.
  2. This tool is small enough to run on regular laptops, which means you don't need powerful computers to build knowledge graphs. This makes technology more accessible to everyone.
  3. Triplex is open-source, allowing anyone to use and improve it. The community can experiment with it freely and innovate new ways to organize and understand information.

Open Source Security Landscape 2024

Resilient Cyber • 139 implied HN points • 21 Apr 24
🕹 Technology Software Development
  1. Most codebases now use a lot of open source software, which can come with serious security risks. This means many systems are more vulnerable because they contain known vulnerabilities that might not be addressed.
  2. The number of components in applications is increasing, leading to software bloat. This makes it tough for teams to manage security and keep everything up to date, which can create more risks for users.
  3. Licensing issues are common in open source software, with many projects having conflicts or unclear licenses. This can lead to legal problems for businesses that use these components in their software.

OpenAI Codex 101: The Complete Guide to AI Coding Agents with 33 Ready-to-Use Prompts

The Product Channel By Sid Saladi • 6 implied HN points • 25 Feb 26
🕹 Technology Software Development
  1. Codex is an autonomous coding agent that can write, test, debug, refactor, and open pull requests, letting you delegate mechanical development work and speed up delivery.
  2. Effective use requires project tooling like AGENTS.md, reusable Skills, automations, and multi-agent worktrees across web, CLI, app, or IDE surfaces to keep work consistent and isolated.
  3. Choose tools by workflow: use Codex for fast, parallel delegation, scheduled automations, and GitHub-native reviews, use a reasoning-first agent for deep debugging, privacy, or huge context — or combine both for best results.

RAG Foundry By Intel

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 13 Aug 24
🕹 Technology Software Development
  1. RAG Foundry is an open-source framework that helps make the use of Retrieval-Augmented Generation systems easier. It brings together data creation, model training, and evaluation into one workflow.
  2. This framework allows for the fine-tuning of large language models like Llama-3 and Phi-3, improving their performance with better, task-specific data.
  3. There is a growing trend in using synthetic data for training models, which helps create tailored datasets that match specific needs or tasks better.

Multi-agent orchestration in Slack | Saleforce's Kurtis Kemple

Dev Interrupted • 9 implied HN points • 10 Feb 26
🕹 Technology Software Development
  1. Chat platforms are becoming agent orchestration hubs where humans and bots work together in real time, and organizations will need higher-level "super agents" to connect and manage isolated agent workflows.
  2. New agent ecosystems introduce fresh risks and human dependencies—agents forming their own social networks and services that hire people for tasks raise security, legal, and ethical concerns, and rogue or exploitable agent chains are a real threat.
  3. Widespread agent adoption will reshape how software is developed and how open source is consumed, shifting teams toward autonomous observe-orient-decide-act workflows and transforming open source projects to serve agent-driven use cases rather than disappearing.

Stop forcing AI tools on your engineers

Leading Developers • 267 implied HN points • 08 Jul 25
🕹 Technology Software Development
  1. Managers should not force engineers to use AI tools. This can create more pressure and lead to mistakes instead of improvements.
  2. It's important to give engineers time to explore and adopt AI tools at their own pace. Rushing them can hurt their performance.
  3. Companies should focus on the outcomes of work rather than just the tools used. The goal should be to serve customers better, not just to show off new technology.

Apache Iceberg Isn't Coming To Save You

SeattleDataGuy’s Newsletter • 341 implied HN points • 27 May 25
🕹 Technology Software Development
  1. Apache Iceberg might seem appealing, but it won't automatically solve your data problems. It's important to really understand what issues you're trying to address before jumping in.
  2. Switching to new tools like Iceberg won't fix a broken data strategy. The focus should be on delivering real business value, not just adopting the latest technology.
  3. If your data team is already doing well and looking to improve, Iceberg could be useful. But make sure it's the right fit for your specific challenges instead of following trends.