The hottest Software Development Substack posts right now

And their main takeaways
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Top Technology Topics

Object-Oriented Brain Rot

Burning the Midnight Coffee • 96 implied HN points • 31 Jan 25
🕹 Technology Software Development Object Oriented Programming Systems Engineering Software Design Computer Science
  1. When modeling objects like rectangles and squares, thinking too rigidly can lead to problems. Sometimes, it's simpler to just write a function to handle what you need rather than forcing everything into class hierarchies.
  2. Object-oriented programming can sometimes make things overly complicated. It's better to focus on solving the actual problem instead of worrying about fitting everything into a strict structure.
  3. Learning to think in terms of complex class hierarchies can actually harm your ability to solve problems. Simple, direct solutions are often more effective than trying to model everything in a complicated way.

We need to talk about AI

Unreported Truths • 29 implied HN points • 30 May 25
🕹 Technology AI Ethics Software Development Future Trends Automation Innovation
  1. Many people believe AI will change our world quickly, but it's hard to know how true that is. People have different opinions and experiences with AI.
  2. AI can do some tasks well, like coding and answering questions, but it often lacks creativity and originality. It mimics emotions but doesn't really challenge users.
  3. The future of AI is uncertain, and it's important to hear from others about their views and experiences with it. There may be real risks or benefits ahead.

What hath AWS wrought?

Cloud Irregular • 2661 implied HN points • 10 Dec 24
🕹 Technology Cloud Computing Software Development DevOps Infrastructure
  1. At this year's AWS re:Invent, there were no major new services launched, which is quite different from previous years. Instead, AWS focused on enhancing existing services and features.
  2. In the past, AWS released many new services, but many of them didn't succeed. This led to dissatisfaction within the developer community.
  3. Now, AWS seems to be concentrating on improving their core offerings. This change could help revive interest and excitement in the AWS developer community again.

How Did LinkedIn Handle 7 Trillion Messages Daily With Apache Kafka?

VuTrinh. • 299 implied HN points • 13 Aug 24
🕹 Technology Data Engineering Infrastructure Software Development Open Source
  1. LinkedIn uses Apache Kafka to manage a massive flow of information, handling around 7 trillion messages every day. They set up a complex system of clusters and brokers to ensure everything runs smoothly.
  2. To keep everything organized, LinkedIn has a tiered system where data is processed locally in each data center, then sent to an aggregate cluster. This helps them avoid issues from moving data across different locations.
  3. LinkedIn has an auditing tool to make sure all messages are tracked and nothing gets lost during transmission. This helps them quickly identify any problems and fix them efficiently.

Historically, 4NF explanations are needlessly confusing

Minimal Modeling • 608 implied HN points • 05 Dec 24
🕹 Technology Database Design Data Structures Software Development Information Systems
  1. Fourth Normal Form (4NF) is mainly about creating simple two-column tables to link related data, like teachers and their skills. This straightforward design is often overlooked in favor of complex definitions.
  2. Many explanations of 4NF start with confusing three-column tables and then break them down into simpler forms. This approach makes it harder for learners to grasp the concept quickly and effectively.
  3. The term 'multivalued dependency' can be simplified to just mean a list of unique IDs. You don’t really need to focus on this term to design good database tables; it's more of a historical detail.
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A Visual Guide to Mamba and State Space Models

Exploring Language Models • 3942 implied HN points • 19 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Algorithm Design
  1. Mamba is a new modeling technique that aims to improve language processing by using state space models instead of the traditional transformer approach. It focuses on keeping essential information while being efficient in handling sequences.
  2. Unlike transformers, Mamba allows for selective attention, meaning it can choose which parts of the input to focus on. This makes it potentially better at understanding context and relevant information.
  3. The architecture of Mamba is designed to be hardware-friendly, helping it to perform well without excessive resource use. It uses techniques like kernel fusion and recomputation to optimize speed and memory use.

My Wronggest Content

Jacob’s Tech Tavern • 656 implied HN points • 26 Nov 24
🕹 Technology Social media Software Development Marketing Content creation Programming
  1. Posting wrong code online can lead to getting helpful feedback from others. Sometimes people are quick to point out mistakes, but that can help you learn.
  2. Using social media regularly can grow your audience. Posting interesting and engaging content helps attract more subscribers.
  3. Accepting criticism is important. It can be tough to hear people say your work is bad, but it's a chance to improve and grow.

Update re: Microsoft and training data

Marcus on AI • 2766 implied HN points • 26 Nov 24
🕹 Technology Artificial Intelligence Data Privacy Machine Learning Software Development Corporate Communication
  1. Microsoft claims they don't use customer data from their applications to train AI, but it's not very clear how that works.
  2. There is confusion around the Connected Services feature, which says it analyzes data but doesn't explain how that affects AI training.
  3. People want more clear answers from Microsoft about data usage, but there hasn't been a detailed response from the company yet.

"Don't talk to strangers" also applies to software development.

System Design Classroom • 499 implied HN points • 19 Jul 24
🕹 Technology Software Development System Design Programming Software Engineering Architecture
  1. Loose coupling is important in software. It means different parts of a program should depend on each other as little as possible, making it easier to change and fix things.
  2. The Law of Demeter suggests that objects should only talk to their direct friends and not reach out too far. This helps to keep dependencies low and makes code more manageable.
  3. Using strategies like the Single Responsibility Principle, interfaces, and dependency injection can improve your code's structure. This makes modules clear, easy to test, and maintain.

LLMs Fight With Both Hands Tied Behind Their Back

Am I Stronger Yet? • 313 implied HN points • 27 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Programming Data science Software Development
  1. Large Language Models (LLMs) like o3 are becoming better at solving complex math and coding problems, showing impressive performance compared to human competitors. They can tackle hard tasks with many attempts, which is different from how humans might solve them.
  2. Despite their advances, LLMs struggle with tasks that require visual reasoning or creativity. They often fail to understand spatial relationships in images because they process information in a linear way, making it hard to work with visual puzzles.
  3. LLMs rely heavily on knowledge in their 'heads' and do not have access to real-world knowledge. When they gain access to more external tools, their performance could improve significantly, potentially changing how they solve various problems.

Can LLMs earn $1M freelancing?

HackerPulse Dispatch • 5 implied HN points • 21 Feb 25
🕹 Technology AI Machine Learning Software Development Multimodal models Freelancing
  1. AI models are being tested to see if they can earn a million dollars through freelancing. But it turns out many of them struggle with real-world tasks.
  2. A new video model can create high-quality videos from text descriptions. It uses advanced techniques to improve video quality and generation.
  3. Small AI models can perform better when they are trained on easier tasks instead of trying to learn from more complex ones.

Measuring Programmer Influence, Kinda Sorta

Software Design: Tidy First? • 1347 implied HN points • 27 Jan 25
🕹 Technology Software Development Data Analysis Programming Project management
  1. Data can provide hints about a programmer's influence, but it can't give a clear answer. It's important to interpret the data with caution and avoid making strict decisions based solely on it.
  2. Creating files is one way to measure initiation of influence, but it's not the only factor. The impact is also determined by how frequently those files are modified by others.
  3. Using data for bonuses or promotions can lead to problems. It's better to focus on improvement and impact rather than just the numbers, to maintain a healthy team dynamic.

Bugs Optional?

Software Design: Tidy First? • 1723 implied HN points • 03 Jan 25
🕹 Technology Software Development Programming Engineering Quality Assurance Project management
  1. Bugs don't have to be a normal part of software development. Some teams manage to almost eliminate bugs by approaching their work differently.
  2. Instead of seeing bugs as inevitable, teams can work to understand and prevent them right from the start. This includes practices like continuous integration and team collaboration.
  3. Changing how we think about bugs from a normal part of life to something rare can help create a better work environment and improve software quality.

Multi-robot collaboration,Grok 3 , smallest video language model, Generative AI Model for Gameplay, AI co-scientist, Mistral Saba, Fiverr Go, Step-Video-T2V and Step-Audio, Pikaswaps & more

AI Brews • 15 implied HN points • 21 Feb 25
🕹 Technology Artificial Intelligence Robotics Machine Learning Software Development Open Source
  1. Grok 3 is a powerful reasoning model that can handle a massive amount of information at once, making it one of the best tools for chatbots right now.
  2. New advancements in AI, like the Vision-Language-Action model Helix and the generative AI model Muse, are making robots smarter and more capable in their tasks.
  3. AI tools are getting more user-friendly, such as Pikaswaps, which allows you to easily replace parts of videos with your own images, making editing simpler for everyone.

BLT: Byte Latent Transformer

Gonzo ML • 315 implied HN points • 23 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Data processing Software Development Innovation
  1. The Byte Latent Transformer (BLT) uses patches instead of tokens, allowing it to adapt based on the complexity of the input. This means it can process simpler inputs more efficiently and allocate more resources to complex ones.
  2. BLT can accurately encode text at a byte level, overcoming issues with traditional tokenization that often lead to mistakes in understanding languages and simple tasks like counting letters.
  3. BLT architecture has shown better performance than older models, handling tasks like translation and sequence manipulation more effectively. This advancement could improve the application of language models across different languages and reduce errors.

Apache Kafka - Overview

VuTrinh. • 539 implied HN points • 06 Jul 24
🕹 Technology Data Engineering Software Development Systems Architecture Distributed Systems
  1. Apache Kafka is a system for handling large amounts of data messages, making it easier for companies like LinkedIn to manage and analyze user activity and other important metrics.
  2. In Kafka, messages are organized into topics and divided into partitions, allowing for better performance and scalability. This way, different servers can handle parts of the data at once.
  3. Kafka uses a pull model for consumers, meaning they can request data as they need it. This helps prevent overwhelming the consumers with too much data at once.

Stuff Worth Reading - Sep 2024

clkao@substack • 79 implied HN points • 30 Sep 24
🕹 Technology Software Development Version Control Analytics Pricing Strategy
  1. GitHub succeeded because it created tools that developers really wanted and used. The combination of Git's technical features and GitHub's social features made it very popular.
  2. The analytics and data workflow still lag behind traditional development methods. It's important to find better ways to show the value of data to businesses.
  3. There's a new way to think about pricing that considers what buyers really want, not just traditional methods. This can lead to smarter pricing strategies.

The CTO’s Hidden Notebook

The CTO Substack • 339 implied HN points • 26 Jul 24
🕹 Technology Productivity Tools Note Taking Knowledge Management Software Development Personal Development
  1. Taking notes is about more than just gathering information. It's about building your own understanding and knowledge over time.
  2. Using a structured method, like the Zettelkasten system, can help you organize your thoughts and learn more effectively.
  3. Writing regularly about what you learn can change how you approach your work and meetings, making them opportunities for growth.

GenAI Isn’t Replacing Devs (And It Shouldn’t)

HackerPulse Dispatch • 8 implied HN points • 18 Feb 25
🕹 Technology Software Development Artificial Intelligence Programming Languages Engineering Tech industry
  1. Firing programmers to replace them with AI can backfire. Companies might end up facing big problems like untrained workers and high costs to hire good developers back.
  2. Experience and human intuition are important in software development. AI can't solve every problem, and skilled developers are still needed for complex tasks.
  3. The new Python 3.14 interpreter will make code run faster without needing any changes. This is great for developers because it saves time and effort.

Report: OpenAI Spends Millions a Year Miscounting the R's in 'Strawberry'

The Algorithmic Bridge • 573 implied HN points • 22 Nov 24
🕹 Technology Artificial Intelligence Machine Learning Tech Ethics Data science Software Development
  1. OpenAI has spent a lot of money trying to fix an issue with counting the letter R in the word 'strawberry.' This problem has caused a lot of confusion among users.
  2. The CEO of OpenAI thinks the problem is silly but feels it's important to address because users are concerned. They are also looking into redesigning how their models handle letter counting.
  3. Some employees joked about extreme solutions like eliminating red fruits to avoid the R issue. They are also thinking of patches to improve letter counting, but it's clear they have more work to do.

Practical Data Engineering using AWS Cloud Technologies

VuTrinh. • 339 implied HN points • 23 Jul 24
🕹 Technology Cloud Computing Data Engineering Software Development Information Systems
  1. AWS offers a variety of tools for data engineering like S3, Lambda, and Step Functions, which can help anyone build scalable projects. These tools are often underused compared to newer options but are still very effective.
  2. Services like SNS and SQS can help manage data flow and processing. SNS allows for publishing messages while SQS aids in handling high event volumes asynchronously.
  3. Using AWS for data engineering is often simpler than switching to modern tools. It's easier to add new AWS services to your existing workflow than to migrate to something completely new.

GPTs Are Maxed Out

The Algorithmic Bridge • 647 implied HN points • 11 Nov 24
🕹 Technology AI Computing Machine Learning Data science Software Development
  1. AI companies are hitting limits with current models. Simply making AI bigger isn't creating better results like it used to.
  2. The upcoming models, like Orion, may not meet the high expectations set by previous versions. Users want more dramatic improvements and are getting frustrated.
  3. A new approach in AI may focus on real-time thinking, allowing models to give better answers by taking a bit more time, though this could test users' patience.

Data Science Weekly - Issue 559

Data Science Weekly Newsletter • 219 implied HN points • 08 Aug 24
🕹 Technology Data science AI Machine Learning Software Development Statistics
  1. Camera calibration is crucial in sports analysis. It helps track players' movements accurately by mapping video frame positions to real field locations.
  2. Understanding the context of data is important for responsible data work. Datasets need good documentation and stories to highlight their historical and social backgrounds.
  3. There's a new, free encyclopedia for learning about cognitive science. It offers easy-to-read articles on various topics for students and researchers.

In The Context Of Long Context

Adjacent Possible • 553 implied HN points • 21 Nov 24
🕹 Technology AI Machine Learning Software Development Innovation Digital Media
  1. A new AI feature can turn a whole book into a fun audio conversation, making learning more engaging. This feature has caught a lot of attention online and even received media coverage.
  2. The ability of the AI to handle large amounts of text—up to 1.5 million words—makes it much more useful for users, allowing for better, more detailed interactions.
  3. Long context models can help organizations make better decisions by recalling important documents and past experiences, adding a new kind of intelligence to team discussions.

Push it to the Limit EP. 1—GPT Engineer

Prompt’s Substack • 119 implied HN points • 25 Aug 24
🕹 Technology AI Tools Software Development Generative AI
  1. Using GPT Engineer can help generate clean front-end React code quickly, even for those with minimal coding knowledge. It's impressive how much can be done with just prompts.
  2. Integrating a Supabase database with GPT Engineer is easy for simple cases, but it can become complex with larger databases due to relationship intricacies.
  3. Creativity in prompting is key when working with bigger databases, as GPT Engineer has some limitations with context as databases grow in complexity.

Normalization is not enough anymore.

System Design Classroom • 559 implied HN points • 23 Jun 24
🕹 Technology Database Design Data processing Software Development Performance optimization Information Systems
  1. Normalization is important for organizing data and reducing redundancy, but it's not sufficient for today's data needs. We have to think beyond just following those strict rules.
  2. De-normalization can help improve performance by reducing complex joins in large datasets. Sometimes, it makes sense to duplicate data to make queries run faster.
  3. Knowing when to de-normalize is key, especially in situations like data warehousing or when read performance matters more than write performance. It's all about balancing speed and data integrity.

Resilient Cyber Newsletter #12

Resilient Cyber • 79 implied HN points • 03 Sep 24
🕹 Technology Cybersecurity AI Security Venture Capital Software Development Data Privacy
  1. Many companies believe they are prepared for cyber threats, but actually, most lack strong leadership involvement in their cybersecurity efforts. That's making them more vulnerable.
  2. Despite spending a lot on security solutions, many enterprises still face breaches, showing that having many tools doesn't always mean better protection.
  3. There's a debate about how founders should manage their startups. Some say founding leaders need to be hands-on rather than relying on traditional management styles that don’t always work for fast-growing companies.

Is It Time to Say Goodbye to Data Engineers?

SeattleDataGuy’s Newsletter • 812 implied HN points • 06 Feb 25
🕹 Technology Data Engineering Software Development Data Management Business Intelligence Analytics
  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.

Launching Live Courses on Systems Programming

Confessions of a Code Addict • 1058 implied HN points • 25 Jan 25
🕹 Technology Software Development Systems Engineering Education Programming Course design
  1. There is a growing gap between complex systems in software and the engineers who understand them. More engineers need to learn how these systems work in detail.
  2. The new live courses will help those interested in systems engineering to gain practical skills. They'll start with basics like programming in X86 assembly and progress to more complex topics.
  3. Hands-on practice is key to learning in these courses. Along with guidance, you'll need to put in effort and time to really understand the concepts.

Why do programmers need private offices with doors?

Shenisha’s Substack • 5 HN points • 02 Oct 24
🕹 Technology Work Environment Productivity Software Development Team Dynamics
  1. Programmers often need private offices to focus better on their work. Short interruptions can really disrupt their thought processes and lower their productivity.
  2. There are two types of work: those that can be interrupted easily and those that cannot. Knowing the difference helps in managing how we communicate in the workplace.
  3. Leaders should protect their team's focus time and understand the value of uninterrupted work. This can lead to greater creativity and better results.

Managing Expand Projects

Software Design: Tidy First? • 1193 implied HN points • 02 Jan 25
💼 Business Project management Software Development Risk management Growth Strategies
  1. In a phase of rapid growth, problems can emerge suddenly, and it's crucial to focus on quick fixes instead of getting bogged down in perfect plans. This might mean using basic solutions to keep things running.
  2. When facing high demand and limited resources, the goal is to delay or prevent resource shortages. This can involve spending more money or reducing the growth rate to manage resources better.
  3. It's important to stay calm and creative during crises. Experimenting with new ideas in small, parallel teams can help find solutions quickly, which is necessary to continue growing without causing irreversible problems.

The ghosts that live in my garage

Basta’s Notes • 122 implied HN points • 13 Jan 25
🕹 Technology Machine Learning Artificial Intelligence Self-driving cars Data science Software Development
  1. Machine learning models are good at spotting patterns that humans might miss. This means they can make predictions and organize data in ways that are impressive and often very useful.
  2. However, machine learning can struggle with unclear or messy data. This fuzziness can lead to mistakes, like misidentifying objects or giving unexpected results.
  3. Not every problem needs a machine learning solution, and sometimes simpler methods work better and are more effective. It's important to think carefully about whether machine learning is truly the best tool for the job.

Additional Testing After Refactoring

Software Design: Tidy First? • 950 implied HN points • 20 Jan 25
🕹 Technology Software Development Testing Refactoring Design Patterns Programming
  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.

Transformer^2: Self-adaptive LLMs

Gonzo ML • 63 implied HN points • 27 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data science Computing Software Development
  1. Transformer^2 uses a new method for adapting language models that makes it simpler and more efficient than fine-tuning. Instead of retraining the whole model, it adjusts specific parts, which saves time and resources.
  2. The approach breaks down weight matrices through a process called Singular Value Decomposition (SVD), allowing the model to identify and enhance its existing strengths for various tasks.
  3. At test time, Transformer^2 can adapt to new tasks in two passes, first assessing the situation and then applying the best adjustments. This method shows improvements over existing techniques like LoRA in both performance and parameter efficiency.

The biggest time sink for engineering teams: testing

Dev Interrupted • 37 implied HN points • 05 Jun 25
🕹 Technology Software Development Quality Assurance AI Integration Automation
  1. Testing is often the biggest delay for engineering teams, slowing down new feature releases.
  2. AI-powered testing tools can automate repetitive tasks, allowing QA teams to work more efficiently and focus on strategy.
  3. The role of QA professionals is shifting towards design and analysis, rather than just executing tests, as automation takes over routine tasks.

The Product Development Triathlon

Software Design: Tidy First? • 1436 implied HN points • 06 Dec 24
🕹 Technology Software Development Product Design Innovation Project management User Experience
  1. Product development happens in three main phases: Explore, Expand, and Extract. Each part has its own challenges and ways to tackle them.
  2. You need different skills and tools for each phase. Trying to use expansion tools in exploration will slow you down.
  3. It's important to notice when you're transitioning between phases. Adapting quickly helps keep the project on track.

Data Science Weekly - Issue 560

Data Science Weekly Newsletter • 139 implied HN points • 15 Aug 24
🕹 Technology Data science AI Machine Learning Software Development Programming
  1. The Turing Test raises questions about what it means for a computer to think, suggesting that if a computer behaves like a human, we might consider it intelligent too.
  2. Creating a multimodal language model involves understanding different components like transformers, attention mechanisms, and learning techniques, which are essential for advanced AI systems.
  3. A recent study tested if astrologers can really analyze people's lives using astrology, addressing the ongoing debate about the legitimacy of astrology among the public.

What Did You Think Getting Closer to AGI Would Be Like?

The Algorithmic Bridge • 318 implied HN points • 07 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Software Development Computing Data science
  1. OpenAI's new model, o1, is not AGI; it's just another step in AI development that might not lead us closer to true general intelligence.
  2. AGI should have consistent intelligence across tasks, unlike current AI, which can sometimes perform poorly on simple tasks and excel on complex ones.
  3. As we approach AGI, we might feel smaller or less significant, reflecting how humans will react to advanced AI like o1, even if it isn’t AGI itself.

OpenAI Announces o1 Model And ChatGPT Pro ($200/Mo)

The Algorithmic Bridge • 329 implied HN points • 05 Dec 24
🕹 Technology AI Models Machine Learning Software Development Data science Innovation
  1. OpenAI has launched a new AI model called o1, which is designed to think and reason better than previous models. It can now solve questions more accurately and is faster at responding to simpler problems.
  2. ChatGPT Pro is a new subscription tier that costs $200 a month. It provides unlimited access to advanced models and special features, although it might not be worth it for average users.
  3. o1 is not just focused on math and coding; it's also designed for everyday tasks like writing. OpenAI claims it's safer and more compliant with their policies than earlier models.