The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

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

The Counterfactual • 599 implied HN points • 28 Jul 23
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Processing 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 AI Machine Learning Open Source Software Development Data science
  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.

Why Code Authors Should Have the Final Say on Code Reviews

Rethinking Software • 249 implied HN points • 27 Oct 24
🕹 Technology Software Development Code Review Agile methodologies Team Dynamics
  1. Code authors should have the final say in reviews to respect their expertise and autonomy. This helps them feel like true professionals.
  2. Mistakes in code are common and can be fixed quickly, so allowing authors to make decisions helps them learn and improve.
  3. Not all code needs to be perfect from the start, especially in the early stages of projects. Giving authors the control lets them decide how polished their work should be.

Microsoft builds the bomb

benn.substack • 1508 implied HN points • 26 May 23
🕹 Technology Data Management Cloud Computing Software Development Artificial Intelligence
  1. The modern data stack aimed to revolutionize how technology is built and sold, focusing on modularity and specialized tools.
  2. Microsoft introduced Fabric as an all-in-one data and analytics platform to address the issue of fragmentation in the modern data stack.
  3. Fabric from Microsoft presents a unified solution but may risk limiting choice and innovation in the data industry.
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Big Tech and Generative AI Q3 '24 Update

Tanay’s Newsletter • 63 implied HN points • 04 Nov 24
🕹 Technology AI Cloud Computing Data Analysis Software Development Tech Innovation
  1. Amazon is making big strides in AI by providing tools for developers and creating custom chips. They are seeing huge interest in their AI services, which are growing fast despite lower profit margins.
  2. Google is using AI to improve its search capabilities and has rolled out new features to enhance user experience. Their AI models, called Gemini, are being adopted widely across their products and they are investing significantly in infrastructure.
  3. Apple has launched its AI system, Apple Intelligence, focusing on privacy and enhancing the user experience of their products. Although they're investing in AI, their spending is still lower compared to competitors, but they plan to increase their efforts.

Authorization in microservice architecture, P3: The Policy Language

Hung's Notes • 39 implied HN points • 18 Jul 24
🕹 Technology Software Development Cybersecurity Data Management Programming Languages Microservices
  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.

An AWS For Sequencing?

ASeq Newsletter • 58 implied HN points • 16 Nov 24
🕹 Technology Bioinformatics Data Analysis Cloud Computing Software Development Genetics
  1. Bioinformatics companies often struggle to succeed on their own, but some are finding unique ways to add value by providing analysis of sequencing data from external service providers.
  2. Just like how companies can use AWS for their server needs, the idea is to create an AWS-like platform specifically for DNA sequencing, making services easier and more accessible.
  3. Building a platform for sequencing could lower barriers for businesses and encourage new applications in the field, opening up more opportunities for innovation.

Triplex — a SOTA LLM for Knowledge Graph Construction

Owen’s Substack • 59 implied HN points • 19 Jul 24
🕹 Technology AI Machine Learning Data science Open Source 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 Cybersecurity Open Source Software Development Risk management
  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.

How the CIA Writes Python

Luminotes • 28 implied HN points • 15 Dec 24
🕹 Technology Programming Software Development Cybersecurity Data science
  1. The CIA has a unique Python style guide, focusing on clarity and readability, with special rules for exceptions, globals, and list comprehensions.
  2. They use specific tools like PyCharm for development and have a custom setup for installing Python and managing packages within secure environments.
  3. There are no strict rules governing coding practices; instead, individuals make choices based on their preferences and the limitations of their working conditions.

RAG Foundry By Intel

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 13 Aug 24
🕹 Technology Artificial Intelligence Software Development Open Source Data science Machine Learning
  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.

eBook: Mastering AI Agents

TheSequence • 77 implied HN points • 07 Feb 25
🕹 Technology AI agents Machine Learning Automation Software Development Data Analysis
  1. You can learn to create effective AI agents with the right guidance. There's a helpful eBook that covers how these agents work and when to use them.
  2. The book reviews three frameworks for developing AI agents, helping you choose what's best for your needs. It also shares case studies to show real-life applications.
  3. It addresses common reasons AI agents fail and provides solutions to avoid these problems. This can help ensure your AI projects succeed.

When LLMs Made Everyone a Coder

ScaleDown • 22 implied HN points • 29 Dec 24
🕹 Technology AI Tools Coding Software Development Product Management Tech Trends
  1. Using AI to write code can be misleading. Just because the code looks good doesn't mean it works; real coding requires understanding the logic behind it.
  2. Simple apps can be more effective than complex ones built with AI. Breaking tasks into manageable steps is key to successful programming.
  3. AI tools are helpful but shouldn't replace engineers. Someone needs to check and fix the code generated by AI, making engineers still very important.

Documentation-driven API Design

The API Changelog • 6 implied HN points • 24 Jan 25
🕹 Technology API design AI Tools Documentation Software Development
  1. You can create an API by simply writing down what you want it to do, and AI can help turn that into a working API document. It's as easy as writing a description and letting the technology handle the rest.
  2. Using AI tools like ChatGPT, you can get detailed how-to guides for your API based on a simple description, making it easier to understand how to use it.
  3. By generating an OpenAPI document from your description, you can quickly set up a mock API server, allowing you to test and get feedback on your API design in no time.

The Sequence Knowledge #482: An Introduction to Corrective RAG

TheSequence • 77 implied HN points • 04 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Information Systems
  1. Corrective RAG is a smarter way of using AI that makes it more accurate by checking its work. It helps prevent mistakes or errors in the information it gives.
  2. This method goes beyond basic retrieval-augmented generation (RAG) by adding feedback loops that refine and improve the output as it learns.
  3. The goal of Corrective RAG is to provide answers that are factually accurate and coherent, reducing confusion or incorrect information.

The AI Shift: Preparing for What’s Next

Dev Interrupted • 32 implied HN points • 05 Dec 24
🕹 Technology AI Tools Software Development Workflows Talent Acquisition Code Quality
  1. AI tools can help developers work faster, but they need to be careful about the quality of the code. It's important for developers to review what AI produces to ensure it meets necessary standards.
  2. AI is a permanent part of software development, but it has its flaws. Many AI-generated codes can be incorrect, so developers should set up proper checks to keep the software secure and reliable.
  3. To prevent burnout and improve productivity, developers should focus on important projects and let automation tools help with code reviews. Changing hiring practices can also help bring in fresh talent and support better workflows.

The Rise Of Application Security Posture Management (ASPM) Platforms

Resilient Cyber • 119 implied HN points • 25 Apr 24
🕹 Technology Vulnerability Management Software Development
  1. Application security is becoming more complicated as software development grows, making it hard for teams to keep track of security issues. It's important for teams to have a clear view of application security to effectively manage vulnerabilities.
  2. ASPM platforms are designed to help organizations manage application security more efficiently by combining tools and workflows. They enable teams to see security risks clearly and respond quickly to issues without overwhelming them with alerts.
  3. The integration of security into the development process, known as DevSecOps, aims to reduce vulnerabilities and improve collaboration among teams. With ASPM, businesses can connect security efforts across different stages of software development for better protection.

My Journey into Personal Computer Software Development in 1983

Farrs’s Substack • 125 HN points • 20 Apr 24
🕹 Technology Software Development Programming Management Bug Fixing
  1. Personal Computers were gaining popularity in 1983, despite being considered toys by some programmers, and had promising applications developed for them.
  2. Taking a risk to work in Personal Computer Software Development led to a successful job offer and opportunity to solve a challenging memory limitation issue.
  3. Facing skepticism and disrespect at the company, the individual showcased exceptional bug-solving abilities, but ultimately chose to leave due to being labeled unfairly.

A metrics layer is like your New Year's resolution…

HyperArc • 39 implied HN points • 11 Jul 24
🕹 Technology Data Analytics Software Development Artificial Intelligence Business Intelligence Automation
  1. A metrics layer helps standardize how companies measure data, making it easier for everyone to understand what is important. It can automate calculations, like rolling averages, which saves time and reduces confusion.
  2. Traditional business intelligence tools often lose useful underlying information, which makes it hard to understand how certain metrics were created. More context is needed to ensure decisions are well-informed and based on complete data.
  3. HyperArc offers a solution by capturing the team's insights and reasoning during analysis. It helps keep track of not just the final metrics, but also the thought process behind them, making it easier to revisit and understand decisions in the future.

sqlmesh cube_generate build part 2

davidj.substack • 23 implied HN points • 18 Dec 24
🕹 Technology Data Models Command-line Software Development
  1. The main goal is to create a command that generates metadata to build a semantic layer for SQL models. This is important because it helps in understanding the structure and relationships within the data.
  2. AI can enhance the process by taking the generated metadata and improving it for better usability. Using tools like OpenAI can make the process easier and faster.
  3. There's an ongoing focus on creating practical solutions rather than aiming for perfection. It's okay to make adjustments and improvements along the way as you learn what works best.

Data Science Weekly - Issue 523

Data Science Weekly Newsletter • 339 implied HN points • 01 Dec 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analysis Software Development
  1. Data science is evolving quickly, and it's important to stay updated with new advances and tools. Courses and reading lists can help you catch up and enhance your skills.
  2. Using machine learning to solve real-world problems, like correctly attributing quotes, shows the practical applications of data science. Collaboration between universities and organizations can lead to innovative solutions.
  3. The job market for data scientists is challenging right now. Many applicants are competing for limited positions, so if you're looking for a job, patience is key.

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

Resilient Cyber • 119 implied HN points • 16 Apr 24
🕹 Technology Cybersecurity Software Engineering Data Protection Software Development
  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.

Rethinking Problem-Solving: From Space Pens to Pencils and Beyond

Wisdom over Waves • 79 implied HN points • 21 May 24
🕹 Technology Software Development Problem Solving Innovation User Experience
  1. Focus on the problem first: Understand the core issue before jumping into solutions. This can lead to more innovative and effective outcomes.
  2. Avoid getting lost in the technical details: Developers should balance focusing on implementation with considering broader business needs and goals.
  3. Collaborate and empathize: Work closely with other teams, seek feedback, and put yourself in the shoes of the end user to improve problem-solving and innovation.

GroupBy #39: 2000+ DBT models in airflow; Serverless Jupyter Notebooks at Meta

VuTrinh. • 59 implied HN points • 11 Jun 24
🕹 Technology Data Engineering Software Development Cloud Computing Analytics Data science
  1. Meta has developed a serverless Jupyter Notebook platform that runs directly in web browsers, making data analysis more accessible.
  2. Airflow is being used to manage over 2000 DBT models, which helps teams create and maintain their own data models effectively.
  3. Building a data platform from scratch can be a valuable learning experience, revealing important lessons about data structure and management.

Embedded Online Conference 2024: My Summary

burkhardstubert • 79 implied HN points • 20 May 24
🕹 Technology Software Development Embedded Systems Artificial Intelligence Programming Languages
  1. Using a top-down approach in software development helps avoid costly mistakes by getting early feedback from customers. It also reduces the blame on software developers when hardware is late.
  2. AI and machine learning can greatly boost productivity in embedded systems by automating repetitive tasks. They can help with coding, documentation, and even testing, making development smoother.
  3. Integrating open source components into embedded systems needs thorough safety analysis. A system bill of materials (SysBoM) helps track interactions and dependencies, ensuring safety and reliability.

Breach simulation is a complicated market

Frankly Speaking • 50 implied HN points • 01 Nov 24
🕹 Technology Cybersecurity Software Development Product Management Market Analysis User Experience
  1. The breach simulation market is confusing because companies market their products in different ways. It's hard to understand exactly what these tools are supposed to solve for security teams.
  2. Turning security services into products is challenging. Many customers prefer high-quality services rather than automated tools because they believe they catch more sophisticated attacks.
  3. For these simulation tools to succeed, they need to show clear benefits to businesses, like saving money or preventing incidents. Right now, many organizations view them as nice-to-have rather than essential.

Ergodic Development

Software Design: Tidy First? • 883 implied HN points • 25 Aug 23
🕹 Technology Software Development Team Collaboration Risk management Innovation
  1. Ergodicity reminds us to treat systems that continue as is differently from those that fail when changed.
  2. Strategies like reducing irreversibility and having skin in the game can help transform failing systems into sustaining ones.
  3. Load redistribution and encouraging collaboration can make development more survivable and sustainable.

How to code for the future

CodeFaster • 36 implied HN points • 19 Nov 24
🕹 Technology Software Development Coding Practices Technical Debt System Design
  1. When coding for the future, it's important not to create more work for yourself later. Focus on avoiding technical debt instead of trying to predict every future need.
  2. Don't go overboard with coding. Keep your code simple and flexible, ensuring it can adapt to changes without adding extra complexity.
  3. Instead of trying to build reusable programs from the start, solve the immediate problem first. You can refactor and create reusable parts later if needed.

Last Call for Quality

QUALITY BOSS • 39 implied HN points • 03 Jul 24
🕹 Technology Software Development Quality Assurance Development Practices DevOps
  1. Testing software too late can lead to more expensive and difficult fixes. It's better to catch bugs earlier in the development process.
  2. Many teams rely too much on manual testing, which can slow things down. A mix of automated and manual testing can improve quality and efficiency.
  3. Ignoring non-functional requirements like security and performance can make software unsatisfactory, even if it meets basic needs. It's important to include these factors in testing plans.

Digging into the OWASP AI Exchange

Resilient Cyber • 239 implied HN points • 10 Jan 24
🕹 Technology AI Security Cybersecurity Open Source Software Development Risk management
  1. OWASP AI Exchange is a valuable resource for understanding AI security risks and sharing knowledge. It helps organizations learn how to protect themselves against threats in AI systems.
  2. The AI Exchange provides guidelines for managing AI security throughout its development and use. Companies can adopt controls to mitigate risks associated with data leaks, manipulation, and insecure outputs.
  3. Practitioners are advised to incorporate standard security practices from app security into AI systems. Regular monitoring and using tools like threat modeling are essential for maintaining safety in AI usage.

In the land of LLMs, can we do better mock data generation?

Neurelo Engineering’s Substack • 1 HN point • 27 Sep 24
🕹 Technology Software Development Data science Programming Languages Artificial Intelligence Machine Learning
  1. Mock data is super useful for testing software, but it hasn't really improved much over the years. It needs to be more flexible and easier to generate high-quality data.
  2. Using LLMs (large language models) can be tricky for creating mock data. Instead of trying to generate everything, it’s often better to use techniques like topological sorting to keep relationships correct between data entries.
  3. A new approach is turning to strategies like the Genesis Point Strategy, which helps create unique mock data efficiently. It shows that you can simplify processes to get good results without overcomplicating things.

Fighting over Crumbs

Rethinking Software • 99 implied HN points • 30 Dec 24
🕹 Technology Software Development Project management Team Dynamics Organizational Behavior Code Review
  1. Many programmers feel like they have no control over their work, which can lead to unhealthy competition for the little power that exists. Instead of fighting for crumbs, they should focus on shared decision-making.
  2. Behaviors like land grabbing and excessive code reviews show that programmers crave autonomy but don't know how to get it responsibly. They need to find better ways to collaborate and share power, rather than hoarding it.
  3. Team leads and committees often create more bureaucracy and slow things down. Programmers should work more as peers, trust each other, and let go of the need for strict control to improve their work environment.

Console #191 -- Interview with Bernhard of ACID Chess - Chess computer for nerds, by nerds

Console • 472 implied HN points • 07 Jan 24
🕹 Technology Open Source Programming AI Software Development Neural Networks
  1. ACID Chess is a chess computer program written in Python that can analyze the movements of pieces on a chessboard through image recognition.
  2. The creator of ACID Chess balanced working on the project with a full-time job by dedicating time in evenings and weekends while finding it to be a good balance.
  3. The creator of ACID Chess believes AI will simplify various aspects of software development, and open-source software will continue to thrive with challenges in monetization for small developers.

A Pain in the Plate Maps

Briefly Bio • 198 implied HN points • 23 Feb 24
🕹 Technology Biotechnology Data science Software Development Research Methodology
  1. Creating 96-well plate maps is important for organizing samples and tracking metadata during scientific experiments. This helps scientists during pipetting and later data analysis.
  2. Current methods for making plate maps, like using spreadsheets, can be clunky and error-prone as they often require managing multiple tables that are not linked.
  3. A new visual plate mapper allows for easy creation and editing of plate maps. It synchronizes the visual layout with a data table, making it simpler to manage and analyze experiment data.

No sacred masterpieces

Basta’s Notes • 753 HN points • 15 Sep 23
🕹 Technology Software Development Data science Engineering Project management Web Development
  1. Sometimes, valuable projects end abruptly without much recognition or lasting impact.
  2. It's important to focus on creating business value with your work, rather than building impressive but ultimately unnecessary solutions.
  3. Every piece of code you write as an engineer is legacy and may not last forever, so focus on learning from each project's outcome.