The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

Weekly Notes

Rethinking Software • 149 implied HN points • 23 Sep 24
🕹 Technology Software Development Agile Methodology Product Management Team Dynamics Data Analysis
  1. Story points are basically just hidden time estimates for tasks in software development. Understanding this can help with better planning and predicting when a project will be finished.
  2. Product management should be like a party host, making sure developers and customers communicate and enjoy their time together. This creates a better experience for everyone involved.
  3. There are ways for companies to run without traditional management, like the tomato processor Morning Star. This might be a model to explore for improving the software industry's workflow.

The Importance Of Granular Data Design For Fine-Tuning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 02 May 24
🕹 Technology AI Data science Machine Learning Natural Language Processing Software Development
  1. Granular data design helps improve the behavior and abilities of language models. This means making training data more specific so the models can reason better.
  2. New methods like Partial Answer Masking allow models to learn self-correction. This helps them improve their responses without needing perfect answers in the training data.
  3. Training models with a focus on long context helps them retrieve information more effectively. This approach tackles issues where models can lose important information in lengthy input.

2024 benchmarks for the DX Core 4

Engineering Enablement • 13 implied HN points • 17 Dec 24
🕹 Technology Software Development Productivity Engineering Innovation Benchmarking
  1. Smaller companies are quicker at delivering work than larger ones. Tech companies with fewer than 500 developers are particularly fast, completing more tasks per week.
  2. Tech companies spend more time creating new features and have a better experience for developers compared to traditional businesses. This helps them innovate more effectively.
  3. Large traditional companies may work slower, but they often have fewer errors in their work. This makes them safer, even if they don't deliver as quickly as tech firms.

How to practice isometric software development

Bad Software Advice • 247 implied HN points • 11 Mar 24
🕹 Technology Software Development Company dynamics Ownership
  1. Transition from offense to defense in a company means focusing on predictability and consistency rather than rapid growth.
  2. Operate within constraints by aligning your project with the company's current theme to open doors and ease scrutiny.
  3. Find ways to expedite approvals, deflect ownership, and handle criticism strategically to navigate a defensive organizational culture.

Software Supply Chain Security in DevSecOps & CI/CD

Resilient Cyber • 259 implied HN points • 27 Sep 23
🕹 Technology Cybersecurity Software Development DevOps Cloud Computing Information Security
  1. Software supply chain attacks are increasing, making it essential for organizations to protect their software development processes. Companies are looking for ways to secure their software from these attacks.
  2. NIST has issued guidance to help organizations improve software supply chain security, especially in DevSecOps and CI/CD environments. Following NIST's recommendations can help mitigate risks and ensure safer software delivery.
  3. The complexity of modern software environments makes security challenging. It's important for organizations to implement strict security measures throughout the development lifecycle to prevent attacks and ensure the integrity of their software.
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Write Cleaner, More Maintainable Rust Code with PhantomData

Aayushya’s Substack • 99 implied HN points • 06 Mar 24
🕹 Technology Coding Software Development Rust Programming
  1. Using PhantomData in Rust can help reduce code duplication by creating a generic struct with common fields and methods.
  2. Marker types like FreeLineQuantityTag and BilledLineQuantityTag can help differentiate between types when refactoring code.
  3. Leveraging advanced Rust features like PhantomData can lead to more maintainable and expressive code in real-world projects.

📽 Webinar: How To Maximize Model Accuracy

TheSequence • 70 implied HN points • 16 Dec 24
🕹 Technology Machine Learning Webinars Data science Software Development
  1. Models can lose accuracy over time in real use. It's important to know why this happens so you can fix it.
  2. Just because a model works well during training doesn't mean it will perform the same way in the real world. There are often differences that can affect results.
  3. Smart feature engineering is crucial for maintaining model accuracy without spending too much money. There are ways to improve performance that don't break the bank.

Exploring recent Python repositories, part 2

Mostly Python • 628 implied HN points • 29 Jun 23
🕹 Technology Programming Open Source Software Development Data Analysis Web Development
  1. The post explores new Python repositories that have gained just a small number of stars, filtering out the projects with no attention.
  2. Over 300,000 Python repositories are pushed to GitHub each month, showing the challenge of getting noticed among the vast amount of projects.
  3. Projects with a few stars can still be interesting and worth exploring, like a Pygame project inspired by Factorio.

Who cares about AuthZ? We went to KubeCon!

Permit.io’s Substack • 79 implied HN points • 28 Mar 24
🕹 Technology Cloud Computing Software Development Cybersecurity Data Management Open Source
  1. Fine-grained authorization is becoming really important as more developers talk about it. People see that better security can happen with smooth developer experiences.
  2. The rise of cloud-native architecture and big data means we need better ways to manage authorization decisions. It helps reduce decision fatigue and improves security.
  3. Tools like Policy as Code and various authorization engines are helping different teams work together better. This can lead to faster and more efficient development processes.

Generative AI 2023: Why This Year Marks a Major Turning Point

Gradient Flow • 199 implied HN points • 16 Nov 23
🕹 Technology AI Data Software Development Podcast Startups
  1. Generative AI, particularly large language models like GPT-4, is rapidly gaining mainstream adoption across various sectors like chatbots, computer programming, medicine, and law.
  2. Executives and managers are increasingly recognizing the transformative potential of generative AI, with surveys showing high interest and willingness to invest in the technology for efficiency and growth.
  3. Studies highlight the significant productivity gains generative AI provides, benefiting lower-performing workers and increasing productivity in areas like writing tasks and customer service by substantial percentages.

How to talk to someone who doesn't trust AI

The AI Frontier • 59 implied HN points • 25 Apr 24
🕹 Technology AI Machine Learning Software Development User Experience Product Design
  1. Many people doubt AI tools because they believe they only look good in demos but don't perform well in real life. Trying out LLMs like ChatGPT can often change that opinion for the better.
  2. Some skeptics challenge AI by asking tricky questions that the AI can't answer. It's important to remember that AI has limitations and not every mistake means it's useless.
  3. People notice that AI responses can seem similar, making it hard to trust their accuracy. Customizing answers and improving quality can help address this issue.

What's a data migration?

Technically • 29 implied HN points • 12 Nov 24
🕹 Technology Data Management Software Development Cloud Computing Database Systems IT Infrastructure
  1. Data migration is the process of moving information from one place to another, like relocating files when changing devices. It involves transferring various types of data, such as documents and databases, to ensure everything is in the right spot.
  2. Migrations can be complex and risky, often causing errors or service disruptions if not done carefully. This makes it crucial for companies to have good planning and oversight to avoid losing important data or negatively affecting users.
  3. There are many reasons to migrate data, such as upgrading technology or meeting new security regulations. Companies often need to adapt to growth or changes in the market, which can lead to costly and lengthy migration projects.

CISA and NCSC's Take on Secure AI Development

Resilient Cyber • 179 implied HN points • 01 Dec 23
🕹 Technology AI Security Software Development Cybersecurity Data Protection Risk management
  1. CISA and NCSC released guidelines for secure AI development that focus on unique security risks and the responsibilities of both AI providers and users. It's important for organizations to understand who is responsible for protecting AI systems.
  2. The guidelines emphasize practices like threat modeling and raising awareness of AI risks during the design phase. This helps organizations build secure systems by understanding potential threats upfront.
  3. Security doesn't stop at deployment; ongoing monitoring and incident response are crucial for maintaining safe AI operations. Companies need to keep an eye on how their AI systems behave and be ready to respond to any security incidents.

Introducing the DX Core 4

Engineering Enablement • 14 implied HN points • 10 Dec 24
🕹 Technology Developer Tools Productivity Software Development Frameworks Metrics
  1. The DX Core 4 is a new framework that combines existing models like DORA, SPACE, and DevEx to measure developer productivity more effectively. It aims to give clear guidance on what companies should measure.
  2. This framework focuses on four main areas: speed, effectiveness, quality, and impact, each with specific metrics to help organizations understand and improve their developer processes.
  3. The DX Core 4 is intended to be transparent and helpful for developers, promoting conversations around their challenges rather than using metrics against them.

Edge 461: The Many Challenges of Kowledge Distillation

TheSequence • 56 implied HN points • 31 Dec 24
🕹 Technology AI Machine Learning Data science Algorithms Software Development
  1. Knowledge distillation can be tricky because there’s a big size difference between the teacher model and the student model. The teacher model usually has a lot more parameters, making it hard to share all the useful information with the smaller student model.
  2. Transferring the complex knowledge from a large model to a smaller one isn't straightforward. The smaller model might not be able to capture all the details that the larger model has learned.
  3. Despite the benefits, there are significant challenges that need to be tackled when using knowledge distillation in machine learning. These challenges stem from the complexity and scale of the models involved.

Round-Up of Embedded World 2024

burkhardstubert • 59 implied HN points • 22 Apr 24
🕹 Technology Embedded Systems IOT Cybersecurity Software Development Conferences
  1. Software updates are important for devices, and using smaller application updates instead of large full updates can save time and bandwidth. It's a smart way to keep devices running smoothly.
  2. Manufacturers need to focus on creating simple, secure solutions for managing software updates and cryptographic keys to comply with new regulations like the EU Cyber Resilience Act.
  3. New companies like QBee and Crypto Quantique are developing innovative tools for secure OTA updates, which help manufacturers manage their devices more effectively and meet security standards.

Bits, Bytes, and Bylaws: Timeless Computing Laws (Part-1)

Wisdom over Waves • 159 implied HN points • 14 Dec 23
🕹 Technology Software Development Time management Organizational Behavior
  1. Hyrum's Law emphasizes that with a large number of users, system behaviors will be relied upon, regardless of what was promised.
  2. Hofstadter's Law points out that tasks often take longer than expected, even with buffers, so it's beneficial to shorten estimation cycles for better planning.
  3. Parkinson's Law highlights how work expands to fill the time available, showing the importance of constraints for creativity and efficiency.

Data Science Weekly - Issue 491

Data Science Weekly Newsletter • 419 implied HN points • 21 Apr 23
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Data Engineering
  1. AI academics are facing challenges keeping up with private sector investments. It's important for them to find survival strategies to remain competitive.
  2. There are ongoing discussions about the rapid progress in machine learning and how it can be overwhelming for developers. Many are sharing thoughts on adapting to this fast-paced change.
  3. Visualizing neural networks properly can help clarify concepts. There is a push for better diagrams to avoid confusion in understanding how these networks function.

RAG, Hallucination & Structure: Research By ServiceNow

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 18 Apr 24
🕹 Technology AI Machine Learning Data science Natural Language Software Development
  1. ServiceNow is using a method called Retrieval-Augmented Generation (RAG) to help transform user requests in natural language into structured workflows. This aims to improve how easily users can create workflows without needing deep technical knowledge.
  2. By using RAG, they want to reduce 'hallucination', which is when AI generates wrong or irrelevant info, and make the AI more reliable. This is important for gaining user trust in AI systems.
  3. The study also suggests future improvements, like changing output formats for efficiency and streamlining processes so that users can see steps one at a time, making it easier to follow along.

The Tech Buffet #17: 9 Effective Techniques To Boost Retrieval Augmented Generation (RAG) Systems

The Tech Buffet • 139 implied HN points • 02 Jan 24
🕹 Technology Artificial Intelligence Natural Language Processing Data Management Software Development Cloud Computing
  1. Make sure the data you use for RAG systems is clean and accurate. If you start with bad data, you'll get bad results.
  2. Finding the right size for document chunks is important. Too small or too large can affect the quality of the information retrieved.
  3. Adding metadata to your documents can help organize search results and make them more relevant to what users are looking for.

HILL: Solving for LLM Hallucination & Slop

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 23 May 24
🕹 Technology AI User Interface NLP Machine Learning Software Development
  1. HILL helps users see when large language models (LLMs) give wrong or misleading answers. It shows which parts of the response might be incorrect.
  2. The system includes different scores that rate the accuracy, credibility, and potential bias of the information. This helps users decide how much to trust the responses.
  3. Feedback from users helped shape HILL's features, making it easier for people to question LLM replies without feeling confused.

GroupBy #31: Migrating a Trillion Entries of Uber’s Ledger Data from DynamoDB to LedgerStore, Grab Experiment Decision Engine

VuTrinh. • 59 implied HN points • 16 Apr 24
🕹 Technology Data Engineering Machine Learning Software Development Cloud Computing
  1. Uber successfully migrated over a trillion entries of its ledger data to a new database called LedgerStore without causing disruptions. This shows how careful planning can make big data moves smooth.
  2. Airbnb has open-sourced a machine learning feature platform called Chronon, which helps manage data and makes it easier for engineers to work with different data sources. This promotes collaboration and innovation in the tech community.
  3. The GrabX Decision Engine boosts experimentation on online platforms by providing tools for better planning and analyzing experiments. This can lead to more informed decisions and improved outcomes in projects.

Back to First Principles | Productiv CTO Ashish Aggarwal

Dev Interrupted • 28 implied HN points • 29 Oct 24
🕹 Technology Software Development Cloud Computing Startup Culture Developer Productivity Artificial Intelligence
  1. Developers have 'bad days' when tools fail, processes are messy, or team communication is weak. Senior devs often feel frustrated with organization problems, while junior ones may take failures personally.
  2. The term 'zombiecorn' describes startups worth over $1 billion that struggle to grow and find their market. They often have high spending, depend heavily on funding, and face challenges with customer growth.
  3. Google is working on an AI called Project Jarvis that could take control of your browser to do tasks. But there's concern it might make Google's other services, like Search and Maps, less reliable.

Learning from the Best

Permit.io’s Substack • 79 implied HN points • 14 Mar 24
🕹 Technology Software Development Engineering Practices Open Source DevOps
  1. Learning from bigger companies can help solve problems effectively. They often share their insights which can be adapted to smaller projects.
  2. Not reinventing the wheel is smart. Using existing solutions like policy engines can save time and effort while ensuring reliability.
  3. Engaging with the community and resources available online can provide valuable knowledge and support for developers looking to improve their work.

I coded the security system myself, it is very secure, because it doesn't work.

Permit.io’s Substack • 99 implied HN points • 15 Feb 24
🕹 Technology Software Development Security Open Source Authentication
  1. Before building your own security system, think about whether it's really necessary. You might find better solutions that are already out there.
  2. Developers often dislike focusing on security tasks because they can be boring. It’s typically more efficient to use existing security tools instead of creating something new.
  3. There are standard systems like OAuth and JWT for handling security, and using open-source or developer platforms can save you a lot of headaches.

💥 Tech Talks Weekly #18

Tech Talks Weekly • 39 implied HN points • 13 Jun 24
🕹 Technology Conferences Software Development Java JavaScript Event Sourcing
  1. This week's Tech Talks Weekly features new talks from 15 different tech conferences. You'll find a variety of topics and insights from prominent speakers.
  2. Popular talks include topics like future-proofing Spring applications and managing code quality. These sessions can help you stay updated on tech trends.
  3. You can support the community by sharing this resource with friends and filling out a feedback form to improve future content.

Weekly Notes

Rethinking Software • 99 implied HN points • 21 Oct 24
🕹 Technology Software Development Agile Methodology Coding Practices Team Dynamics
  1. Managing programmers can be unpredictable. It's important to accept that things may not always go as planned.
  2. Euphemisms in corporate language can hide unpleasant truths. Words like 'alignment' often mean forcing compliance rather than true cooperation.
  3. Scrum practices may not be effective for all teams. Some core principles can actually create stress and hinder productivity instead of helping it.

CISA - Secure Software Development Attestation Final Form

Resilient Cyber • 79 implied HN points • 13 Mar 24
🕹 Technology Cybersecurity Software Development Government Policy Regulatory Compliance
  1. CISA has released a final form for secure software development that vendors need to follow to sell software to the Federal government. This means companies must prove their software is developed with important security practices.
  2. The attestation form applies to software developed or significantly changed after September 14, 2022, making it crucial for many vendors. This rule covers popular Software as a Service (SaaS) products as well.
  3. Not all software is included; for example, software created directly by Federal agencies and open-source software is exempt. This leaves some gaps in security measures that need attention, especially for software that might still pose risks.

BI is not ready for AI

HyperArc • 3 HN points • 06 Sep 24
🕹 Technology AI Data science Machine Learning Business Intelligence Software Development
  1. Business Intelligence (BI) needs both good models and great data to be effective with AI. Without quality data, AI can't really show its true power.
  2. Many BI tools only focus on successful outcomes, like specific metrics, while ignoring the complete journey of discovery. This limited data can lead to missing important insights.
  3. To improve AI's effectiveness in BI, we should include a wider range of experiences and exploration paths, not just successful queries. This fuller picture can help create better AI training sets.

From Rockets to Roadmaps: An Engineer's Guide to Product Success

Dev Interrupted • 14 implied HN points • 03 Dec 24
🕹 Technology Software Development Engineering Culture AI Applications Product Management Team Dynamics
  1. Engineers can drive product vision, leading to faster and more innovative development. This shifts the focus from just coding to solving real business problems.
  2. With AI making coding easier, engineers who understand customer needs and market trends will stand out. Their blend of technical skills and business savvy is crucial for success.
  3. Collaboration and teamwork are key in software development. It's not just about individual contributions but how teams work together to create better solutions.

What's a vector database?

Technically • 34 implied HN points • 21 Oct 24
🕹 Technology AI Data science Machine Learning Software Development Databases
  1. A vector database is a special storage for data used in AI. It helps store numbers that represent different types of information like text or images.
  2. To make AI models smarter, they need to use unique data from companies. This helps tailor responses and improve accuracy.
  3. There are ways to enhance AI models with unique data, like fine-tuning them or using a method called Retrieval Augmented Generation (RAG) to include important information in prompts.

DevEx: Better than an ExDev (And your Ex)

Permit.io’s Substack • 19 implied HN points • 04 Jul 24
🕹 Technology Software Development Cybersecurity User Experience DevOps Cloud Computing
  1. Developer experience (DevEx) is really important because it helps developers focus on building great apps while also handling security tasks more smoothly.
  2. It's crucial to make security features easy to use so that everyone involved, from developers to non-technical users, can manage permissions and access without problems.
  3. A successful approach to DevEx considers the whole development process, ensuring security practices are integrated naturally into workflows from start to finish.

Unleashing LLMs in Cybersecurity: A Playbook for All Industries

Gradient Flow • 259 implied HN points • 20 Apr 23
🕹 Technology Cybersecurity AI Automation Data Analysis Software Development
  1. Large Language Models (LLMs) are gaining interest in various industries, especially in cybersecurity, and can be used as a playbook for implementation in other domains.
  2. Custom LLMs can be created for cybersecurity applications, leading to potential advancements like specialized chatbots and content generation for enhanced security measures.
  3. LLMs are transforming automation processes in cybersecurity, offering improved accuracy and convenience, and displaying potential for impact across multiple industries through domain-specific adaptations.

E3 - 🛠️ Prompt Engineering Basics and 50+ Free 🎁 Product Management Prompts 🚀

The Product Channel By Sid Saladi • 20 implied HN points • 24 Nov 24
🕹 Technology AI Tools Product Management Software Development Prompt engineering Data Analysis
  1. Prompt engineering is about crafting the right questions to get useful responses from AI. Think of it like asking the AI to help you with specific tasks in a clear way.
  2. This skill can help product managers speed up their work by automating tasks and generating creative ideas. It's a powerful tool for making better decisions based on data.
  3. Understanding how to structure prompts effectively can lead to more relevant and accurate results. It involves giving clear instructions, context, and examples to guide the AI.

Data Science Weekly - Issue 484

Data Science Weekly Newsletter • 439 implied HN points • 02 Mar 23
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Cloud Computing
  1. Data scientists need the right tools and environment to do their jobs effectively. Organizations can help by improving their data science infrastructure.
  2. Understanding how to choose and advocate for important metrics is vital for product teams. This can lead to significant growth in user engagement.
  3. A/B testing is crucial in fraud detection to compare models and determine their effectiveness. It can provide valuable insights that improve model performance.