The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

What Are Realistic GPT-4 Size Expectations?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 15 Feb 23
🕹 Technology Software Development
  1. GPT-4 is likely to have around 1 trillion parameters, which is much smaller than the rumored 100 trillion. This is based on how language models have grown over time.
  2. Experts suggest that it's not just about the number of parameters. The quality of training data is equally important for improving performance in language models.
  3. There is a limited supply of high-quality language data. If better data sources don’t emerge, the growth of model sizes may slow down significantly.

The Large Language Model Landscape

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Feb 23
🕹 Technology Software Development
  1. There are now many companies making large language models (LLMs) for different language tasks, giving users lots of choices.
  2. The main functions of LLMs include answering questions, translating, generating text, generating responses, and classifying information.
  3. While classification is very important for businesses, text generation is one of the most impressive and flexible uses of LLMs.

How To Create HuggingFace🤗 Custom AI Models Using autoTRAIN

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Feb 23
🕹 Technology Software Development
  1. autoTRAIN lets you build custom AI models without needing to code. It's user-friendly and has both free and paid options.
  2. You can easily upload your data in different formats like CSV, TSV, or JSON. The platform keeps your data private and secure.
  3. As your model trains, you can see real-time results about its accuracy. This helps you understand how well it's performing and make necessary adjustments.

How to use Google’s CausalImpact

Logos • 0 implied HN points • 23 Dec 21
🕹 Technology Software Development
  1. Google's CausalImpact helps you see how actions, like a marketing campaign, affect outcomes like sales. It predicts what would have happened without that action, making it easier to understand its impact.
  2. Using CausalImpact requires some basic coding in R, but even beginners can follow along. You'll collect data in a simple format, run the analysis, and see results visually and in tables.
  3. When using CausalImpact, it's crucial to choose the right control variables. They should correlate with your main outcomes but not be influenced by the actions you're analyzing.

Coming soon

DataSyn’s Substack • 0 implied HN points • 27 Aug 24
🕹 Technology Software Development
  1. A new Substack for DataSyn is launching soon. It will likely share information about synthetic data and its uses.
  2. Subscribing to this Substack could provide useful insights in the field of data science.
  3. The focus seems to be on artificial intelligence and large language models.
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LLM and GenAI future from a business perspective

Thoughts from the trenches in FAANG + Indie • 0 implied HN points • 17 Aug 24
💼 Business Software Development
  1. LLM and GenAI are helpful tools that boost human productivity, even though they can't think creatively on their own.
  2. The cost of using these models is decreasing, making it easier for businesses to choose vendors based on price and convenience.
  3. To get the most value from LLM, companies must control and organize their data properly, which may create new job opportunities in data management and security.

On communicating why software is late

Thoughts from the trenches in FAANG + Indie • 0 implied HN points • 17 Jun 23
🕹 Technology Software Development
  1. Software projects often experience delays, especially when creating new software. It's important for both engineers and stakeholders to work together and understand how to communicate about these delays effectively.
  2. Clear communication about the project's delay is crucial. Everyone should know the new expected delivery date, what caused the delay, and what is being done to fix it.
  3. It's helpful to regularly share updates about the project's progress. Using a simple color system can show how likely the project is to meet deadlines, helping everyone stay informed and manage expectations.

Working effectively with AWS Lambda - Logs

Thoughts from the trenches in FAANG + Indie • 0 implied HN points • 09 Jun 23
🕹 Technology Software Development
  1. AWS Lambda allows you to run code without managing servers, making it a great choice for many developers.
  2. Using AWS CLI to stream logs from Lambda to your terminal is much faster and more efficient than using the AWS Console.
  3. You need to know the log group for your Lambda function, but once you do, setting up log streaming is a simple process.

DLD #1 | Data Landscape Digest 🗞️

Practical Data Engineering Substack • 0 implied HN points • 25 Aug 24
🕹 Technology Software Development
  1. Data engineering is evolving rapidly, and staying updated on new tools and technologies is important for success in the field.
  2. Mastering the fundamentals, like SQL and Python, is crucial as they form the foundation for using advanced tools effectively.
  3. Open source solutions, like Apache Hudi and XTable, are gaining popularity and can provide great benefits for managing data efficiently.

LLMs can only generate

Sunday Letters • 0 implied HN points • 14 Jul 24
🕹 Technology Software Development
  1. Generative models like LLMs can only create new content from scratch. They can't just fix mistakes in the specific part we want; they'll regenerate everything instead.
  2. Reliability is key for these systems to be useful. Unlike humans, who can iterate and refine work step by step, generative models don't have that ability to just modify a piece.
  3. When using generative models, it's important to clearly scope the work. You should restrict what you want the model to generate to avoid unexpected changes, using coding to help manage the tasks.

How link detection engages users more

CommandBlogue • 0 implied HN points • 28 May 24
🕹 Technology Software Development
  1. Links are common in today's digital world, often replacing traditional file sharing. Using links helps keep information accessible but can pull users away from your app.
  2. Enhancing user experience is important, so product builders should aim to integrate link previews or embed features. This allows users to interact with linked content without leaving the main app.
  3. Users prefer to stay in one app for convenience. The less they have to jump between different applications, the smoother their experience will be.

The simple button that makes data easier to use

CommandBlogue • 0 implied HN points • 28 May 24
🕹 Technology Software Development
  1. Adding a reset button in dashboards helps users easily undo multiple customizations with one click. It saves time and makes exploring data more efficient.
  2. This feature allows users to quickly return to the default view, which is helpful when working with multiple users in an app.
  3. Just like pressing delete to start over, users prefer easy solutions that let them change their paths without wasting time.

The easiest way to fix navigation

CommandBlogue • 0 implied HN points • 20 Mar 24
🕹 Technology Software Development
  1. Always have back and forward buttons in apps to help users navigate easily. This small change can make a big difference.
  2. Users should not need to understand the whole site layout to find their way around. It’s key for new users to feel confident while using the app.
  3. Making users feel smart and comfortable boosts their overall experience. If they don’t feel lost, they’re more likely to stick around.

How to make settings less frustrating

CommandBlogue • 0 implied HN points • 20 Mar 24
🕹 Technology Software Development
  1. Users often struggle to find the right settings because the organization of options can be confusing. Labels need to be clear so users know exactly where to look.
  2. A good solution is to show users what settings are already active. This helps them understand their current options without clicking through multiple menus.
  3. Reducing the number of choices and distractions can help users feel less overwhelmed. A simple display of enabled settings can lead to a smoother experience.

Man, I missed TypeScript ♥️

André Casal's Substack • 0 implied HN points • 23 Aug 24
🕹 Technology Software Development
  1. TypeScript makes coding easier by catching errors early, so developers can avoid running broken code. Plus, it helps with better auto-completion and suggestions.
  2. Adding support for multiple package managers like npm, yarn, and pnpm is simple and can enhance a project's flexibility for users.
  3. Showing users where they are in the process with a step counter improves their experience. It helps them feel more in control during a task.

Getting LaunchFast on the hands of devs ♥️

André Casal's Substack • 0 implied HN points • 09 Aug 24
🕹 Technology Software Development
  1. Getting user feedback is really important. Talking to developers showed what needs to be improved in the product.
  2. The homepage of the app now has clear instructions for users. This makes it easier for new customers to understand how to use the product right away.
  3. Next steps include improving the landing page and preparing for a launch on Product Hunt. There’s a lot to work on to make the product better!

BulkUnsubscribe: Unsubscribe from all marketing emails in 1-click

aspiring.dev • 0 implied HN points • 16 Jun 24
🕹 Technology Software Development
  1. You can now easily unsubscribe from a lot of marketing emails in just one click. This is possible with a new standard by Gmail and Yahoo that lets emails include an 'Unsubscribe' button.
  2. There are different methods to unsubscribe, like sending an email, clicking a link, or using a 'one-click' option that works automatically. The 'one-click' method is the easiest and most efficient.
  3. A tool is being developed to automate the unsubscribe process by checking your emails and removing you from unwanted mailing lists, making it a lot simpler to manage your inbox.

How To Build AWS-Compatible APIs: AWS Sigv4

aspiring.dev • 0 implied HN points • 01 Mar 24
🕹 Technology Software Development
  1. AWS Sigv4 is a way to authenticate requests when using AWS services. It works by signing requests with your Access Key ID and Secret Access Key, similar to RSA keys.
  2. You can create your own AWS-compatible APIs by implementing signature verification in middleware. This allows your API to mimic AWS services like S3 or DynamoDB.
  3. Building these APIs can be a good idea for startups. You can create custom services that interact with AWS or even replace AWS services entirely while maintaining compatibility.

Building a Fly.io-like Scheduler Part 2: Resource Requirements and Multi-Region

aspiring.dev • 0 implied HN points • 26 Feb 23
🕹 Technology Software Development
  1. We can make scheduler systems smarter by adding task requirements like region and resource slots. This means a worker can only take on a task if it has the right resources available.
  2. Workers compare the incoming requests against their available resources. If they can't meet the requirements, they simply ignore the task instead of taking it.
  3. The system can be expanded to include more detailed requirements in the future, such as specific CPU types or GPU support, making it adaptable to different tasks and workloads.

Building a Fly.io-like Scheduler Part 1: Basic Coordinator and Workers

aspiring.dev • 0 implied HN points • 23 Feb 23
🕹 Technology Software Development
  1. Fly.io uses synchronous scheduling, meaning you either get a compute resource when you ask for it or you don't. This makes it simpler to handle workloads like serverless functions.
  2. The scheduler's design allows workers to manage their own availability, removing the need for a separate database. This lets workers freely join or leave the system without issues.
  3. In this system, a coordinator requests and schedules tasks on available worker nodes. The first worker to respond gets the task, making it efficient for various jobs like running Docker containers or AI inference.

AT&T Wireless Service Disruption Affects Customers Due to 'Software Issue'

Vigilainte Newsletter • 0 implied HN points • 28 Aug 24
🕹 Technology Software Development
  1. AT&T is facing a major service disruption due to a software issue, causing many customers to lose their ability to make calls or use data.
  2. People are frustrated with the lack of communication from AT&T's support, which has been overwhelmed and unable to provide clear solutions.
  3. This outage is especially bad timing for AT&T, as they just got fined by the FCC for not notifying 911 about a previous outage.

[in case you missed it] Data Science Weekly - Issue 471

Data Science Weekly Newsletter • 0 implied HN points • 04 Dec 22
🕹 Technology Software Development
  1. MLOps is important for automating machine learning products. It helps researchers and practitioners understand the roles and workflows needed in machine learning.
  2. Companies face challenges when moving to realtime machine learning. They need to balance performance, cost, and complexity in their ML pipelines.
  3. The FDA has outlined guiding principles for using AI in medical devices. These principles aim to ensure safety and effectiveness in tech for healthcare.

[in case you missed it] Data Science Weekly - Issue 469

Data Science Weekly Newsletter • 0 implied HN points • 20 Nov 22
🕹 Technology Software Development
  1. Learning machine learning can be a challenging but rewarding journey, and it often involves continuous effort to improve skills and practices.
  2. Robotics and AI are making a big impact in industries like fulfillment, but there are still many challenges to overcome as the technology scales.
  3. Emerging AI capabilities, particularly in large language models, are becoming increasingly action-driven, resembling more advanced forms of intelligence.

[in case you missed it] Data Science Weekly - Issue 467

Data Science Weekly Newsletter • 0 implied HN points • 06 Nov 22
🕹 Technology Software Development
  1. Startups using large language models should focus on improving user experience, as it's currently their biggest hurdle, not the data or algorithms.
  2. Data science notebooks have evolved significantly since they were first created, and there are predictions for how they'll continue to develop in the future.
  3. OpenAI is supporting new AI startups by offering $1 million each and early access to their systems, which could help boost innovation in the field.

[in case you missed it] Data Science Weekly - Issue 466

Data Science Weekly Newsletter • 0 implied HN points • 30 Oct 22
🕹 Technology Software Development
  1. Teaching science should start with the values and virtues of being a good scientist rather than just tools and techniques. Focusing on qualities like curiosity and creativity is key.
  2. Creating a data dictionary before collection is crucial. It helps guide your data collection and makes interpreting results easier later on.
  3. Open source reinforcement learning is evolving with new organizations to improve standardization and support. This effort aims to enhance the quality and usability of available tools.

[in case you missed it] Data Science Weekly - Issue 464

Data Science Weekly Newsletter • 0 implied HN points • 16 Oct 22
🕹 Technology Software Development
  1. Building a community of R users can greatly enhance collaboration and knowledge sharing, especially in specialized fields like pharmaceuticals.
  2. Generating research ideas often starts with identifying gaps in existing literature, which can be guided by specific frameworks to improve the quality of ideas.
  3. Data cleaning is crucial for model accuracy, and its success relies on effective ETL processes and organizational commitment to maintaining high-quality data.

[in case you missed it] Data Science Weekly - Issue 463

Data Science Weekly Newsletter • 0 implied HN points • 09 Oct 22
🕹 Technology Software Development
  1. To explore a large CSV file, you should use handy tools and methods to quickly understand the data without getting overwhelmed.
  2. AI can help convert messy unstructured text into organized data, speeding up tasks that would usually take a long time manually.
  3. Building a career in data science involves learning not just the technical skills but also how to navigate job opportunities and project management.

[in case you missed It] Data Science Weekly - Issue 459

Data Science Weekly Newsletter • 0 implied HN points • 11 Sep 22
🕹 Technology Software Development
  1. Organizations should work on improving their data quality because it directly impacts their success and competitive edge. Creating better data can lead to better decisions and outcomes.
  2. The modern data stack's activation layer is crucial for turning data into actionable insights. This allows companies to go beyond just looking at data and actually use it to improve their products and services.
  3. Using the right tools, like ONNX for model deployment, can help make machine learning models more portable and less tied to specific programming environments. This makes it easier to run models across different programming languages.

[in case you missed it] Data Science Weekly - Issue 457

Data Science Weekly Newsletter • 0 implied HN points • 28 Aug 22
🕹 Technology Software Development
  1. AI has limits when it comes to understanding human language. It can't fully replicate how humans think because language itself is restrictive.
  2. Observable now offers Free Teams, making it easier for data people to collaborate publicly. You can create teams quickly and share notebooks without complicated setups.
  3. The backpropagation algorithm in machine learning is often misunderstood. It is more complex than just applying the chain rule repeatedly, and oversimplifying it can lead to problems.

[in case you missed it] Data Science Weekly - Issue 456

Data Science Weekly Newsletter • 0 implied HN points • 21 Aug 22
🕹 Technology Software Development
  1. Machine learning models need regular maintenance. Even after they're deployed, the changing world means they require constant updates to stay effective.
  2. Specialized skills in data science can lead to better job opportunities. Understanding different roles can help you maximize your impact in the field.
  3. Learning resources for machine learning and data science are widely available. Whether through courses, videos, or discussions, there's plenty of help to get started in this exciting area.

[in case you missed it] Data Science Weekly - Issue 446

Data Science Weekly Newsletter • 0 implied HN points • 12 Jun 22
🕹 Technology Software Development
  1. The connection between literature and AI has a long history. There are many examples of how machines have been used to create and assist in writing over the years.
  2. Jupyter Notebooks are versatile tools for data science. They can be used in surprising ways beyond just coding, mixing visualizations and markdown effectively.
  3. Understanding how to use AI responsibly is important. As AI increasingly relies on crowdworkers for data, it raises ethical questions about oversight and compliance.

[in case you missed it] Data Science Weekly - Issue 444

Data Science Weekly Newsletter • 0 implied HN points • 29 May 22
🕹 Technology Software Development
  1. Good ML systems need careful design and planning. It's important to know the difference between research and real-world applications.
  2. Data isn't always the best way to make decisions. Sometimes relying too much on data can lead to worse outcomes.
  3. New AI technologies are changing how we think about intellectual property. We might need new laws to keep up with inventions created by machines.

[in case you missed it] Data Science Weekly - Issue 443

Data Science Weekly Newsletter • 0 implied HN points • 22 May 22
🕹 Technology Software Development
  1. There's a new initiative where you can share what you're up to, and they might include your story in the newsletter. It's a nice way to connect with others in the data science community.
  2. There's a focus on improving software development skills for data scientists by following best practices like version control and automatic testing. This can help teams work better together.
  3. AI-generated art is being debated, with some arguing it's just imitation and not true art. It raises questions about the value of creativity and human experience in art.

[in case you missed it] Data Science Weekly - Issue 435

Data Science Weekly Newsletter • 0 implied HN points • 27 Mar 22
🕹 Technology Software Development
  1. Algorithmic impact assessments are important for using data in healthcare. They help ensure that the technology is safe and beneficial for everyone involved.
  2. Using deep learning on electronic medical records may not work well right now. It might be better to focus on improving IT support and fixing underlying structures instead.
  3. There's a problem with how we explain AI systems. Many explanations offered today don't truly help us understand how these systems work.

[in case you missed it] Data Science Weekly - Issue 413

Data Science Weekly Newsletter • 0 implied HN points • 24 Oct 21
🕹 Technology Software Development
  1. Understanding your tools is essential for success in computer science. Learning how to use the command line and version control can help you a lot.
  2. Improving language models to reduce harmful content is a complex task. It's important to ensure these models are safe while still being effective.
  3. Getting a job in data science is easier when you know what companies look for. Keep an eye on the key skills and experiences employers value most.

[in case you missed it] Data Science Weekly - Issue 409

Data Science Weekly Newsletter • 0 implied HN points • 26 Sep 21
🕹 Technology Software Development
  1. Trees are becoming a new model for understanding ecology and plant intelligence. They help researchers think more deeply about the environment.
  2. Effective machine learning often starts without actually using machine learning. It’s important to focus on gathering quality data and defining clear processes first.
  3. Business Intelligence (BI) tools are evolving, but they should focus on providing clear and complete answers to data-related questions for users.

[in case you missed it] Data Science Weekly - Issue 400

Data Science Weekly Newsletter • 0 implied HN points • 25 Jul 21
🕹 Technology Software Development
  1. A new documentary used AI to generate Anthony Bourdain's voice, raising questions about ethics in media. It's important to think about how technology like this affects what we perceive as real.
  2. Deep learning is becoming more effective despite challenges, and understanding its success can help bridge gaps between traditional statistics and modern AI. Bigger and deeper models often yield better results, even with less data.
  3. Combining different AI models, like Transformers and convolutional neural networks, can lead to better performance in tasks like image recognition. This shows that mixing approaches can help overcome the limitations of each technology on its own.

[in case you missed it] Data Science Weekly - Issue 396

Data Science Weekly Newsletter • 0 implied HN points • 27 Jun 21
🕹 Technology Software Development
  1. Understanding hype cycles can help us see how technology develops over time. It's interesting to look back at these cycles to learn from past trends.
  2. Multi-task learning is beneficial as it allows machines to make multiple predictions. This can lead to more effective and efficient models.
  3. AI struggles with understanding basic concepts like 'same' and 'different.' This limitation raises questions about how truly intelligent AI can become.

[in case you missed it] Data Science Weekly - Issue 390

Data Science Weekly Newsletter • 0 implied HN points • 16 May 21
🕹 Technology Software Development
  1. AI can solve complex puzzles better than humans, but humans still have unique skills. Don't give up on challenging word games just yet!
  2. Defining trees in biology is tricky because many plants don't fit into clear categories. It's surprising how many things that look like trees actually aren't.
  3. New technology makes searching through large image databases easier. With smart algorithms, you can quickly find the pictures you're looking for without remembering file names.