The hottest Software Development Substack posts right now

And their main takeaways
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Random Chain-Of-Thought For LLMs & Distilling Self-Evaluation Capability

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 08 Jan 24
🕹 Technology AI Machine Learning Data science Natural Language Processing Software Development
  1. Complexity in processing data for large language models (LLMs) is growing. Breaking tasks into smaller parts is becoming a standard practice.
  2. LLMs are now handling tasks that used to require human supervision, such as generating explanations or synthetic data.
  3. Providing detailed context during inference is crucial to avoid mistakes and ensure better responses from LLMs.

LLM Performance Over Time & Task Contamination

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Jan 24
🕹 Technology AI Machine Learning Natural Language Processing Data science Software Development
  1. LLMs do better on tasks related to older data compared to newer data. This means they might struggle with recent information.
  2. Training data can affect how well LLMs perform in certain tasks. If they have seen examples before, they can do better than if it's completely new.
  3. Task contamination can create a false impression of an LLM's abilities. It can seem like they are good at new tasks, but they might have already learned similar ones during training.

Intelligent & Programable Prompt Pipelines From Haystack

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 18 Dec 23
🕹 Technology Artificial Intelligence Software Development Data science Machine Learning Chatbots
  1. Prompt pipelines help connect different prompts in a simpler way than using complex autonomous agents. This means making sure that data flows smoothly when using tools powered by AI.
  2. While using JSON for output is helpful, there are challenges in maintaining a consistent structure. This can make it tricky to handle the data as it changes.
  3. The Haystack framework offers a way to bridge basic prompts and more complex systems. It shows how to manage user input and AI output for better interactions.

OpenAI Announced 28 Models To Be Switched Off

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 07 Dec 23
🕹 Technology AI Machine Learning Data science Computing Software Development
  1. OpenAI is shutting down 28 of its language models, and users need to switch to new models before the deadline. It's important for developers to find alternative models or consider self-hosting their solutions.
  2. Cost is a big issue with using language models; it’s usually more expensive to generate responses than to provide input. Users must monitor their token usage carefully to manage expenses.
  3. LLM Drift is a real concern, as responses from language models can change significantly over time. Continuous monitoring is needed to ensure accuracy and performance remain stable.

Chain-Of-Note (CoN) Retrieval For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Nov 23
🕹 Technology AI Data science Machine Learning Natural Language Software Development
  1. Chain-of-Note (CoN) helps improve how language models find and use information. It does this by sorting through different types of information to give better answers.
  2. CoN uses three types of reading notes to keep responses accurate. This means it can better handle situations where the data isn’t directly answering a question.
  3. Combining CoN with data discovery and design is important for getting reliable information. This makes sure that language models work well in different situations.
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Now You Can Toggle OpenAI Model Determinism

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Nov 23
🕹 Technology AI NLP LLMs Chatbots Software Development
  1. OpenAI now lets you control whether their model gives consistent answers to the same questions. This means if you ask it something more than once, you'll get the same answer each time.
  2. This feature is useful for testing and debugging, where you need to see the same response to know the system is working correctly.
  3. To get the same output consistently, you need to set a 'seed' number in your request. Make sure to keep the other settings the same each time you ask.

Knowledge Retrieval Via The OpenAI Playground

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 08 Nov 23
🕹 Technology AI Machine Learning Software Development Data science Natural Language
  1. OpenAI has introduced a Retrieval Augmentation tool in its Playground. This means the assistant can now find and use information from uploaded documents to answer questions better.
  2. When users upload a file, the assistant automatically processes it. It retrieves relevant content based on what the user asks and the context needed to give an answer.
  3. This feature aims to improve the assistant's performance while offering insights for better management. More controls and flexibility will be important as users need to customize how documents are handled.

LLM Alignment, Hallucination & Misinformation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Nov 23
🕹 Technology AI Data science Machine Learning Natural Language Software Development
  1. It's important to have good data design and human supervision for large language models. This helps improve accuracy and creates better conversations.
  2. Large language models can produce different answers to the same question at different times. This means they are not always consistent.
  3. Misinformation and hallucinations can happen with these models, but we can reduce these issues by using better training and feedback methods.

Self-Refine Is An Iterative Refinement Loop For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Nov 23
🕹 Technology AI Machine Learning Natural Language Processing Software Development Data science
  1. Self-Refine improves LLM output without needing extra training data. It does this by refining the output through feedback in a loop.
  2. The approach mimics how humans recheck their work to find better ways to express ideas, like improving an email draft or optimizing code.
  3. Quality of results gets better with more iterations, but it's important to balance this with potential delays and costs. Stronger models produce better refinements.

Data Delivery To Large Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Oct 23
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Software Development
  1. Data delivery is key to making large language models (LLMs) work well. It involves giving the model the right data at the right time to get accurate answers.
  2. There are two main stages for data delivery: during training and during inference. Training helps the model learn, while inference is when the model uses what it learned to respond to questions.
  3. A balanced approach is needed for data delivery in LLMs. Using different methods together will lead to better results than sticking to one single method.

Large Language Model Landscape

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Oct 23
🕹 Technology AI NLP Machine Learning Data science Software Development
  1. Large Language Models (LLMs) are evolving and diversifying, leading to the rise of Foundation Models that can handle various types of data like text and images. This means they can do more complex tasks now.
  2. There's a shift in how LLMs are used, with a focus on improving their functions like text analysis, speech recognition, and dialog generation. New techniques help these models perform better in their designated tasks.
  3. The market is seeing exciting new opportunities, especially in tools that help businesses use LLMs effectively, like data discovery and user-friendly interfaces. These tools can help companies tap into the potential of LLMs better.

RAG & Fine-Tuning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Sep 23
🕹 Technology Artificial Intelligence Machine Learning Natural Language Data science Software Development
  1. RAG, or Retrieval Augmented Generation, helps improve responses by adding relevant information to AI prompts. This makes the AI's answers more accurate and contextually appropriate.
  2. Fine-tuning adjusts the AI's behavior based on specific data, which can enhance its performance in certain fields like medicine or law. However, it may not always adapt well to unique user inputs.
  3. Using RAG alongside fine-tuning is the best approach. RAG is easier to implement and helps keep the AI's responses up-to-date while fine-tuning improves overall quality.

Emerging Large Language Model (LLM) Application Architecture

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 19 Sep 23
🕹 Technology AI Machine Learning Natural Language Software Development Automation
  1. Large Language Models (LLMs) work with unstructured data like human conversations. They generate natural language, but can sometimes give incorrect answers, known as 'hallucination.'
  2. Fine-tuning LLMs isn't popular anymore due to high costs and the need for constant updates. Instead, focusing on relevant prompts helps get better, accurate responses.
  3. Using multiple LLMs for different prompts makes sense. New tools are emerging to test how well different models work with specific prompts.

Agents, LLMs & Multihop Question Answering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Apr 23
🕹 Technology AI Machine Learning Natural Language Processing Software Development Data science
  1. Agents can use different tools based on user requests. This gives them the flexibility to respond to questions that don't fit a typical sequence.
  2. Prompt chaining involves linking prompts together to create a more complex response. However, it can struggle with unexpected user queries.
  3. For better responses, it's important for an Agent to have clear instructions on which tool to use. Fine-tuning these instructions can improve how well the Agent answers questions.

Generative AI Prompt Pipelines

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Apr 23
🕹 Technology AI Machine Learning Natural Language Data science Software Development
  1. Prompt pipelines make it easier to provide answers by using templates and adding specific context from a knowledge source. This helps to create better responses based on user requests.
  2. When a user asks something, the system finds the right template, fills in the necessary information, and sends it off to get a clear answer quickly.
  3. Using these pipelines helps to avoid mistakes by ensuring the information used is updated and accurate, rather than relying on potentially outdated data.

Training & Testing Text Classification Models with Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 28 Mar 23
🕹 Technology Machine Learning Artificial Intelligence Cloud Computing Data science Software Development
  1. Google's AutoML makes it easy to build classification models without needing much technical know-how. It simplifies the process, allowing more people to create models.
  2. Vertex AI can classify text into single or multiple categories, but it doesn't support complex class structures. So, simple classifications work best.
  3. While AutoML speeds up model creation, training times can be long. It's important to plan your data splits and annotation sets for better model performance.

Creating Training Data For Text Classification In Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Mar 23
🕹 Technology AI Data science Machine Learning Cloud Computing Software Development
  1. Creating training data for AI is a crucial first step in making it work well. It involves careful organization and structuring of data to help the AI learn effectively.
  2. A data-centric approach requires ongoing exploration and refinement of the training data. This means continuously checking the data for patterns and making adjustments as needed.
  3. Using human labelers to categorize data can be costly and complex. It's often easier to automate this process with human oversight rather than sending data out for labeling.

A First Look At OpenAI GPT-4

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Mar 23
🕹 Technology Artificial Intelligence Machine Learning Natural Language Software Development Innovation
  1. GPT-4 is a step up from GPT-3.5, but the difference is mostly noticeable with complex tasks. For simple chat, you might not see much change.
  2. Currently, GPT-4 can't process images, but there's hope for that feature in the future. It'll be announced if it becomes available.
  3. One cool feature of GPT-4 is its ability to handle longer texts, over 25,000 words. This is great for detailed conversations or long content creation.

What Does ChatML Mean For Prompt Chaining Applications

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Mar 23
🕹 Technology AI Data science Natural Language Software Development
  1. Chatbots allow users to input data more freely using natural language. This means people don't have to fit their input into specific forms or buttons.
  2. Prompt engineering helps users create effective prompts for large language models. It involves designing prompts that guide the model to produce the desired responses.
  3. With the introduction of ChatML, there will be a standard way to format prompts. This could make it easier for different applications to understand and process user requests.

Chaining Large Language Model (LLM) Prompts Via Visual Programming

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Feb 23
🕹 Technology Artificial Intelligence Programming Software Development Data science Product Design
  1. Chaining LLM prompts can make complex tasks easier to handle. It allows many prompts to work together for better results.
  2. Using templates for prompts helps to save time and keep things organized. They allow you to reuse parts of your prompts easily.
  3. There's a growing opportunity to combine traditional logic with LLMs. This mix can enhance chatbot and AI systems in powerful ways.

The Anatomy Of Large Language Model (LLM) Powered Conversational Applications

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Feb 23
🕹 Technology AI Machine Learning Software Development User Experience Natural Language
  1. To make applications using large language models (LLMs) successful, businesses need to ensure they add real value through their API calls.
  2. The development of a good framework is important for collaboration between designers and developers, helping to turn conversation designs smoothly into functional applications.
  3. User experience is key; users just want great experiences without worrying about the technology behind it.

What Are Realistic GPT-4 Size Expectations?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 15 Feb 23
🕹 Technology Artificial Intelligence Machine Learning Software Development Data science Natural Language Processing
  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 AI Machine Learning Natural Language Software Development Data science
  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 AI Models Data science Machine Learning Software Development Web applications
  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 Data science Statistics Machine Learning Software Development Analytics
  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.

LLM and GenAI future from a business perspective

Thoughts from the trenches in FAANG + Indie • 0 implied HN points • 17 Aug 24
💼 Business Tech Trends Data Management Productivity Tools Market Analysis 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 Project management Agile Methodology Stakeholder engagement
  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 Cloud Computing Software Development DevOps Serverless AWS
  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 Data Engineering Open Source Cloud Computing Software Development Analytics
  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 AI Machine Learning Software Development Data science Digital Transformation
  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 Product Design User Experience Software Development Cloud Services
  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 Data Management User Experience Product Design 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 User Experience Navigation Product Design Software Development User Behavior
  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.

Use relative dates and to help users learn your UI

CommandBlogue • 0 implied HN points • 20 Mar 24
🕹 Technology User Experience User Interface Product Design Software Development Natural Language Processing
  1. Using relative dates makes it easier for users to understand and interact with a user interface. For example, saying 'next Thursday' is more natural than giving a specific date.
  2. People think about time differently than computers do. They often use relative terms, so designs should accommodate that way of thinking.
  3. Date pickers should be simple and consistent with other input methods. Changing how users input information can frustrate them and make the experience less enjoyable.

How to make settings less frustrating

CommandBlogue • 0 implied HN points • 20 Mar 24
🕹 Technology User Experience Product Design Software Development User Interface Usability testing
  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 Programming Web Development Open Source
  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 Entrepreneurship Product Design User Experience DevOps
  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 Automation User Experience Web 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 Cloud Computing APIs Web Development Programming
  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.