The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

HackerRank Springboot Test — Trades Api

Better Engineers • 0 implied HN points • 03 May 24
🕹 Technology Software Development API design Web Development Database Management
  1. You can create REST APIs for managing trade records using Spring Boot and JPA. Start by setting up the project and required dependencies.
  2. Understanding the API endpoints is crucial. You need to handle POST, GET, and provide some query parameters to filter trades.
  3. Don’t forget to design the database schema properly and create service and controller classes for handling requests and responses.

Creating Custom Notifications in Android: A Step-by-Step Guide

Better Engineers • 0 implied HN points • 26 Apr 23
🕹 Technology Software Development Mobile Apps User Experience Programming
  1. Creating a notification channel is the first step to customize notifications in your Android app. This helps users control how they receive notifications.
  2. Designing a custom layout for the notification is crucial. It allows you to display information in a unique way, making it more engaging for users.
  3. Using NotificationCompat.Builder helps you build and trigger the notification effectively. You can also add interactive elements to enhance the user experience.

Improve Kotlin Code Review — Part II

Better Engineers • 0 implied HN points • 23 Apr 23
🕹 Technology Programming Software Development Code Review
  1. Using generics in Kotlin allows you to create code that can work with different types, making it more flexible and reusable. For example, you can create a box that holds any type of object.
  2. The 'in' and 'out' keywords help define how generic types can be used, allowing for safer and more organized code. The 'in' keyword is for consuming data, while 'out' is for producing it.
  3. Utility functions like 'applyIf' and 'withNotNull' help you write cleaner code by letting you conditionally run actions only when certain conditions are met or when values are not null.

Prefer Composition over Inheritance

Better Engineers • 0 implied HN points • 23 Mar 23
🕹 Technology Software Development Programming Languages Systems Design Code Optimization
  1. Composition is often better than inheritance because it allows you to create new classes by combining existing ones. This helps avoid complex class hierarchies.
  2. Using interfaces can help you achieve different behaviors without relying on a single inheritance path. This keeps your code flexible and clear.
  3. Delegation lets you pass tasks to other objects, which helps separate functionality and maintain cleaner, more understandable code.

Application Of Singleton and Delegated Properties

Better Engineers • 0 implied HN points • 09 Jul 22
🕹 Technology Software Development Programming Mobile Development Android
  1. Singletons help ensure that a class has only one instance, which is useful for managing shared resources like a database.
  2. Delegated properties in Kotlin allow you to reuse common behaviors like lazy loading or observing changes without repeating code.
  3. You can create custom delegates to handle unique cases, like ensuring a property can only be assigned once, adding flexibility to your code.
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Magic of Abstraction in Kotlin

Better Engineers • 0 implied HN points • 06 Jul 22
🕹 Technology Software Development Programming Languages Software Engineering System Architecture
  1. Abstraction helps hide complex code, making it easier to manage and change later. This way, users don’t need to see all the details, which simplifies their experience.
  2. Using constants instead of magic numbers improves clarity and makes future changes easier. By giving a meaningful name to a constant, we can change its value without affecting the logic in our functions.
  3. Creating interfaces allows for flexibility in our code. We can build different implementations for the same interface, making it easier to adapt the software for different platforms or needs.

Trie (Keyword Tree) Prefix Tree

Better Engineers • 0 implied HN points • 27 May 20
🕹 Technology Data Structures Algorithms Programming Software Development Information Retrieval
  1. A Trie is a special data structure that helps store and retrieve strings efficiently by organizing them based on their prefixes. This makes searching and inserting words faster.
  2. Tries are useful in many applications, like predictive text and autocomplete features, because they allow quick access to stored words and their prefixes.
  3. While Tries have advantages over hash tables, such as no key collisions, they can require more memory and may perform slower when accessing stored data on slower devices.

Deadline and Redline

Splattern • 0 implied HN points • 23 Oct 23
🕹 Technology Software Development Team Dynamics Work Culture Productivity
  1. Friendship and support can really help during stressful times. When you lose something important like your laptop, it's great to have friends who can lend a hand.
  2. Working relationships matter, and they can help boost productivity. Sometimes informal chats during meetings can lead to faster approvals and better understanding.
  3. It's okay to have tough days, but focusing on the positives can shift your mindset. Embracing nature and good company after a weary week can really uplift your spirits.

How SMS Fraud Works and How to Guard Against It

Tranquil Thoughts • 0 implied HN points • 28 Feb 23
🕹 Technology Cybersecurity Communication Fraud prevention Software Development Data Protection
  1. SMS fraud involves bad actors using special phone numbers to trick services into sending them many authentication messages, which helps them make money.
  2. To prevent SMS fraud, companies can use tactics like blocking suspicious IPs, limiting the number of SMS sent to a number, or even using alternatives like WhatsApp for communication.
  3. There’s a chance for SMS service providers like Twilio to develop tools that can quickly identify and block fraud, helping many businesses stay safe from attacks.

It's Too Late To Close Pandora's Box on AI

The Future of Life • 0 implied HN points • 09 Apr 23
🕹 Technology AI Machine Learning Data science Tech Ethics Software Development
  1. It's too late to stop the progress of AI technology. Once a breakthrough is made, it often spreads quickly and can't be controlled.
  2. Many new models are now being created that are just as good or even better than the well-known ones like ChatGPT. This means competition is driving rapid improvements.
  3. Instead of trying to pause development, we should focus on making AI safer and finding ways to align it with human values. Collaboration on safety standards is key.

Thoughts on how ChatGPT4 applies abstract concepts

The Future of Life • 0 implied HN points • 24 Mar 23
🕹 Technology AI Machine Learning Software Development Programming Automation
  1. ChatGPT can apply complex concepts like the SOLID principles in programming, which typically require extensive knowledge and experience. This shows how the model understands and utilizes abstract frameworks effectively.
  2. The model is capable of analyzing philosophical ideas, like Objectivism, and provides thoughtful explanations about them. This demonstrates its ability to engage in deep reasoning and relate concepts to real-life situations.
  3. There's curiosity about the limits of ChatGPT's reasoning abilities, especially with abstract concepts. It's suggested that there may be specific types of reasoning that only humans can easily handle.

The AI apocalypse is coming

The Future of Life • 0 implied HN points • 24 Mar 23
🕹 Technology AI risks Software Development Ethics Automation
  1. Most people worry about a dangerous AI with bad intentions, but the real risk is super-competent AI used by the wrong people. This is hard to understand because that kind of AI doesn't exist yet.
  2. In the next ten years, we might see super-competent AI that can solve many human problems. This could be a technology that helps in various fields, not just chatbots.
  3. To prevent disasters from AI, we need to acknowledge the risks, invest in safety research, and create better safety protocols. Just banning AI won't help and could make things worse.

Image Data Augmentation using Albumentations

The Beep • 0 implied HN points • 08 May 24
🕹 Technology Machine Learning Computer Vision Data science Software Development Artificial Intelligence
  1. Data augmentation helps improve deep learning models by artificially increasing the size and diversity of training data. This makes models better at understanding new, unseen data.
  2. It's especially useful when there's a limited amount of training data or the data has lots of variations. For example, if images are taken in different lighting or angles, data augmentation can help the model learn to handle those differences.
  3. Albumentations is a fast tool for applying these augmentations in image processing. It allows users to easily create different versions of images to enhance model training.

Awesome List for AutoML - Speed Up Model Development

The Beep • 0 implied HN points • 09 Apr 24
🕹 Technology Machine Learning Automation Software Development Cloud Services Data science
  1. AutoML automates tasks in the machine learning process, making it easier for people with less expertise to use. This means more folks can build models without needing to learn everything about data science.
  2. Using AutoML can save time and resources as it speeds up tasks like data preparation and model tuning. This lets data scientists focus on more complex problems instead.
  3. Though AutoML is helpful, it may reduce control over the modeling process and can introduce biases. It's important to combine AutoML with human expertise to make sure decisions are well-informed.

Top Embedding Model for VectorDB

The Beep • 0 implied HN points • 22 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Database Management
  1. VectorDB is a type of database that organizes data as vectors, making it easy to index and search different types of information like images, text, or sounds.
  2. RoBERTa is one model that can transform text into vectors, but it has a limit of 512 tokens and might shorten longer texts.
  3. When choosing an embedding model for a VectorDB project, it's important to consider the model's size and capabilities based on your needs.

Cart Recommendation with VectorDB

The Beep • 0 implied HN points • 15 Feb 24
🕹 Technology AI Data science Software Development Machine Learning Programming
  1. VectorDB helps supermarkets recommend items based on customers' previous shopping carts. It turns past transaction data into useful suggestions to increase sales.
  2. The recommendation system involves transforming shopping data into vectors and indexing them for efficient searches. This makes it quick to find similar items for recommendations.
  3. Using Python libraries like Pandas, Numpy, and Annoy, developers can create and manage the vectorized data easily. This setup allows for fast and accurate item suggestions for supermarket customers.

Best of LLM Models for your use cases

The Beep • 0 implied HN points • 01 Feb 24
🕹 Technology Artificial Intelligence Open Source Machine Learning Software Development Data science
  1. There are many open-source language models (LLMs) tailored for specific fields like healthcare, mathematics, and coding. These can perform better in their niche compared to general models.
  2. Models like Clinical Camel and Meditron are designed specifically for medical applications, using curated datasets to enhance their accuracy and performance in healthcare settings.
  3. The push for open-source LLMs promotes collaboration and innovation. By sharing models and data, communities can work together to improve technology and solve problems more effectively.

Elevate The LLM Game with Prompt Engineering

The Beep • 0 implied HN points • 25 Jan 24
🕹 Technology AI Machine Learning Software Development Data science Automation
  1. Prompt engineering helps you create better questions for AI, leading to more helpful answers. It involves trying different ways to ask until you get the response you want.
  2. There are different types of prompts, like zero-shot, one-shot, and few-shot. Each type provides different amounts of context to help the AI understand what you're asking.
  3. Using tools for prompt engineering can make the process easier and more efficient. They help in crafting prompts that get better results without needing to retrain the AI.

Beep Boop Beep Boop... Initializing 🤖

The Beep • 0 implied HN points • 16 Dec 23
🕹 Technology Artificial Intelligence Data science Software Development Cybersecurity Machine Learning
  1. The Beep is a newsletter focused on data technology and artificial intelligence. It covers a variety of topics in those fields.
  2. Readers can subscribe to keep updated on the latest trends and insights in tech and AI.
  3. The newsletter aims to make complex subjects more accessible for everyone interested in technology.

Mid-2024 Predictions Review

The AI Frontier • 0 implied HN points • 11 Jul 24
🕹 Technology AI Machine Learning Software Development Data science Regulation
  1. Commercial large language models (LLMs) like OpenAI's and Anthropic's are still leading the market. They have a big advantage that makes it hard for new competitors to catch up quickly.
  2. Open-source LLMs are improving faster than expected. Their quality is getting closer to commercial models, and they offer appealing price and performance.
  3. Regulation in the AI space is becoming more important. There's a growing need to watch how governments respond and manage AI developments moving forward.

The Tech Buffet #10: 12 Python Decorators to Take Your Code to the Next Level

The Tech Buffet • 0 implied HN points • 31 Oct 23
🕹 Technology Programming Software Development Web Development Data science
  1. Python decorators help make your code cleaner and easier to maintain. They allow you to add features to your functions without changing how they work.
  2. Using decorators can save you from writing repetitive code. They help you reuse code blocks efficiently across different functions.
  3. Getting started with decorators can be simple, like creating a logger that tracks when a function starts and finishes. Once you understand the basics, you can explore more advanced decorators.

AI Agents

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Aug 24
🕹 Technology Artificial Intelligence Software Development Machine Learning User Interfaces Automation
  1. AI Agents are programs that use large language models to work on tasks independently. They can break down complex questions and find solutions like humans do.
  2. These agents can handle tasks by analyzing user interfaces and predicting next actions by looking at icons and text. This makes them more effective in completing tasks on screens.
  3. Recent advancements have improved AI Agents' ability to understand and navigate user interfaces, allowing them to act more like real users. This helps them give better and more accurate results.

LangGraph Introduced SubGraphs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Jul 24
🕹 Technology Artificial Intelligence Software Development UI/UX Design Machine Learning Data Management
  1. LangGraph allows users to create and manage states using graphs. This helps in making complex conversation flows simpler and more organized.
  2. Sub-graphs can perform specific tasks like summarizing logs separately while still connecting back to a main graph. This lets each section work independently but share important information.
  3. LangGraph is flexible and lets users visualize and modify conversation flows easily. It works with regular Python functions, making it adaptable for various applications.

LangChain Just Launched LangGraph Cloud

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 01 Jul 24
🕹 Technology Artificial Intelligence Software Development Cloud Computing Data Management NLP
  1. LangGraph Cloud is a new service that helps users build and host their LangGraph applications easily. It's like having a managed platform to run your projects without worrying about servers.
  2. Agents are becoming more common and can handle complicated user questions automatically. They break tasks into smaller steps, making it easier to manage them.
  3. LangGraph Studio lets users visualize how data flows in their applications. This tool helps with debugging and understanding processes, even though you can't change the code directly in it.

DSPy & The Principle Of Assertions

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 May 24
🕹 Technology AI Programming NLP Machine Learning Software Development
  1. Assertions in the DSPy framework help guide language model outputs, acting like guardrails to ensure the results are reliable and accurate.
  2. There are two types of assertions: hard and soft. Hard assertions stop the process if critical rules are broken, while soft suggestions help improve outputs without stopping everything.
  3. With the ability to retry and self-refine, the DSPy framework allows language models to adapt and learn from mistakes, promoting better results over time.

Using DSPy For A RAG Implementation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 May 24
🕹 Technology AI Programming Data science Software Development Machine Learning
  1. Retrieval-augmented generation (RAG) helps language models use current knowledge to give smarter answers. This makes them more useful, but setting it up can be tricky.
  2. DSPy makes building RAG systems easier by providing a simple way to set up the necessary components. It helps streamline the process for developers.
  3. Using DSPy, you can quickly execute a RAG program to answer questions. The results are good, and the setup is straightforward, making it beginner-friendly.

Agentic RAG: Context-Augmented OpenAI Agents

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Mar 24
🕹 Technology AI Machine Learning Software Development OpenAI Automation
  1. Agentic RAG combines OpenAI's function calling with autonomous agents for better task management. This makes it easier to choose the right tools for different tasks.
  2. LlamaIndex's ContextRetrieverOpenAIAgent allows you to use multiple tools while keeping the process straightforward. It helps manage complexity by organizing various functions effectively.
  3. This new approach allows for more detailed queries and better analysis of data. It lets users run complex calculations while ensuring the results can be easily understood.

Time-Aware Adaptive RAG (TA-ARE)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 01 Mar 24
🕹 Technology AI Machine Learning Natural Language Data processing Software Development
  1. Time-Aware Adaptive RAG (TA-ARE) helps decide when it's necessary to retrieve extra information for answering questions, making the process more efficient.
  2. Adaptive retrieval is better than standard methods because it only retrieves information when needed, reducing unnecessary costs in using resources.
  3. The study suggests that understanding the timing of questions can improve how large language models respond, making them more capable without needing extra training.

LLM Drift, Prompt Drift & Cascading

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 23 Feb 24
🕹 Technology AI Machine Learning Data science Software Development Automation
  1. LLM Drift means that a language model's responses can change a lot over time. It's important to keep an eye on how these models perform since they might get worse unexpectedly.
  2. Prompt Drift occurs when the same input doesn't give the same result over time due to changes in the model or data. This can cause differences in what users expect and what they actually get.
  3. Cascading happens when one mistake in a chain of tasks leads to more problems in subsequent tasks. Once one part has an error, it can make everything else after it worse.

Leveraging LLM In-Context Learning Abilities

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Feb 24
🕹 Technology AI Machine Learning Natural Language Software Development Data science
  1. Large Language Models (LLMs) learn best when given specific context in their prompts. They use this context to generate accurate answers instead of relying solely on what they were previously trained on.
  2. Response time is very important when using LLMs, especially for conversational applications. Hosting LLMs locally can help reduce delays and save on costs.
  3. The process of breaking down complex questions into smaller ones can lead to better answers. This involves organizing thoughts and evaluating the quality of the information used to answer the questions.

Seven RAG Engineering Failure Points

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Data processing Software Development
  1. Retrieval-Augmented Generation (RAG) reduces errors in information by combining data retrieval with language models. This helps produce more accurate and relevant responses.
  2. RAG allows for better organization of data, making it easy to include specific industry-related information. This is important for tailoring responses to user needs.
  3. There are several potential failure points in RAG, such as missing context or providing incomplete answers. It's crucial to design systems that can handle these issues effectively.

Prompt-RAG: Vector Embedding Free Retrieval-Augmented Generation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 26 Jan 24
🕹 Technology AI Language Models Data Management Machine Learning Software Development
  1. Prompt-RAG is a simpler way to use language models without needing complex data setups like vector embeddings. This makes it easier to apply for specific tasks.
  2. It uses a Table of Contents to find the right information quickly, which helps generate more accurate responses to user questions.
  3. While it's great for small projects, it may face challenges with larger data or technical scaling as needs grow.

Chain Of Natural Language Inference (CoNLI)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Jan 24
🕹 Technology Artificial Intelligence Natural Language Data Analysis Machine Learning Software Development
  1. There are three types of hallucinations in AI-generated text: context-free, ungrounded, and self-conflicting. Each type means there's a different way the text can be misleading.
  2. The CoNLI framework helps detect and reduce hallucinations in text responses. It can rewrite responses to improve their accuracy without needing special tuning.
  3. CoNLI works even when the user has limited control over the AI model, making it easier to ensure that the generated output aligns with correct information.

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.

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.