The hottest Applications Substack posts right now

And their main takeaways
Category
Top Technology Topics

You can’t build a moat with AI (redux)

Generating Conversation • 256 implied HN points • 20 Feb 25
🕹 Technology AI Software Applications Data User Experience
  1. Using AI like LLMs isn't unique anymore. Just having AI in your product doesn't really set it apart from competitors.
  2. To really stand out, focus on making a great user experience and integrating your product into how users already work. This makes your tool more valuable and hard to replace.
  3. Data is crucial for AI. It's not just about having lots of data; it's about using it smartly over time to improve your product and understand your users better.

Which AI to Use Now: An Updated Opinionated Guide

One Useful Thing • 2229 implied HN points • 26 Jan 25
🕹 Technology AI Software Data Models Applications
  1. When choosing an AI, consider using a paid version for better features. Claude, Gemini, and ChatGPT are the top choices right now.
  2. New AI advances include live interaction and reasoning capabilities. This helps AIs understand and respond more naturally, making them feel more human.
  3. Privacy is now better handled by major AI models, and you can customize them for your specific needs. Explore different AIs to find one that fits your style.

Notes on DeepSeek: Generative AI is All About the Applications Now

Big Technology • 25395 implied HN points • 27 Jan 25
🕹 Technology AI Applications Development Infrastructure Investment
  1. Generative AI is now cheaper to build, making it easier for developers to create new applications. This means we might start seeing more innovative uses of AI technology.
  2. The focus is shifting from how much money is spent on infrastructure to what practical applications can be built with AI. This could change the way companies approach AI development.
  3. While there is potential for exciting products, there is still uncertainty about how to effectively use generative AI. Not all that has been built so far has met high expectations.

DeepSeek, o3, and AI applications

Generating Conversation • 116 implied HN points • 06 Feb 25
🕹 Technology AI Applications Models Innovation Competition
  1. DeepSeek R1 is a strong AI model that has impressed the industry, but life goes on, and the world hasn't changed drastically because of it. More good models out there mean better choices for those building AI applications.
  2. Competition is heating up in the AI space. Other companies, like OpenAI, are responding by releasing new models quickly to keep up with emerging players like DeepSeek.
  3. The trend of making AI models more affordable is continuing. This can help more people and businesses use AI, solving new problems that weren’t possible before.

Street Smart

polymathematics • 159 implied HN points • 30 Aug 24
🕹 Technology AI Computing Innovation Community Applications
  1. Communal computing can connect people in a neighborhood by using technology in shared spaces. Imagine an app that helps you explore local history or find nearby restaurants right from your phone.
  2. AI could work for more than just individuals; it can help whole communities. For example, schools could have their own AI tutors to assist students together.
  3. There are cool projects like interactive tiles in neighborhoods that let people share information and connect with each other in real life, making technology feel more personal and community-focused.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Navigating the AI Jungle - Chat Bots

Erik Explores • 61 implied HN points • 02 Feb 25
🕹 Technology AI Tools User Experience Applications Machine Learning
  1. There are many AI tools available, and it can be confusing to choose the right one. It's helpful to rely on personal experiences to see which tools work well.
  2. OpenAI's ChatGPT is popular for its good interface and features, like voice chat, which makes learning interactive and fun.
  3. DeepSeek allows for using AI models directly on your computer, giving flexibility, but it's important to choose the right model for your specific task.

Layer 3 Deep Dive

DeFi Education • 399 implied HN points • 12 Jun 24
🔮 Crypto Blockchain DeFi Web3 Applications Infrastructure
  1. Layer 3 is the application layer that helps make blockchain technology user-friendly. It aims to simplify how people interact with decentralized finance (DeFi) and other crypto apps.
  2. Layers 1 and 2 are the foundational blockchains, but most users won't need to understand them. The goal is to focus on user experience rather than the underlying complexity.
  3. To bring crypto applications to a wider audience, it’s important to extend and enhance existing technologies, making them more accessible to everyone.

Predictions for AI in 2025

Generating Conversation • 46 implied HN points • 09 Jan 25
🕹 Technology Artificial Intelligence Predictions Applications Model development Investment Trends
  1. AI applications will become essential for businesses. Companies that don't adopt AI might struggle to keep up with competition.
  2. Investments in AI are expected to stay steady or increase. This means more money will flow into AI startups and technologies in the coming year.
  3. Foundation models will improve, but there may be fewer new releases. Companies will focus on enhancing existing models rather than just creating new ones.

OpenAI Acquired Rockset

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 31 Jul 24
🕹 Technology AI Data Analytics Infrastructure Applications
  1. OpenAI bought Rockset to make their data retrieval system better, which helps in using AI more effectively.
  2. The acquisition shows that LLMs are being seen more like a tool, and the focus is shifting to building useful applications using these technologies.
  3. Rockset's technology will help OpenAI work better with developers and make it easier to access and use real-time data for AI products.

Strategies for an Accelerating Future

One Useful Thing • 1033 implied HN points • 20 Feb 24
🕹 Technology AI Machine Learning Innovation Applications Future
  1. Advancements in AI, such as larger memory capacity in models like Gemini, are enhancing AI's ability for superhuman recall and performance.
  2. Improvements in speed, like Groq's hardware for quick responses from AI models, are making AI more practical and efficient for various tasks.
  3. Leaders should consider utilizing AI in their organizations by assessing what tasks can be automated, exploring new possibilities made possible by AI, democratizing services, and personalizing offerings for customers.

Happy 1st Birthday, GPT-4

Marcus on AI • 1383 implied HN points • 16 Mar 24
🕹 Technology AI Ethics Applications Progress Competition
  1. There seems to be a possible plateau in GPT-4's capability, with no one decisively beating it yet.
  2. Despite challenges, there has been progress in discovering applications and putting GPT-4 type models into practice.
  3. Companies are finding putting Large Language Models into real-world use challenging, with many initial expectations proving unrealistic.

Claude, the Stars, and You

Default Wisdom • 66 implied HN points • 21 Nov 24
🕹 Technology AI Innovation Applications Trends Analysis
  1. The topic discusses a case study related to AI and its implications. AI is becoming a significant part of various fields, including writing and creative industries.
  2. The author encourages feedback and interaction from readers to improve future case studies. Engaging with the audience can lead to better content and insights.
  3. There is a focus on the role of bloggers and content creators in utilizing AI tools. These technologies can enhance creativity and efficiency in producing content.

An AI Haunted World

One Useful Thing • 972 implied HN points • 19 Dec 23
🕹 Technology AI Models Applications Development Autonomy
  1. The development of open source AI models is democratizing AI usage and allowing for easier modification and widespread deployment.
  2. The efficiency and affordability of LLMs will lead to AI being incorporated into various products for troubleshooting, monitoring, and interaction, potentially creating an 'AI haunted world'.
  3. Future AI integration may involve hierarchies of various AI models working together, with smart generalist AIs delegating tasks to cheaper, specialized AIs.

Artificial Intelligence: The Rise of AI in Earnings Call Summarization

The Fintech Blueprint • 334 implied HN points • 30 Jan 24
🕹 Technology AI Finance Analysis Tools Applications
  1. AI is revolutionizing financial analysis through earnings call summarizations by tools like Bloomberg, AlphaSense, TiredBanker, and Aviso.
  2. AI helps in quickly isolating key points from earnings calls and deriving insights that improve financial decision-making.
  3. AI-driven tools have the potential to mitigate human error in analyzing financial data and are expected to see universal adoption in the financial services sector.

Generative AI: What Happened, Where It's Going 2023-2024

Rod’s Blog • 416 implied HN points • 19 Dec 23
🕹 Technology AI Predictions Applications Ethics Regulation
  1. Generative AI is rapidly advancing and has a wide range of applications from enhancing creativity to solving real-world problems.
  2. In 2023, Generative AI saw explosive growth, with a significant number of organizations implementing it in various business functions.
  3. Expected trends in 2024 for Generative AI include more advanced language models, more creative applications, and increased focus on ethical and responsible considerations.

Unveiling the Revolutionary Architecture behind LLMs - "Attention is all you need"

Mindful Matrix • 219 implied HN points • 17 Mar 24
🕹 Technology AI NLP Architecture Training Applications
  1. The Transformer model, introduced in the groundbreaking paper 'Attention Is All You Need,' has revolutionized the world of language AI by enabling Large Language Models (LLMs) and facilitating advanced Natural Language Processing (NLP) tasks.
  2. Before the Transformer model, recurrent neural networks (RNNs) were commonly used for language models, but they struggled with modeling relationships between distant words due to their sequential processing nature and short-term memory limitations.
  3. The Transformer architecture leverages self-attention to analyze word relationships in a sentence simultaneously, allowing it to capture semantic, grammatical, and contextual connections effectively. Multi-headed attention and scaled dot product mechanisms enable the Transformer to learn complex relationships, making it well-suited for tasks like text summarization.

The Sequence Engineering #483: Block's goose is a Brand New Framework for Building Agentic Applications

TheSequence • 91 implied HN points • 05 Feb 25
🕹 Technology AI Software Frameworks Applications Development
  1. Block has introduced a new framework called goose, which helps connect large language models to actions. This means it can make LLMs do things more effectively.
  2. The release of goose shows that big companies are really getting into building applications that can act on their own. It's changing how we look at AI and its capabilities.
  3. The ongoing development of agentic workflows is significant, and it hints that AI will continue to grow and improve in how it helps us solve problems.

GenAI and LLMs: Insights from TikTok and KPMG

Gradient Flow • 119 implied HN points • 18 Apr 24
🕹 Technology AI Data Management Legal Framework Applications
  1. Large enterprises are shifting towards in-house AI application development using foundation models, impacting the industry by enabling cost savings and customization.
  2. AI adoption rates among U.S. businesses are rapidly growing, expected to almost double by Fall 2024, with a focus on technology and development applications.
  3. Companies like TikTok and KPMG are adopting GenAI in different ways – TikTok invests heavily in content creation, while KPMG focuses on integrating AI into audit and advisory services, showcasing diverse applications of GenAI.

Enterprise Generative AI Unfolded

Gradient Flow • 439 implied HN points • 27 Jul 23
🕹 Technology AI Development Tools Enterprise Applications
  1. Mastering Model Development & Optimization is crucial for building efficient and powerful Generative AI and Large Language Models. Scaling to large datasets, applying model compression strategies, and efficient model training are key aspects.
  2. Customizability & Fine-tuning are essential to adapt pre-existing LLMs to specific business needs. Techniques like fine-tuning and in-context learning help tailor LLMs for unique use cases, such as adjusting speech synthesis models for customized experiences.
  3. Investing in Operational Tooling & Infrastructure, including robust model hosting, orchestration, and maintenance tools, is vital for efficient and real-time deployment of AI systems in enterprises. Tools for logging, tracking, and enhancing LLM outputs ensure quality control and ongoing improvements.

Embracing weirdness: What it means to use AI as a (writing) tool

One Useful Thing • 887 implied HN points • 05 Sep 23
🕹 Technology AI Writing Applications Innovation
  1. AI is weird and different from traditional software, so we need to embrace its uniqueness to fully understand its capabilities.
  2. AI can do much more than just act as a thesaurus or grammar checker; it has the potential to help in creative idea generation and simulate individual readers for market feedback.
  3. To unlock the true value of AI, we should experiment with unconventional uses of AI tools while being mindful of ethical concerns and technical limitations.

Agent AI: Agentic Applications Are Software Systems With A Foundation Model AI Backbone & Defined Autonomy via Tools

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 05 Aug 24
🕹 Technology AI Software Models Applications Data
  1. Agentic Applications are advanced software systems that use AI models to operate more independently. They can navigate and process information effectively using tools.
  2. The MindSearch framework helps break down complex questions into simpler parts, making it easier to find answers online. It simulates how humans think and search for information.
  3. There are special agents in this system, like WebPlanner and WebSearcher, that work together to gather and organize information from the web, enhancing the problem-solving process.

Building LLM-powered Apps: What You Need to Know

Gradient Flow • 519 implied HN points • 06 Apr 23
🕹 Technology AI Machine Learning Data science Applications Models
  1. Developers can now create AI-powered applications without deep machine learning knowledge, opening up opportunities for rapid experimentation and innovation.
  2. Building custom large language models (LLMs) is becoming more accessible through startups offering resources for model fine-tuning or training from scratch.
  3. Integration of custom LLMs with third-party services, utilizing knowledge bases, and serving models efficiently are key areas of focus for developers in the AI application space.

Sora: The Explorer

Cybernetic Forests • 139 implied HN points • 18 Feb 24
🕹 Technology AI Video Generation Applications
  1. New text-to-video models like Sora by OpenAI are pushing boundaries in video generation, offering longer and more diverse outputs compared to previous models.
  2. Sora's method involves training on a variety of video formats like widescreen, vertical, and square, leading to more efficiency and comprehensive use of video data for generation.
  3. One challenging aspect of Sora is its ability to create multiple synthetic scenarios that all lead to the same outcome, posing risks of misinformation and manipulation in media content.

A few themes for 2025

Tanay’s Newsletter • 63 implied HN points • 08 Jan 25
🕹 Technology AI Applications Innovation Market Trends Product Development
  1. AI is getting better at solving problems during its reasoning process. This means we might see smarter AI that can think through complex issues and improve its answers.
  2. Multimodal AI, which handles different types of data like text, images, and videos, is on the rise. In 2025, we can expect more creative and useful applications that actually change how we work.
  3. AI agents, or smart systems that can work independently, are likely to become more common. This year, they might really start acting like human coworkers, helping businesses run more smoothly.

How to train ChatGPT on your own text (train a text AI to generate content about your docs, book, website, etc)

Mythical AI • 235 implied HN points • 19 Feb 23
🕹 Technology AI Chatbots Models Training Applications
  1. Large language models like ChatGPT can summarize articles, write stories, and engage in conversations.
  2. To train ChatGPT on your own text, you can use methods like giving the AI data in the prompt, fine-tuning a GPT3 model, using a paid service, or using an embedding database.
  3. Interesting use cases for training GPT3 on your own data include personalized email generators, chatting in the style of famous authors, creating blog posts, chatting with an author or book, and customer service applications.

Edge 438: Meet DataGemma: Google DeepMind's Effort to Ground LLMs in Factual Knowledge

TheSequence • 112 implied HN points • 10 Oct 24
🕹 Technology AI Data Research Models Applications
  1. DataGemma is a new model developed by Google DeepMind that helps large language models (LLMs) use factual information.
  2. It aims to reduce errors, known as hallucinations, and make LLMs more reliable for important tasks.
  3. The model uses a large data source called DataCommons to verify the information it provides.

Synthetic data: Anthropic’s CAI, scaling, OpenAI’s Superalignment, tips, and open-source examples

Democratizing Automation • 332 implied HN points • 29 Nov 23
🕹 Technology AI Data Models Methods Applications
  1. Synthetic data is becoming more important in AI, with a focus on removing human involvement.
  2. Proponents believe that using vast amounts of synthetic data can lead to breakthroughs in AI models.
  3. Open and closed communities are both utilizing synthetic data for different end goals.

The new anachronism

Sunday Letters • 39 implied HN points • 14 Apr 24
🕹 Technology AI User Experience Applications Digital Media Innovation Future Trends
  1. Technology changes fast, and things we think are normal now might seem really strange to future generations. For example, the idea of using rotary phones or only having a few TV channels is hard for young people to imagine.
  2. Apps and documents may seem outdated soon. In the future, instead of using fixed apps or linear documents, we might have AI that creates personalized experiences and lets us interact in more flexible ways, like having conversations.
  3. As technology evolves, we will have more control over our digital experiences. Just like how TV shifted from networks to streaming, the way we create and share digital content will also change, making it easier and more accessible for everyone.

What is a data product?

davidj.substack • 71 implied HN points • 15 Mar 24
🕹 Technology Data Products Interfaces Applications
  1. A data product can take various forms and be consumed in different ways, always requiring an interface for consumption.
  2. From raw data like CSV files to refined database tables, streams, JSON files, and ORM abstracted layers, all can be considered data products.
  3. BI tools, AI automation, and semantic layers play crucial roles in creating consumable data products for various industries, making data more refined and accessible.

Week #4: Overview Of Conformal Predictors

Mindful Modeler • 139 implied HN points • 10 Jan 23
🕹 Technology Machine Learning Data science Research Methods Applications
  1. Conformal prediction is a versatile approach applicable to various machine learning tasks beyond just regression and classification.
  2. When learning about a new conformal prediction method, it's important to consider the machine learning task, non-conformity score used, and how the method deviates from the standard recipe.
  3. Staying up to date with new research in conformal prediction can be facilitated by resources like the 'Awesome Conformal Prediction' repository and following experts in the field on platforms like Twitter.