The hottest Applications Substack posts right now

And their main takeaways
Category
Top Technology Topics

AI #148: Christmas Break

Don't Worry About the Vase • 2553 implied HN points • 25 Dec 25
🕹 Technology Applications
  1. AI capabilities are accelerating fast — models like Claude Opus 4.5 and GPT‑5.2‑Codex are getting much better at long‑horizon, agentic coding and benchmarked tasks.
  2. Policy and public opinion are catching up: states are passing laws like New York’s RAISE Act and voters broadly favor federal AI regulation, even as industry and politics push back.
  3. The social and safety picture is messy — AI is disrupting jobs and media (deepfakes and a lot of low‑quality 'slop'), and aligning and reliably monitoring smarter systems remains hard despite improving interpretability tools.

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

Big Technology • 25395 implied HN points • 27 Jan 25
🕹 Technology Applications
  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.

RL and The Era of Experience

Tanay’s Newsletter • 138 implied HN points • 10 Feb 26
🕹 Technology Applications
  1. AI is shifting from learning from static human data to learning from experience, with models improving by taking actions in environments, receiving feedback, and scaling reinforcement learning.
  2. A new RL ecosystem is emerging with companies that build environments, provide RL infrastructure, and offer RL-as-a-service, enabling labs and apps (like coding tools) to train and improve agents.
  3. Important open questions remain about how well RL-trained models generalize, whether RL scaling alone is enough, and the need for continual learning plus many more realistic evaluations and environments.

Street Smart

polymathematics • 159 implied HN points • 30 Aug 24
🕹 Technology 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.

Giving your AI a Job Interview

One Useful Thing • 1028 implied HN points • 12 Nov 25
🕹 Technology Applications
  1. Measuring AI performance is tricky because common tests can be flawed and sometimes don't really show how smart the AI is. We're often left uncertain about what these benchmarks actually mean.
  2. Using a more personal approach, like creating fun and unique tests, can help people understand how different AI models work. This way, you get a feel for the AI's strengths and weaknesses in a more relatable way.
  3. When companies choose AI tools, it's important to do thorough testing based on real tasks instead of just relying on average performance scores. Understanding specifically how well an AI can perform your unique tasks is key.
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March Newsletter

RSS DS+AI Section • 11 implied HN points • 01 Mar 26
🕹 Technology Applications
  1. AI is spreading into many areas, but bias, safety and governance are still unresolved, so people are calling for stronger auditing and regulation.
  2. Research is moving fast — scaling laws, reasoning models, agentic systems and shifting LLM representations are driving progress, yet we still don’t fully understand model behavior or failure modes.
  3. Practitioners are focused on real-world use: there’s lots of practical guidance, on-device and open-source work, and community events and job opportunities to help teams deploy AI effectively.

Layer 3 Deep Dive

DeFi Education • 399 implied HN points • 12 Jun 24
🔮 Crypto Applications
  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.

($) The AI Competition as a Football Game

Interconnected • 92 implied HN points • 06 Jan 26
🕹 Technology Applications
  1. Right now the US is judged to be slightly ahead of China in the AI competition, scored like a halftime football game (USA 29, China 25).
  2. The analysis breaks the competition into five stacked layers — energy, infrastructure capacity, chips/compute, foundational models, and applications — and scores each layer separately.
  3. Those layer-by-layer scores reveal trade-offs (for example, China scores higher on energy while the US leads on other layers), so who wins depends on which parts of the stack matter most.

Which AI to Use Now: An Updated Opinionated Guide

One Useful Thing • 2229 implied HN points • 26 Jan 25
🕹 Technology 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.

February Newsletter

RSS DS+AI Section • 29 implied HN points • 01 Feb 26
🕹 Technology Applications
  1. AI misuse and ethical risks are increasing — deepfakes, automated exploit generation, bias, and job impacts mean security, fairness, and regulation need urgent attention.
  2. Research is advancing rapidly across many fronts, including model consistency, memory/lookup mechanisms, test-time training, decentralized and open-source models, and early work on AI systems that can improve themselves.
  3. Practical resources and community activity are abundant, with tutorials, benchmarks, tools, academic outlets, and job opportunities helping practitioners deploy AI responsibly and learn new skills.

The business of AI in 2026

Generating Conversation • 70 implied HN points • 08 Jan 26
💼 Business Applications
  1. Big investments in data centers and GPUs are likely to pay off as inference gets cheaper and more AI applications become economical, so infrastructure buildout is a bullish trend.
  2. Large companies will keep acquiring startups and doing acqui‑hires, and those acqui‑hires can harm the startup ecosystem and spook talent unless policy or enforcement changes.
  3. Frontier labs will move up into higher‑margin applications, so startups must differentiate on orchestration, workflows, and solving harder domains like healthcare, security, and SRE where adoption is slower but more defensible.

Single-Use Disposable Applications

General Robots • 627 implied HN points • 09 Jul 25
🕹 Technology Applications
  1. Creating apps is getting easier and faster, meaning you can make exactly what you need without searching for it. It's now quicker to build a tool than to look for one that might work.
  2. Software apps are becoming single-use tools tailored to specific tasks. Instead of complex applications, people will create simple, disposable apps for immediate needs.
  3. In this new tech environment, anyone can build these tools, not just developers. This shift changes how software will be designed and used in the future.

OpenAI Acquired Rockset

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 31 Jul 24
🕹 Technology 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.

Artificial Intelligence: The Rise of AI in Earnings Call Summarization

The Fintech Blueprint • 334 implied HN points • 30 Jan 24
🕹 Technology 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 Applications
  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 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.

GenAI and LLMs: Insights from TikTok and KPMG

Gradient Flow • 119 implied HN points • 18 Apr 24
🕹 Technology 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 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.

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 Applications
  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.

Happy 1st Birthday, GPT-4

Marcus on AI • 1383 implied HN points • 16 Mar 24
🕹 Technology Applications
  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.

Building LLM-powered Apps: What You Need to Know

Gradient Flow • 519 implied HN points • 06 Apr 23
🕹 Technology Applications
  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 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.

Strategies for an Accelerating Future

One Useful Thing • 1033 implied HN points • 20 Feb 24
🕹 Technology Applications
  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.

AI #50: The Most Dangerous Thing

Don't Worry About the Vase • 940 implied HN points • 08 Feb 24
🕹 Technology Applications
  1. Gemini Ultra is Google's latest AI model, described better than GPT-4 but conservative in responses.
  2. AI language models like ChatGPT and Google are widely used and offer mundane utility, despite some limitations.
  3. AI advancements raise concerns about deepfakes, fake IDs, and a need for regulations to address security risks.

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 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.

An AI Haunted World

One Useful Thing • 972 implied HN points • 19 Dec 23
🕹 Technology Applications
  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.

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

Generating Conversation • 256 implied HN points • 20 Feb 25
🕹 Technology Applications
  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.

AI is Eating the (Research) World

Bojan’s Newsletter • 216 implied HN points • 03 Oct 23
🕹 Technology Applications
  1. AI is revolutionizing research fields like computer science, starting in 2013.
  2. AI is a versatile tech applicable in diverse fields yet still underutilized in non-CS disciplines.
  3. Scarcity of good datasets limits AI's wider adoption in research, but foundational models could change that.

January Newsletter

RSS DS+AI Section • 11 implied HN points • 01 Jan 26
🕹 Technology Applications
  1. AI and large language models are advancing rapidly, with major companies and open-source projects pushing innovations in long-context reasoning, memory, and generative capabilities. Competition is driving frequent releases and new research on foundation models and video/world-models.
  2. Ethics, bias, interpretability, and regulation remain central concerns as real-world uses expand, prompting debates, lawsuits, and calls for better safety research. Work on interpretability is seen as especially important for progressing AI more safely.
  3. The community is focusing on practical adoption and professionalisation through tutorials, production tips, projects, workshops, a new journal, and competency frameworks. There are also learning opportunities, internships, and calls for volunteers to help shape best practices and careers.

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

One Useful Thing • 887 implied HN points • 05 Sep 23
🕹 Technology Applications
  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.

The new anachronism

Sunday Letters • 39 implied HN points • 14 Apr 24
🕹 Technology Applications
  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.

DeepSeek, o3, and AI applications

Generating Conversation • 116 implied HN points • 06 Feb 25
🕹 Technology Applications
  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.

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 Applications
  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.